Release v1.1.1 of the Ento-Linguistics research project.
Corpus: 7,609 PubMed abstracts (full search surface drained), 7,073 PMC open-access full texts, 2,460 BHL historical documents (1850–1970), 61 arXiv preprints, 536/536 OpenAlex citation enrichments. The manuscript is fully token-driven: all corpus statistics are injected at PDF build time from pipeline artifacts (strict mode; no hard-coded values).
v1.1.1 adds: pruned and parallelized language-analysis stages (the statistics/full-text stages drop from hours to ~25 min), corpus-fingerprint freshness guards for both statistical artifacts, a dry-run-only manuscript variable validator, and regenerated figures, statistics, and PDF.
", "authors": "Daniel Ari Friedman, Tucker Cahill Chambers", "abstract": "Scientific language does not merely describe biological phenomena; it actively constitutes the generative models through which researchers parse complex systems. This paper makes three core contributions to understanding—and correcting—the epistemic consequences of this constitutive role. First, we introduce a six-domain Ento-Linguistic framework that decomposes the terminological landscape of insect research into analytically tractable themes, isolating domains where anthropomorphic language most severely distorts causal modeling. Second, we develop an open-source computational pipeline that integrates automated term extraction, co-occurrence network construction, and information-theoretic ambiguity scoring with principles from Active Inference and Complex Systems Theory. Third, we propose and validate four evidence-based meta-standards—Clarity, Appropriateness, Consistency, and Evolvability (CACE)—as a formalized protocol for lexical engineering. Analysis of a corpus encompassing 369 entomological publications (48787 tokens; 7105 unique token types; Type–Token Ratio 0.1456) extracts 888 candidate terms (with 261 assigned to specific semantic domains across 6 conceptual clusters linked by 9 weighted relationships). The resulting terminology networks display strong modularity alongside systematic cross-domain bridging—most prominently in the Power and Labor domain, where 43 bridging terms generate extensive semantic bleed-over into adjacent domains. Terms such as “queen” (241 occurrences), “worker” (269), and “caste” (121) implicitly impose hierarchical control topologies onto biological structures that are fundamentally stigmergic and decentralized. Across all 261 domain-assigned terms, 16.9% exhibit context-dependent semantic drift, demonstrating how conceptual constructs like “individuality” span multiple biological scales and consequently blur the formal systemic boundaries (Markov Blankets) required for mathematically rigorous modeling. The accompanying fully reproducible computational pipeline provides the quantitative analytical tools necessary for a more self-aware and epistemically rigorous scientific practice. All code and data are available at https://github.com/docxology/ento_linguistics.", "keywords": [], "doi": "10.5281/zenodo.19574117", "github_release_url": "https://github.com/docxology/ento_linguistics/releases/tag/v1.1.1" }, "2026_ReproducibleResearch": { "year": "2026", "topic": "ReproducibleResearch", "name": "A template/ approach to Reproducible Generative Research: Architecture and Ergonomics from Configuration through Publication", "description": "Infrastructure-as-code research lifecycle: Two-Layer Architecture, eight-stage build pipeline, Zero-Mock testing, and Documentation Duality (README + AGENTS + SKILL).", "authors": "Daniel Ari Friedman", "abstract": "The reproducibility crisis in computational research is fundamentally structural: research artifacts are scattered across disconnected tools. template/ applies Infrastructure as Code to the research lifecycle, making the manuscript, test suite, and provenance chain version-controlled, deterministically buildable, and independently verifiable.", "tags": [ "reproducible-research", "infrastructure-as-code", "build-pipeline", "open-science", "model-context-protocol" ], "keywords": [ "reproducible research", "infrastructure as code", "build pipeline", "open science", "Model Context Protocol" ] }, "2026_ActInfMetaAnalysis": { "year": "2026", "topic": "ActInfMetaAnalysis", "name": "A Living Meta-Analysis Architecture for Active Inference: Assertion Extraction, Nanopublications, and Hypothesis Scoring", "description": "Computational living meta-analysis of the Active Inference and Free Energy Principle literature: multi-source retrieval, nanopublication extraction, and hypothesis scoring architecture.", "authors": "Daniel Ari Friedman, Joel Dietz", "abstract": "No prior automated system tracks hypothesis-level evidence across the full Active Inference and Free Energy Principle (FEP) literature. Manual synthesis cannot keep pace with a field that has grown at a compound annual rate of 20.36% across 2005–2026, and the FEP’s theoretical generality has invited falsifiability critiques that only hypothesis-specific evidence profiling can address. Building on pioneering systematic manual annotation paired with ontology-based anal- ysis at the scale of hundreds of papers, we present a computational meta-analysis framework that automates and scales this approach. The pipeline retrieves literature from arXiv, Semantic Scholar, and OpenAlex, deduplicating 𝑁 = 819 papers via a canonical identifier hierarchy (DOI > arXiv ID > Semantic Scholar ID > OpenAlex ID). It classifies papers into a three-tier taxonomy spanning eight categories: A (Core Theory), B (Tools & Translation), and C (Application Domains). An LLM-powered extraction system then evaluates each abstract against eight core hypotheses, producing structured nanopublications—each encoding directionality, a confidence score, and natural-language reasoning—that populate an RDF-compatible knowledge graph scored by a citation-weighted evidence function. All extracted assertions are automatically generated and have not been manually validated; hypothesis scores should be considered preliminary. The resulting evidence landscape reveals a field where application domains (Domain C, 64.0%) collectively dominate the corpus, with tools development (Domain B, 20.8%)—including pymdp, RxInfer.jl, and interpretable alternatives such as Free Energy Projective Simulation—and core theory (Domain A, 15.2%) rounding out the taxonomy. Non-negative matrix factorization identifies 5 latent topics that cross-cut the keyword taxonomy, and citation network analysis exposes a sparse yet structured graph (2,176 intra-corpus edges out of 29,323 total outgoing references—only 7.4% reference resolution, reflecting the corpus’s specialised scope rather than the underlying citation density of any single paper) anchored by pronounced hub papers. Hypothesis scores cluster into three tiers: a broad consensus tier (score > 0.83) covering five hypotheses—H7 Morphogenesis, H2 AIF Optimality, H4 Predictive Coding, H6 Clinical Utility, and H5 Scalability; a near-consensus boundary (H8 Language AIF, score ≈+0.83); a moderate debate tier (H3 Markov Blanket Realism, ≈+0.78); and a diffuse tier (H1 FEP Universality, ≈+0.48) where a large neutral plurality reflects the principle’s broad invocation without explicit empirical test—though absolute score magnitudes are inflated by publication bias and linguistic asymmetry in academic writing, making relative rankings and temporal trajectories more reliable than point estimates. By demonstrating that automated LLM-driven assertion extraction—operating without human-validated ground truth—can generate scalable, queryable representations of scientific evidence, this work provides a reusable architecture for living literature reviews—continuously updated knowledge graphs that track hypothesis-level consensus across rapidly evolving fields. All code, results, and methods to reproduce this manuscript are open source at https://github.com/ActiveInferenceInstitute/act_inf_metaanalysis/ .", "tags": [ "active-inference", "meta-analysis", "nanopublications", "assertion-extraction", "citation-weighted-scoring", "literature-review", "free-energy-principle", "computational-bibliography", "open-science" ], "keywords": [ "Active Inference", "meta-analysis", "nanopublications", "assertion extraction", "citation-weighted scoring", "literature review architecture", "Free Energy Principle", "computational bibliography" ] }, "2026_FocusedAttentionMeditation": { "year": "2026", "topic": "FocusedAttentionMeditation", "name": "Dynamic Attentional Agents in Focused Attention Meditation: Hierarchical Computational Modeling of Expert-Novice Differences", "description": "Three-level hierarchical Active Inference framework for focused attention meditation: thoughtseed agents (Markov blankets) couple to DMN/VAN/DAN/FPN; simulations reproduce 49% lower free energy and DMN suppression in expert meditators.", "authors": "Prakash Chandra Kavi, Daniel Ari Friedman, Gustavo Patow", "abstract": "We develop a three-level hierarchical framework to model the attentional dynamics of focused attention (FA) meditation, laying a foundation for advanced active inference (AIF) implementations. Grounded in the Free Energy Principle and Neuronal Packet Hypothesis, we conceptualize meditation as a predictive processing system where “thoughtseeds”—transient, agent-like entities forming Markov blankets—minimize variational free energy via bidirectional coupling with attentional networks (DMN, VAN, DAN, FPN). Simulations across biologically plausible parameters reproduce expert–novice differences: 49% lower free energy during breath focus, suppressed DMN activity (0.18 vs. 0.31), and faster distraction recovery.", "tags": [ "active-inference", "focused-attention-meditation", "thoughtseeds", "free-energy-principle", "hierarchical-modeling", "precision-weighting", "contemplative-neuroscience", "expert-novice", "predictive-processing", "computational-psychiatry" ], "keywords": [ "Active Inference", "focused attention meditation", "thoughtseeds", "Free Energy Principle", "hierarchical modeling", "expert-novice differences", "DMN", "precision weighting", "Neuronal Packet Hypothesis", "contemplative neuroscience" ] }, "2026_CognitiveCaseDiagrams": { "year": "2026", "topic": "CognitiveCaseDiagrams", "name": "Cognitive Diagrams: Reviewing Categorical Accounts of Linguistic Case", "description": "Linguistic case offers a setting in which to examine how diagrams connect relational structure, compositional syntax, and uncertainty. This article reviews categorical approaches and supplies an executable collection of deliberately small examples. The implementation includes case-role graphs, pregroup derivations, tensor contractions, synthetic similarity matrices, Bayesian filtering, quantile utilities, and positive-operator-valued measurements. These components share notation, but their mathematical relationships require separate assumptions; a common diagrammatic vocabulary does not establish their equivalence.\n\nThe principal contribution is an inspectable comparison of these constructions and their limits. We distinguish generated derivations from illustrative drawings, finite score distributions from Bellman return distributions, presentation statistics from topos invariants, and model-unit prediction errors from physiological measurements. The accompanying suite collects 1883 tests across 94 files; that is a collection count, and a separate source-bound quality receipt records 1883 passing tests with 92.13% line-and-branch coverage (Appendix C). The figure pipeline produces 33 figures from source. All numerical illustrations use synthetic inputs; no corpus study, participant experiment, EEG fit, quantum-hardware run, or deployed-agent security evaluation is reported. Topos-theoretic transfer, cognitive advantages of case diagrams, and case-based authorization remain research proposals with explicit validation requirements. This is working revision 2.7.0; its local validation does not constitute publication or independent mathematical certification.", "authors": "Daniel Ari Friedman", "abstract": "Linguistic case offers a setting in which to examine how diagrams connect relational structure, compositional syntax, and uncertainty. This article reviews categorical approaches and supplies an executable collection of deliberately small examples. The implementation includes case-role graphs, pregroup derivations, tensor contractions, synthetic similarity matrices, Bayesian filtering, quantile utilities, and positive-operator-valued measurements. These components share notation, but their mathematical relationships require separate assumptions; a common diagrammatic vocabulary does not establish their equivalence.\n\nThe principal contribution is an inspectable comparison of these constructions and their limits. We distinguish generated derivations from illustrative drawings, finite score distributions from Bellman return distributions, presentation statistics from topos invariants, and model-unit prediction errors from physiological measurements. The accompanying suite collects 1883 tests across 94 files; that is a collection count, and a separate source-bound quality receipt records 1883 passing tests with 92.13% line-and-branch coverage (Appendix C). The figure pipeline produces 33 figures from source. All numerical illustrations use synthetic inputs; no corpus study, participant experiment, EEG fit, quantum-hardware run, or deployed-agent security evaluation is reported. Topos-theoretic transfer, cognitive advantages of case diagrams, and case-based authorization remain research proposals with explicit validation requirements. This is working revision 2.7.0; its local validation does not constitute publication or independent mathematical certification.", "keywords": [], "doi": "10.5281/zenodo.19695259", "github_release_url": "https://github.com/docxology/cognitive_case_diagrams/releases/tag/v2.4.0" }, "2026_FEPLean": { "year": "2026", "topic": "FEPLean", "name": "Towards Lean 4 Formalization of the Free Energy Principle: AI-Driven Theorem Sketching and Verification for Active Inference and Bayesian Mechanics", "description": "FEP_Lean v1.1.0 is a source-bound, machine-checked catalogue of 155 topics across 20 reviewed families and five areas: the Free Energy Principle, Active Inference, Bayesian Mechanics, Information Geometry, and non-equilibrium Thermodynamics. Every catalogue row carries a reviewed invariant, explicit assumptions and boundaries, a namespaced Lean 4 theorem body, and deterministic manuscript metadata.
The release is pinned to Lean 4.33.1 and Mathlib 4.33.1 (locked Mathlib revision 0df444a360eaa60ab8c11dca51a86af692955474). Its native receipt verifies 155/155 topic closures with zero errors, warnings, or sorry. The schema-4 formalism audit covers all 823 required formal-resource declarations, including 699 evidence declarations, and reports no sorryAx or untrusted project axioms. The canonical Python acceptance run collected 1,203 tests: 1,080 passed, 123 skipped, zero failed or errored, with 89.81% line coverage (8,808/9,807 statements). The schema-4 Chrome 151 browser receipt replays six source-bound screenshots covering 155 topics, 20 families, 133 relations, 48 satisfied capabilities, and 15 typed numerical witnesses.
These checks establish the exact shipped formal statements and reproducible software evidence; they do not prove the Free Energy Principle as a physical theory. Provider-backed Hermes commentary is optional and unavailable for this release cut, and no historical provider report is promoted to current full-mode evidence.
Source and release: github.com/ActiveInferenceInstitute/fep_lean and GitHub release v1.1.0. The exact deterministic 232-member evidence bundle has SHA-256 0009447598ecd3bbf68548eab360704a2539480379791eb0128186fa230884ea and binds release commit d7b6f8b15ea9dc451b191e3674a1fd72b5e586b4.
FEP_Lean v1.1.0 is a source-bound, machine-checked catalogue of 155 topics across 20 reviewed families and five areas: the Free Energy Principle, Active Inference, Bayesian Mechanics, Information Geometry, and non-equilibrium Thermodynamics. Every catalogue row carries a reviewed invariant, explicit assumptions and boundaries, a namespaced Lean 4 theorem body, and deterministic manuscript metadata.
The release is pinned to Lean 4.33.1 and Mathlib 4.33.1 (locked Mathlib revision 0df444a360eaa60ab8c11dca51a86af692955474). Its native receipt verifies 155/155 topic closures with zero errors, warnings, or sorry. The schema-4 formalism audit covers all 823 required formal-resource declarations, including 699 evidence declarations, and reports no sorryAx or untrusted project axioms. The canonical Python acceptance run collected 1,203 tests: 1,080 passed, 123 skipped, zero failed or errored, with 89.81% line coverage (8,808/9,807 statements). The schema-4 Chrome 151 browser receipt replays six source-bound screenshots covering 155 topics, 20 families, 133 relations, 48 satisfied capabilities, and 15 typed numerical witnesses.
These checks establish the exact shipped formal statements and reproducible software evidence; they do not prove the Free Energy Principle as a physical theory. Provider-backed Hermes commentary is optional and unavailable for this release cut, and no historical provider report is promoted to current full-mode evidence.
Source and release: github.com/ActiveInferenceInstitute/fep_lean and GitHub release v1.1.0. The exact deterministic 232-member evidence bundle has SHA-256 0009447598ecd3bbf68548eab360704a2539480379791eb0128186fa230884ea and binds release commit d7b6f8b15ea9dc451b191e3674a1fd72b5e586b4.
Introduction to Biology: A Generative Approach is an open biology textbook with forty-four chapters, ranging from systems science and chemical foundations through cells, metabolism, genetics, microbiology, physiology, evolution, and ecology. Organized as Unit 0 plus Units I–X, the text presents biology as an evidence-grounded discipline in which mechanisms, measurements, and simple models are developed together, so readers can move between narrative explanation and the quantitative constraints that shape biological claims. Five recurring themes—evolution, information, structure and function, systems and emergence, and the cell—provide orientation across scales and align with mainstream undergraduate biology competencies; Unit 0 adds an optional systems, historical, and philosophical lens without replacing the traditional molecular-to-ecological sequence.
\nWhere the curriculum is quantitative, corresponding computations live in tested code modules organized by domain (biochemistry, cell biology, genetics, physiology, ecology, evolution, microbiology, botany, and neuroscience), and many figures and process diagrams are generated programmatically rather than supplied as static artwork alone. The edition pairs each chapter with a paper-based laboratory activity and a question bank that progresses from recall to synthesis; model answers are visible in this instructor build. Primary literature is cited inline, glossary and curriculum-mapping appendices support course design, and the manuscript is maintained as a reproducible open-science artifact (source at https://github.com/docxology/biology_textbook ; archived at DOI 10.5281/zenodo.20286478). Text is released under Creative Commons Attribution 4.0; accompanying source code under Apache-2.0.
", "authors": "Daniel Ari Friedman", "abstract": "Introduction to Biology: A Generative Approach is an open biology textbook with forty-four chapters, ranging from systems science and chemical foundations through cells, metabolism, genetics, microbiology, physiology, evolution, and ecology. Organized as Unit 0 plus Units I–X, the text presents biology as an evidence-grounded discipline in which mechanisms, measurements, and simple models are developed together, so readers can move between narrative explanation and the quantitative constraints that shape biological claims. Five recurring themes—evolution, information, structure and function, systems and emergence, and the cell—provide orientation across scales and align with mainstream undergraduate biology competencies; Unit 0 adds an optional systems, historical, and philosophical lens without replacing the traditional molecular-to-ecological sequence.
\nWhere the curriculum is quantitative, corresponding computations live in tested code modules organized by domain (biochemistry, cell biology, genetics, physiology, ecology, evolution, microbiology, botany, and neuroscience), and many figures and process diagrams are generated programmatically rather than supplied as static artwork alone. The edition pairs each chapter with a paper-based laboratory activity and a question bank that progresses from recall to synthesis; model answers are visible in this instructor build. Primary literature is cited inline, glossary and curriculum-mapping appendices support course design, and the manuscript is maintained as a reproducible open-science artifact (source at https://github.com/docxology/biology_textbook ; archived at DOI 10.5281/zenodo.20286478). Text is released under Creative Commons Attribution 4.0; accompanying source code under Apache-2.0.
", "keywords": [ "Biology" ], "doi": "10.5281/zenodo.20286478", "github_release_url": "https://github.com/docxology/biology_textbook/releases/tag/v1.0.0" }, "2026_PolicyEntanglementActive": { "year": "2026", "topic": "PolicyEntanglementActive", "name": "Policy Entanglement in Active Inference: A Coupling-Parameter Deformation Framework for Multi-Stream Policy Posterior Distributions, Machine-Checked and Simulated with a Typed Float Boundary", "description": "Active inference models often need to choose among several policy streams at once, for example streams tied to different effectors, sensory channels, agents, agents within a group, or planning horizons. Standard discrete active-inference implementations keep this manageable by treating those streams as independent, but that simplification removes the dependencies that make coordinated action possible. This manuscript introduces policy entanglement: a controlled deformation of the usual independent policy posterior by a scalar coupling strength and explicit compatibility and preference potentials. The construction preserves the finite active inference setting while making cross-stream dependence a first-class modeling object rather than an implicit artifact of the chosen factorization. The framework keeps a claim-strength ledger that distinguishes exact recoveries, parameterized embeddings, numerical witnesses, and structural analogies. Mean-field active inference is the exact independent case. Products of experts, copula variational inference, options, hierarchical and sophisticated inference, branching-time active inference, renormalization-style compression, and Markov-blanket multi-agent views are connected as special cases through their stated posterior-factorization maps. The central result is a free-energy decomposition that separates ordinary per-stream free energy, coupling preference terms, the coupling normalizer, and the information cost of leaving independence. The decomposition makes multi-information the explicit surcharge paid by a non-factorized policy posterior and shows how coupling strength, compatibility structure, and off-diagonal preference costs enter the same accounting identity. This result supplies the organizing principle for the rest of the paper. It supports an information-geometric reading of the coupled policy family as a path away from the mean-field submanifold, a projection identity that returns the coupled posterior to its independent marginals, a spectral and tensor-train view of dominant coordinated policy modes, a heterogeneous-ensemble coupling-tax bound, and a phase vocabulary for under-coupled, mixed, and highly concentrated policy posteriors. These interpretations are intentionally limited: the manuscript does not claim a neural, clinical, biological, or quantum implementation, and Markov-blanket and tensor-network language is used as scoped modeling analogy unless a specific theorem row or generated artifact supports a stronger statement. The main decomposition analytic identity is machine-checked in ℝ in the Mathlib-backed Lean layer with an axiom audit and negative controls. A separate stock-Lean boundary fragment remains Mathlib-free and exposes the theorem surface as typed contracts for the Python simulation layer and the manuscript registry, including witness-consuming rows where analytic payloads are deliberately supplied at the boundary. The executable numerical layer remains a Float pipeline, so a verified Float↔ℝ error bridge is still an explicitly open interface rather than an implied proof; conservative interval brackets on the K=2 decomposition sweep certify Float residuals within a widened high-precision envelope (output/reports/float_real_residual.json) without promoting the registry row to proved. The empirical companion uses pymdp and NumPy to sweep coupled policy ensembles, run short and long rollouts, check the projection identity to round-off precision, produce free-energy, entropy, total-correlation, action-distribution, robustness, and adversarial sidecars, and render figures from those artifacts. The manuscript, figures, theorem map, citation registry, notation glossary (§S6), bibliography, and PDF are regenerated from the same source-owned pipeline, so prose claims are tied to Lean sources, Python witnesses, output metadata, and validation gates rather than maintained by hand. All manuscript methods, tests, and documentation are available as open-source software at https://github.com/ActiveInferenceInstitute/policy_entanglement (DOI: https://doi.org/10.5281/zenodo.20419536). --- Associated artifacts GitHub release: v1.0.0 (https://github.com/ActiveInferenceInstitute/policy_entanglement/releases/tag/v1.0.0) DOI: https://doi.org/10.5281/zenodo.20418904 Zenodo: https://zenodo.org/records/20418904 PDF SHA-256: ae7cdd62929324101ead3eba8177199141b0089a9baf35558107149331666fde
", "authors": "Daniel Ari Friedman", "abstract": "Active inference models often need to choose among several policy streams at once, for example streams tied to different effectors, sensory channels, agents, agents within a group, or planning horizons. Standard discrete active-inference implementations keep this manageable by treating those streams as independent, but that simplification removes the dependencies that make coordinated action possible. This manuscript introduces policy entanglement: a controlled deformation of the usual independent policy posterior by a scalar coupling strength and explicit compatibility and preference potentials. The construction preserves the finite active inference setting while making cross-stream dependence a first-class modeling object rather than an implicit artifact of the chosen factorization. The framework keeps a claim-strength ledger that distinguishes exact recoveries, parameterized embeddings, numerical witnesses, and structural analogies. Mean-field active inference is the exact independent case. Products of experts, copula variational inference, options, hierarchical and sophisticated inference, branching-time active inference, renormalization-style compression, and Markov-blanket multi-agent views are connected as special cases through their stated posterior-factorization maps. The central result is a free-energy decomposition that separates ordinary per-stream free energy, coupling preference terms, the coupling normalizer, and the information cost of leaving independence. The decomposition makes multi-information the explicit surcharge paid by a non-factorized policy posterior and shows how coupling strength, compatibility structure, and off-diagonal preference costs enter the same accounting identity. This result supplies the organizing principle for the rest of the paper. It supports an information-geometric reading of the coupled policy family as a path away from the mean-field submanifold, a projection identity that returns the coupled posterior to its independent marginals, a spectral and tensor-train view of dominant coordinated policy modes, a heterogeneous-ensemble coupling-tax bound, and a phase vocabulary for under-coupled, mixed, and highly concentrated policy posteriors. These interpretations are intentionally limited: the manuscript does not claim a neural, clinical, biological, or quantum implementation, and Markov-blanket and tensor-network language is used as scoped modeling analogy unless a specific theorem row or generated artifact supports a stronger statement. The main decomposition analytic identity is machine-checked in ℝ in the Mathlib-backed Lean layer with an axiom audit and negative controls. A separate stock-Lean boundary fragment remains Mathlib-free and exposes the theorem surface as typed contracts for the Python simulation layer and the manuscript registry, including witness-consuming rows where analytic payloads are deliberately supplied at the boundary. The executable numerical layer remains a Float pipeline, so a verified Float↔ℝ error bridge is still an explicitly open interface rather than an implied proof; conservative interval brackets on the K=2 decomposition sweep certify Float residuals within a widened high-precision envelope (output/reports/float_real_residual.json) without promoting the registry row to proved. The empirical companion uses pymdp and NumPy to sweep coupled policy ensembles, run short and long rollouts, check the projection identity to round-off precision, produce free-energy, entropy, total-correlation, action-distribution, robustness, and adversarial sidecars, and render figures from those artifacts. The manuscript, figures, theorem map, citation registry, notation glossary (§S6), bibliography, and PDF are regenerated from the same source-owned pipeline, so prose claims are tied to Lean sources, Python witnesses, output metadata, and validation gates rather than maintained by hand. All manuscript methods, tests, and documentation are available as open-source software at https://github.com/ActiveInferenceInstitute/policy_entanglement (DOI: https://doi.org/10.5281/zenodo.20419536). --- Associated artifacts GitHub release: v1.0.0 (https://github.com/ActiveInferenceInstitute/policy_entanglement/releases/tag/v1.0.0) DOI: https://doi.org/10.5281/zenodo.20418904 Zenodo: https://zenodo.org/records/20418904 PDF SHA-256: ae7cdd62929324101ead3eba8177199141b0089a9baf35558107149331666fde
", "keywords": [ "active inference", "free energy principle", "policy inference", "mean-field", "total correlation", "information geometry", "Schmidt rank", "tensor networks", "sophisticated inference", "Lean theorem proving", "machine-checked free-energy identity" ], "doi": "10.5281/zenodo.20418904", "github_release_url": "https://github.com/ActiveInferenceInstitute/policy_entanglement/releases/tag/v1.0.0" }, "2026_BoundedAutoResearchTiny": { "year": "2026", "topic": "BoundedAutoResearchTiny", "name": "Bounded AutoResearch for a Tiny Reproducible Machine-Learning Task", "description": "This paper presents Deterministic bounded AutoResearch for a small MNIST neural-network task, a public template exemplar that\nturns an AutoResearch loop into ordinary reproducible research infrastructure.\nThe case study is intentionally small but concrete: 2000 training\nand 500 test images from MNIST handwritten digit database are evaluated by the\nbounded small MNIST neural-network classification loop. The run evaluates\n4 of 5 proposed candidates,\nincluding Tiny patch-attention classifier, selects\nexp-mlp-tanh-64 (MLP,\n50890 parameters), and improves test_accuracy from\n82.6% to 89.4%\n(6.8% absolute change). The validated diagnostic layer reports\nmacro F1 89.4%, bootstrap accuracy interval\n86.4% to 92.0%, Brier score 0.161,\nnegative log likelihood 0.361, top-2 accuracy\n95.6%, and exact McNemar p-value 0.000.\nThe same pipeline writes proposal, candidate, run, review, benchmark, evidence,\nfigure, confusion-matrix, statistical-summary, probability-quality, and\nsecurity-integrity artifacts from declared output contracts; uses\n0 LLM calls at USD 0.00 cost; and records\n7 configured stages, 6 supported\nlocal-artifact claims, and 78 required artifacts.\nThe local security attestation status is passed,\nwith 0 checksum mismatch(es). The final\nreadiness status is passed, with review gates deferred to a\nhuman rather than self-approved by the generated run.\n\n---\nAssociated artifacts\nGitHub release: v0.3.2 (https://github.com/docxology/template_autoresearch_project/releases/tag/v0.3.2)\nDOI: https://doi.org/10.5281/zenodo.20417016\nZenodo: https://zenodo.org/records/20417016\nPDF SHA-256: e07b62850a1995935283d37a45c21d71fa7c4e69cdcc451c5a1ea8aee6d0c94a", "authors": "Daniel Ari Friedman", "abstract": "This paper presents Deterministic bounded AutoResearch for a small MNIST neural-network task, a public template exemplar that\nturns an AutoResearch loop into ordinary reproducible research infrastructure.\nThe case study is intentionally small but concrete: 2000 training\nand 500 test images from MNIST handwritten digit database are evaluated by the\nbounded small MNIST neural-network classification loop. The run evaluates\n4 of 5 proposed candidates,\nincluding Tiny patch-attention classifier, selects\nexp-mlp-tanh-64 (MLP,\n50890 parameters), and improves test_accuracy from\n82.6% to 89.4%\n(6.8% absolute change). The validated diagnostic layer reports\nmacro F1 89.4%, bootstrap accuracy interval\n86.4% to 92.0%, Brier score 0.161,\nnegative log likelihood 0.361, top-2 accuracy\n95.6%, and exact McNemar p-value 0.000.\nThe same pipeline writes proposal, candidate, run, review, benchmark, evidence,\nfigure, confusion-matrix, statistical-summary, probability-quality, and\nsecurity-integrity artifacts from declared output contracts; uses\n0 LLM calls at USD 0.00 cost; and records\n7 configured stages, 6 supported\nlocal-artifact claims, and 78 required artifacts.\nThe local security attestation status is passed,\nwith 0 checksum mismatch(es). The final\nreadiness status is passed, with review gates deferred to a\nhuman rather than self-approved by the generated run.\n\n---\nAssociated artifacts\nGitHub release: v0.3.2 (https://github.com/docxology/template_autoresearch_project/releases/tag/v0.3.2)\nDOI: https://doi.org/10.5281/zenodo.20417016\nZenodo: https://zenodo.org/records/20417016\nPDF SHA-256: e07b62850a1995935283d37a45c21d71fa7c4e69cdcc451c5a1ea8aee6d0c94a", "keywords": [ "autoresearch", "reproducible research", "machine learning benchmark", "artifact readiness", "human review", "local artifact integrity" ], "doi": "10.5281/zenodo.20417016", "github_release_url": "https://github.com/docxology/template_autoresearch_project/releases/tag/v0.3.2" }, "2026_ConvergenceAnalysisGradient": { "year": "2026", "topic": "ConvergenceAnalysisGradient", "name": "Convergence Analysis of Gradient Descent Optimization", "description": "This paper presents a convergence study of fixed-step gradient descent on a convex quadratic, framed as the computational exemplar of the Research Project Template (https://github.com/docxology/template). The implementation lives in projects/templates/template_code_project/src/optimizer.py; experiments and figures are orchestrated by projects/templates/template_code_project/scripts/optimization_analysis.py and hydrated into the manuscript through scripts/z_generate_manuscript_variables.py, so tables and prose track output/data/optimization_results.csv after every pipeline run.\n\nWe evaluate 6 step sizes from $\\alpha = 0.01$ to $\\alpha = 2.5$, spanning conservative, near-optimal, aggressive, and divergent regimes for a unit Hessian model. The build chain exercises template infrastructure end-to-end: scientific helpers (infrastructure.scientific.stability, infrastructure.scientific.benchmarking), validation, rendering (infrastructure/rendering/pdf_renderer.py), and reporting. Accessibility-oriented plotting defaults (colourblind-safe palette, 300 dpi exports) are centralized in src/figures/ and src/analysis/.\n\nContributions are methodological and architectural. On the methods side, we relate empirical iteration counts and error decay to the scalar contraction factor $\\rho(\\alpha) = |1-\\alpha|$ and document cases where runs hit $N_{\\max} = 1000$ before meeting the gradient tolerance. On the architecture side, we demonstrate a zero-mock test suite on project src/ (see test_optimizer.py (https://github.com/docxology/template/blob/main/projects/templates/template_code_project/tests/test_optimizer.py)), automated six-figure analysis, and reproducibility metadata (configuration hash, artifact counts) injected into .\n\nResults (this configuration): 4 of 6 grid points report converged=True in the CSV; non-convergent rows flag either slow progress at small $\\alpha$ under the iteration cap or instability when $|1-\\alpha| \\geq 1$. The analytical minimizer remains $x^\\ast = 1.0$ with $f(x^\\ast) = -0.5$ for the configured $(A,b)$.\n\nKeywords: optimization algorithms, gradient descent, convergence analysis, numerical methods, mathematical programming, reproducible research, infrastructure automation\n\n---\nAssociated artifacts\nGitHub release: v2.5.2 (https://github.com/docxology/template_code_project/releases/tag/v2.5.2)\nDOI: https://doi.org/10.5281/zenodo.20417136\nZenodo: https://zenodo.org/records/20417136\nPDF SHA-256: cd54b95893501467503fab2c4b432573306bc94f7040085550beb87d094b4e50", "authors": "Daniel Ari Friedman", "abstract": "This paper presents a convergence study of fixed-step gradient descent on a convex quadratic, framed as the computational exemplar of the Research Project Template (https://github.com/docxology/template). The implementation lives in projects/templates/template_code_project/src/optimizer.py; experiments and figures are orchestrated by projects/templates/template_code_project/scripts/optimization_analysis.py and hydrated into the manuscript through scripts/z_generate_manuscript_variables.py, so tables and prose track output/data/optimization_results.csv after every pipeline run.\n\nWe evaluate 6 step sizes from $\\alpha = 0.01$ to $\\alpha = 2.5$, spanning conservative, near-optimal, aggressive, and divergent regimes for a unit Hessian model. The build chain exercises template infrastructure end-to-end: scientific helpers (infrastructure.scientific.stability, infrastructure.scientific.benchmarking), validation, rendering (infrastructure/rendering/pdf_renderer.py), and reporting. Accessibility-oriented plotting defaults (colourblind-safe palette, 300 dpi exports) are centralized in src/figures/ and src/analysis/.\n\nContributions are methodological and architectural. On the methods side, we relate empirical iteration counts and error decay to the scalar contraction factor $\\rho(\\alpha) = |1-\\alpha|$ and document cases where runs hit $N_{\\max} = 1000$ before meeting the gradient tolerance. On the architecture side, we demonstrate a zero-mock test suite on project src/ (see test_optimizer.py (https://github.com/docxology/template/blob/main/projects/templates/template_code_project/tests/test_optimizer.py)), automated six-figure analysis, and reproducibility metadata (configuration hash, artifact counts) injected into .\n\nResults (this configuration): 4 of 6 grid points report converged=True in the CSV; non-convergent rows flag either slow progress at small $\\alpha$ under the iteration cap or instability when $|1-\\alpha| \\geq 1$. The analytical minimizer remains $x^\\ast = 1.0$ with $f(x^\\ast) = -0.5$ for the configured $(A,b)$.\n\nKeywords: optimization algorithms, gradient descent, convergence analysis, numerical methods, mathematical programming, reproducible research, infrastructure automation\n\n---\nAssociated artifacts\nGitHub release: v2.5.2 (https://github.com/docxology/template_code_project/releases/tag/v2.5.2)\nDOI: https://doi.org/10.5281/zenodo.20417136\nZenodo: https://zenodo.org/records/20417136\nPDF SHA-256: cd54b95893501467503fab2c4b432573306bc94f7040085550beb87d094b4e50", "keywords": [ "optimization algorithms", "gradient descent", "convergence analysis", "numerical methods", "mathematical programming", "reproducible research", "infrastructure automation" ], "doi": "10.5281/zenodo.20417136", "github_release_url": "https://github.com/docxology/template_code_project/releases/tag/v2.5.2" }, "2026_EditorialQualityAt": { "year": "2026", "topic": "EditorialQualityAt", "name": "Editorial Quality at Scale: A Reproducible Prose-Review Pipeline", "description": "This paper documents template_prose_project, the prose-focused exemplar of the Research Project Template (https://github.com/docxology/template). It pairs the template's two-layer architecture with the prose analysis infrastructure (https://github.com/docxology/template/tree/main/infrastructure/prose) (readability metrics, structural outline, editorial quality flags) and the reference validation infrastructure (https://github.com/docxology/template/tree/main/infrastructure/reference) (BibTeX validation), demonstrating that rigorous editorial review can be expressed as a configurable, deterministic pipeline with no novel domain algorithm of its own.\n\nA single manuscript/config.yaml defines target grade-level bands, citation-density floors, structural rules (every section has an H1, no heading levels skipped), and bibliography-consistency policy. The pipeline reads the manuscript, runs the prose analysers, cross-checks every citation against manuscript/references.bib, evaluates the configured checks, and writes a deterministic markdown review report alongside three figures (per-file word counts, readability metrics, citation density) and a JSON manuscript_report.json suitable for CI artefacts.\n\nRun snapshot. The current configuration analyses 8 file(s) totalling 1742 words across 86 sentence(s) and 64 paragraph(s). Average Flesch-Kincaid grade level is 15.93; average Gunning Fog index is 16.69; the manuscript references 6 unique citation key(s); the longest section is 413 words and the shortest is 17. These numbers are auto-substituted by scripts/z_generate_manuscript_variables.py after every run, so the abstract tracks the JSON outputs in output/.\n\nThe contribution is methodological and architectural: a generic, reusable prose-quality module (infrastructure/prose/) that any project in the template can opt into, plus a minimal, configurable exemplar (projects/templates/template_prose_project/) that wires it to the bibliography and the manuscript pipeline.\n\nKeywords: prose analysis, readability, editorial review, reproducible manuscript review, scientific infrastructure\n\n---\nAssociated artifacts\nGitHub release: v0.4.2 (https://github.com/docxology/template_prose_project/releases/tag/v0.4.2)\nDOI: https://doi.org/10.5281/zenodo.20417104\nZenodo: https://zenodo.org/records/20417104\nPDF SHA-256: 290d21b10bd588b978d6a3200cdf0e3c2441ca86fcdc777ab41975fa910a260e", "authors": "Daniel Ari Friedman", "abstract": "This paper documents template_prose_project, the prose-focused exemplar of the Research Project Template (https://github.com/docxology/template). It pairs the template's two-layer architecture with the prose analysis infrastructure (https://github.com/docxology/template/tree/main/infrastructure/prose) (readability metrics, structural outline, editorial quality flags) and the reference validation infrastructure (https://github.com/docxology/template/tree/main/infrastructure/reference) (BibTeX validation), demonstrating that rigorous editorial review can be expressed as a configurable, deterministic pipeline with no novel domain algorithm of its own.\n\nA single manuscript/config.yaml defines target grade-level bands, citation-density floors, structural rules (every section has an H1, no heading levels skipped), and bibliography-consistency policy. The pipeline reads the manuscript, runs the prose analysers, cross-checks every citation against manuscript/references.bib, evaluates the configured checks, and writes a deterministic markdown review report alongside three figures (per-file word counts, readability metrics, citation density) and a JSON manuscript_report.json suitable for CI artefacts.\n\nRun snapshot. The current configuration analyses 8 file(s) totalling 1742 words across 86 sentence(s) and 64 paragraph(s). Average Flesch-Kincaid grade level is 15.93; average Gunning Fog index is 16.69; the manuscript references 6 unique citation key(s); the longest section is 413 words and the shortest is 17. These numbers are auto-substituted by scripts/z_generate_manuscript_variables.py after every run, so the abstract tracks the JSON outputs in output/.\n\nThe contribution is methodological and architectural: a generic, reusable prose-quality module (infrastructure/prose/) that any project in the template can opt into, plus a minimal, configurable exemplar (projects/templates/template_prose_project/) that wires it to the bibliography and the manuscript pipeline.\n\nKeywords: prose analysis, readability, editorial review, reproducible manuscript review, scientific infrastructure\n\n---\nAssociated artifacts\nGitHub release: v0.4.2 (https://github.com/docxology/template_prose_project/releases/tag/v0.4.2)\nDOI: https://doi.org/10.5281/zenodo.20417104\nZenodo: https://zenodo.org/records/20417104\nPDF SHA-256: 290d21b10bd588b978d6a3200cdf0e3c2441ca86fcdc777ab41975fa910a260e", "keywords": [ "prose analysis", "readability", "editorial review", "reproducible research", "manuscript quality" ], "doi": "10.5281/zenodo.20417104", "github_release_url": "https://github.com/docxology/template_prose_project/releases/tag/v0.4.2" }, "2026_TemplateApproachReproducible": { "year": "2026", "topic": "TemplateApproachReproducible", "name": "A template/ approach to Reproducible Generative Research", "description": "The reproducibility crisis in computational research is fundamentally structural: research artifacts are scattered across disconnected tools—LaTeX editors, Jupyter notebooks, ad-hoc shell scripts—with no enforced mechanism to keep code, data, and manuscript synchronized. Studies have shown that most published findings are false positives, replication rates in psychology hover around 36%, and only 24% of 1.4 million Jupyter notebooks can be successfully re-executed. Existing tools address fragments of this problem: workflow managers (Snakemake, Nextflow, CWL) orchestrate computation; literate programming systems (Quarto, Jupyter Book, R Markdown, Overleaf, OpenAI Prism) render documents; data versioning tools (DVC) track artifacts—but none enforces cross-cutting quality standards as architectural invariants. template/ applies the principle of Infrastructure as Code to the research lifecycle, making the manuscript, test suite, and provenance chain version-controlled, deterministically buildable, and independently verifiable. It is built on a Two-Layer Architecture that separates 23 infrastructure subdirectories (20 importable Python packages, ~604 modules, validated by ~7,780 tests) from self-contained project workspaces, connected by a YAML-declared pipeline (12 stages; default full 10)-based build pipeline progressing from environment sanitization through test execution (with a Zero-Mock testing policy enforcing 90% project-level and 60% infrastructure-level coverage via real filesystem operations and subprocess invocations), analysis script invocation, Pandoc/XeLaTeX rendering, SHA-256 cryptographic hashing with steganographic watermarking, structural PDF validation, and LLM-assisted review. A Documentation Duality standard equips every directory with both human-readable README.md and machine-readable AGENTS.md files, while each infrastructure module additionally carries a SKILL.md—a structured skill descriptor aligned with the Model Context Protocol—enabling AI agents to locate and invoke module capabilities without hallucinating API signatures.\n\nScalability is demonstrated across the generated public exemplar roster (templates/template_active_inference, templates/template_autoresearch_project, templates/template_autoscientists, templates/template_code_project, templates/template_gold_refinement, templates/template_literature_meta_analysis, templates/template_madlib, templates/template_newspaper, templates/template_prose_project, templates/template_sia, templates/template_template, templates/template_textbook), with representative heterogeneous cases under projects/templates/: optimization (template_code_project, 231 tests), prose (template_prose_project, 120 tests), and AutoResearch readiness (template_autoresearch_project, 296 tests). These guarantee control-positive layouts for code-centric, prose-centric, and retrieval-centric workflows at 90%+ project coverage alongside 60%+ infrastructure gates. All three share identical pipeline stages without cross-project coupling. This manuscript adds a complementary reflexive artifact: authored from projects/templates/template_template (127 tests) as a public exemplar in the same discovered/rendered tree, using the same analysis and render path and injecting counters from repository introspection. The fact that these words, metrics, and figures were generated by the pipeline they describe demonstrates self-documenting capacity: rendered through the DAG, validated without mocks, optionally watermarked. A comparative analysis against nine peer tools across fourteen dimensions positions template/ as integrating fourteen distinctive enforcement capabilities—testing thresholds, cryptographic provenance, steganographic watermarking, multi-project management, MCP-aligned skill descriptors, Zero-Mock policy, orchestration through publication—in one repository. Code is released under the Apache License 2.0 at github.com/docxology/template; the work remains open-ended.\n\n---\nAssociated artifacts\nGitHub release: v1.0.9 (https://github.com/docxology/template_template/releases/tag/v1.0.9)\nDOI: https://doi.org/10.5281/zenodo.20419007\nZenodo: https://zenodo.org/records/20419007\nPDF SHA-256: 535bd80943d0ae9fd504a926efb41c6b39c3a812a94ea4d51bc974029bca563c", "authors": "Daniel Ari Friedman", "abstract": "The reproducibility crisis in computational research is fundamentally structural: research artifacts are scattered across disconnected tools—LaTeX editors, Jupyter notebooks, ad-hoc shell scripts—with no enforced mechanism to keep code, data, and manuscript synchronized. Studies have shown that most published findings are false positives, replication rates in psychology hover around 36%, and only 24% of 1.4 million Jupyter notebooks can be successfully re-executed. Existing tools address fragments of this problem: workflow managers (Snakemake, Nextflow, CWL) orchestrate computation; literate programming systems (Quarto, Jupyter Book, R Markdown, Overleaf, OpenAI Prism) render documents; data versioning tools (DVC) track artifacts—but none enforces cross-cutting quality standards as architectural invariants. template/ applies the principle of Infrastructure as Code to the research lifecycle, making the manuscript, test suite, and provenance chain version-controlled, deterministically buildable, and independently verifiable. It is built on a Two-Layer Architecture that separates 23 infrastructure subdirectories (20 importable Python packages, ~604 modules, validated by ~7,780 tests) from self-contained project workspaces, connected by a YAML-declared pipeline (12 stages; default full 10)-based build pipeline progressing from environment sanitization through test execution (with a Zero-Mock testing policy enforcing 90% project-level and 60% infrastructure-level coverage via real filesystem operations and subprocess invocations), analysis script invocation, Pandoc/XeLaTeX rendering, SHA-256 cryptographic hashing with steganographic watermarking, structural PDF validation, and LLM-assisted review. A Documentation Duality standard equips every directory with both human-readable README.md and machine-readable AGENTS.md files, while each infrastructure module additionally carries a SKILL.md—a structured skill descriptor aligned with the Model Context Protocol—enabling AI agents to locate and invoke module capabilities without hallucinating API signatures.\n\nScalability is demonstrated across the generated public exemplar roster (templates/template_active_inference, templates/template_autoresearch_project, templates/template_autoscientists, templates/template_code_project, templates/template_gold_refinement, templates/template_literature_meta_analysis, templates/template_madlib, templates/template_newspaper, templates/template_prose_project, templates/template_sia, templates/template_template, templates/template_textbook), with representative heterogeneous cases under projects/templates/: optimization (template_code_project, 231 tests), prose (template_prose_project, 120 tests), and AutoResearch readiness (template_autoresearch_project, 296 tests). These guarantee control-positive layouts for code-centric, prose-centric, and retrieval-centric workflows at 90%+ project coverage alongside 60%+ infrastructure gates. All three share identical pipeline stages without cross-project coupling. This manuscript adds a complementary reflexive artifact: authored from projects/templates/template_template (127 tests) as a public exemplar in the same discovered/rendered tree, using the same analysis and render path and injecting counters from repository introspection. The fact that these words, metrics, and figures were generated by the pipeline they describe demonstrates self-documenting capacity: rendered through the DAG, validated without mocks, optionally watermarked. A comparative analysis against nine peer tools across fourteen dimensions positions template/ as integrating fourteen distinctive enforcement capabilities—testing thresholds, cryptographic provenance, steganographic watermarking, multi-project management, MCP-aligned skill descriptors, Zero-Mock policy, orchestration through publication—in one repository. Code is released under the Apache License 2.0 at github.com/docxology/template; the work remains open-ended.\n\n---\nAssociated artifacts\nGitHub release: v1.0.9 (https://github.com/docxology/template_template/releases/tag/v1.0.9)\nDOI: https://doi.org/10.5281/zenodo.20419007\nZenodo: https://zenodo.org/records/20419007\nPDF SHA-256: 535bd80943d0ae9fd504a926efb41c6b39c3a812a94ea4d51bc974029bca563c", "keywords": [ "reproducible research", "infrastructure-as-code", "steganography", "cryptographic provenance", "LaTeX rendering", "modular infrastructure", "publication integrity", "zero-mock testing", "thin orchestrator", "two-layer architecture", "FAIR4RS", "research software engineering" ], "doi": "10.5281/zenodo.20419007", "github_release_url": "https://github.com/docxology/template_template/releases/tag/v1.0.9" }, "2026_ActiveInferenceMulti": { "year": "2026", "topic": "ActiveInferenceMulti", "name": "Active Inference Multi-Track Exemplar", "description": "We study a minimal Active Inference stack on toy models: a Bernoulli–Ising analytical oracle, a pymdp T-maze rollout, and a sheaf-indexed compose contract that binds 34 fragment tracks into 12 flat IMRAD sections. The methodological contribution is a discipline rather than a domain finding: every reported number is hydrated from a generated artifact and every cross-track claim is machine-checked before rendering, so no figure or statistic can drift from the artifact that produced it — 6 sheaf axioms are verified before composition and 25 negative controls keep each failure path live. Claims are limited to those models and their generated artifacts.\n\n reports a 17-row coverage matrix (5 IMRAD group headers) regenerated from the live manifest at compose time. documents the T-maze harness aligned with pymdp sophisticated_inference examples (https://github.com/infer-actively/pymdp/tree/main/examples/experimental/sophisticated_inference).\n\n records 12 / 12 invariant checks passed. SI planning horizon: 2 steps. Sweep RMSE 0 nats bounds analytical–empirical agreement on the coupling grid.\n\n---\nAssociated artifacts\nGitHub release: v0.3.2 (https://github.com/docxology/template_active_inference/releases/tag/v0.3.2)\nDOI: https://doi.org/10.5281/zenodo.20417021\nZenodo: https://zenodo.org/records/20417021\nPDF SHA-256: f191b48f94394cab17069fd04502c59fc1c287e7893eb078e05ba4be04d4a04c", "authors": "Daniel Ari Friedman", "abstract": "We study a minimal Active Inference stack on toy models: a Bernoulli–Ising analytical oracle, a pymdp T-maze rollout, and a sheaf-indexed compose contract that binds 34 fragment tracks into 12 flat IMRAD sections. The methodological contribution is a discipline rather than a domain finding: every reported number is hydrated from a generated artifact and every cross-track claim is machine-checked before rendering, so no figure or statistic can drift from the artifact that produced it — 6 sheaf axioms are verified before composition and 25 negative controls keep each failure path live. Claims are limited to those models and their generated artifacts.\n\n reports a 17-row coverage matrix (5 IMRAD group headers) regenerated from the live manifest at compose time. documents the T-maze harness aligned with pymdp sophisticated_inference examples (https://github.com/infer-actively/pymdp/tree/main/examples/experimental/sophisticated_inference).\n\n records 12 / 12 invariant checks passed. SI planning horizon: 2 steps. Sweep RMSE 0 nats bounds analytical–empirical agreement on the coupling grid.\n\n---\nAssociated artifacts\nGitHub release: v0.3.2 (https://github.com/docxology/template_active_inference/releases/tag/v0.3.2)\nDOI: https://doi.org/10.5281/zenodo.20417021\nZenodo: https://zenodo.org/records/20417021\nPDF SHA-256: f191b48f94394cab17069fd04502c59fc1c287e7893eb078e05ba4be04d4a04c", "keywords": [ "active inference", "pymdp", "sophisticated inference", "generalized notation notation", "lean" ], "doi": "10.5281/zenodo.20417021", "github_release_url": "https://github.com/docxology/template_active_inference/releases/tag/v0.3.2" }, "2026_BeeStack": { "year": "2026", "topic": "BeeStack", "name": "BeeStack: An Evidence-Typed Scaffold for Whole-Colony Honeybee Simulation", "description": "BeeStack is an executable, evidence-typed research scaffold for whole-colony simulation of the Western honey bee (Apis mellifera), organized as five layers (Body, Brain, Mind, Swarm, Niche). It pairs FlyBody/MuJoCo body and small-scene swarm renders with curated empirical BeeBrain datasets and reduced deterministic kernels, keeping fidelity a declared per-module property: every quoted number is traceable from configuration to artifact to manuscript, gaps are catalogued rather than hidden, and the validation rate is a config-band self-test measure, not a biological-realism score. This 1.0 release accompanies the manuscript 'BeeStack: An Evidence-Typed Scaffold for Whole-Colony Honeybee Simulation' and includes the combined PDF and the full source archive. Source: https://github.com/docxology/BeeStack", "authors": "Daniel Ari Friedman, Tucker Cahill Chambers", "abstract": "BeeStack is an executable, evidence-typed research scaffold for whole-colony simulation of the Western honey bee (Apis mellifera), organized as five layers (Body, Brain, Mind, Swarm, Niche). It pairs FlyBody/MuJoCo body and small-scene swarm renders with curated empirical BeeBrain datasets and reduced deterministic kernels, keeping fidelity a declared per-module property: every quoted number is traceable from configuration to artifact to manuscript, gaps are catalogued rather than hidden, and the validation rate is a config-band self-test measure, not a biological-realism score. This 1.0 release accompanies the manuscript 'BeeStack: An Evidence-Typed Scaffold for Whole-Colony Honeybee Simulation' and includes the combined PDF and the full source archive. Source: https://github.com/docxology/BeeStack", "keywords": [ "honeybee", "Apis mellifera", "active inference", "simulation scaffold", "swarm intelligence", "niche construction", "FlyBody", "MuJoCo", "antennal lobe", "mushroom body", "central complex", "waggle dance", "BEEHAVE", "reproducible research" ], "doi": "10.5281/zenodo.20420556", "github_release_url": "https://github.com/docxology/BeeStack/releases/tag/v1.0.0" }, "2026_WhenDoBugs": { "year": "2026", "topic": "WhenDoBugs", "name": "When do bugs see (infra)red?", "description": "Objective: To review the plausibility of insect detection of infrared (IR) cues that covary with semiochemical vibrational signatures, and to produce falsifiable predictions through the integration of comparative entomology, spectroscopy, neural timing analysis, and computational electromagnetism. The vibrational theory remains contested, so the framework treats IR/vibrational sensing as a testable complement to molecular recognition rather than a replacement for receptor binding . Methods: We integrate: (i) literature-grounded morphometric ranges for antennal sensilla, (ii) ATR-FTIR evidence that insect body chemistry can support species discrimination, (iii) published olfactory receptor neuron timing constraints, and (iv) deterministic electromagnetic models that expose their assumptions and parameter sensitivity . Preregistered experimental protocols specify QCL/LED bands (2--25 µm), thermal matched controls, power density 0.1--2 mW/cm², and N≥50 per condition. All analyses use fixed random seeds (42) where stochastic routines are present. Results: The computational figures show where sensillum-scale dimensions, CHC-associated mid-IR bands, and atmospheric windows overlap, but they do not by themselves establish biological IR olfaction. The strongest empirical anchors are narrower: fast insect ORN first-spike timing, photomechanic IR organs in pyrophilous beetles, hematophagy IR cues in mosquitoes and kissing bugs, thermogenic pollination signals in cycads, thermosensitive coeloconic sensilla in ants, and passive cuticle IR optics . These sources motivate specific experiments while also constraining the manuscript's range and mechanism claims. Conclusions: The framework yields five preregistered falsifiers aligned with \\Cref{sec:discussion}: (1) spectral nulls under matched thermal load, (2) geometric mismatch between sensilla dimensions and predicted resonances, (3) environmental misalignment of CHC peaks with transmission windows, (4) temporal indistinguishability of IR versus thermal ORN latencies, and (5) behavioral independence of IR-only orientation from chemical gradients. Protocols specify QCL/LED bands (2--25 µm), matched power deposition, and N≥50 per condition to separate electromagnetic detection from thermal artifacts. Implications: Applications of this work include biomimetic IR sensor design, better-controlled pest-monitoring experiments, and clearer tests of whether insect olfactory systems ever use wavelength-specific electromagnetic information. Keywords: insect olfaction, infrared detection, vibrational theory, electromagnetic sensing, sensilla morphology, cuticular hydrocarbons, atmospheric transmission, biomimetic sensors Reproducibility: Complete implementation with seven case studies in Appendices --- Associated artifacts GitHub release: v1.0.0 (https://github.com/docxology/cohereants/releases/tag/v1.0.0) PDF SHA-256: 36bd97a8ff522937390213a69abb40c8c87a06ab783457e62d7effef4762bac2
", "authors": "Tucker Chambers, Daniel A. Friedman", "abstract": "Objective: To review the plausibility of insect detection of infrared (IR) cues that covary with semiochemical vibrational signatures, and to produce falsifiable predictions through the integration of comparative entomology, spectroscopy, neural timing analysis, and computational electromagnetism. The vibrational theory remains contested, so the framework treats IR/vibrational sensing as a testable complement to molecular recognition rather than a replacement for receptor binding . Methods: We integrate: (i) literature-grounded morphometric ranges for antennal sensilla, (ii) ATR-FTIR evidence that insect body chemistry can support species discrimination, (iii) published olfactory receptor neuron timing constraints, and (iv) deterministic electromagnetic models that expose their assumptions and parameter sensitivity . Preregistered experimental protocols specify QCL/LED bands (2--25 µm), thermal matched controls, power density 0.1--2 mW/cm², and N≥50 per condition. All analyses use fixed random seeds (42) where stochastic routines are present. Results: The computational figures show where sensillum-scale dimensions, CHC-associated mid-IR bands, and atmospheric windows overlap, but they do not by themselves establish biological IR olfaction. The strongest empirical anchors are narrower: fast insect ORN first-spike timing, photomechanic IR organs in pyrophilous beetles, hematophagy IR cues in mosquitoes and kissing bugs, thermogenic pollination signals in cycads, thermosensitive coeloconic sensilla in ants, and passive cuticle IR optics . These sources motivate specific experiments while also constraining the manuscript's range and mechanism claims. Conclusions: The framework yields five preregistered falsifiers aligned with \\Cref{sec:discussion}: (1) spectral nulls under matched thermal load, (2) geometric mismatch between sensilla dimensions and predicted resonances, (3) environmental misalignment of CHC peaks with transmission windows, (4) temporal indistinguishability of IR versus thermal ORN latencies, and (5) behavioral independence of IR-only orientation from chemical gradients. Protocols specify QCL/LED bands (2--25 µm), matched power deposition, and N≥50 per condition to separate electromagnetic detection from thermal artifacts. Implications: Applications of this work include biomimetic IR sensor design, better-controlled pest-monitoring experiments, and clearer tests of whether insect olfactory systems ever use wavelength-specific electromagnetic information. Keywords: insect olfaction, infrared detection, vibrational theory, electromagnetic sensing, sensilla morphology, cuticular hydrocarbons, atmospheric transmission, biomimetic sensors Reproducibility: Complete implementation with seven case studies in Appendices --- Associated artifacts GitHub release: v1.0.0 (https://github.com/docxology/cohereants/releases/tag/v1.0.0) PDF SHA-256: 36bd97a8ff522937390213a69abb40c8c87a06ab783457e62d7effef4762bac2
", "keywords": [ "insect olfaction", "infrared detection", "vibrational theory of olfaction", "semiochemicals", "sensilla morphology", "electromagnetic sensing", "active inference", "reproducible research" ], "doi": "10.5281/zenodo.20450880", "github_release_url": "https://github.com/docxology/cohereants/releases/tag/v1.0.0" }, "2026_SelfImprovementAgent": { "year": "2026", "topic": "SelfImprovementAgent", "name": "Self-Improvement Agent Harness: A Deterministic SIA Exemplar", "description": "This exemplar documents template_sia, a deterministic implementation of the Self-Improvement Agent (SIA) harness contract described in the Self-Improvement Agents specification (Hexo AI, 2026, arXiv:2605.27276). The default pipeline replays fixture-backed generations for the mini_classify task; opt-in live mode runs bounded target subprocesses and optional Ollama-backed meta/feedback steps.\n\nRun snapshot. Task mini_classify, run 1, 3 generation(s), live=false. Final accuracy=0.8333 over 6 held-out samples. Values are injected by scripts/z_generate_manuscript_variables.py after analysis.\n\nKeywords: self-improvement agents, benchmark harness, reproducible evaluation, agent loops\n\n---\nAssociated artifacts\nGitHub release: v0.1.2 (https://github.com/docxology/template_sia/releases/tag/v0.1.2)\nDOI: https://doi.org/10.5281/zenodo.20453879\nZenodo: https://zenodo.org/records/20453879\nPDF SHA-256: 6e6d19d04182628bb825471cf8094b5c32d2c491d2c646652ec7e2439ba80773", "authors": "Daniel Ari Friedman", "abstract": "This exemplar documents template_sia, a deterministic implementation of the Self-Improvement Agent (SIA) harness contract described in the Self-Improvement Agents specification (Hexo AI, 2026, arXiv:2605.27276). The default pipeline replays fixture-backed generations for the mini_classify task; opt-in live mode runs bounded target subprocesses and optional Ollama-backed meta/feedback steps.\n\nRun snapshot. Task mini_classify, run 1, 3 generation(s), live=false. Final accuracy=0.8333 over 6 held-out samples. Values are injected by scripts/z_generate_manuscript_variables.py after analysis.\n\nKeywords: self-improvement agents, benchmark harness, reproducible evaluation, agent loops\n\n---\nAssociated artifacts\nGitHub release: v0.1.2 (https://github.com/docxology/template_sia/releases/tag/v0.1.2)\nDOI: https://doi.org/10.5281/zenodo.20453879\nZenodo: https://zenodo.org/records/20453879\nPDF SHA-256: 6e6d19d04182628bb825471cf8094b5c32d2c491d2c646652ec7e2439ba80773", "keywords": [ "self-improvement agents", "benchmark harness", "reproducible research", "agent evaluation" ], "doi": "10.5281/zenodo.20453879", "github_release_url": "https://github.com/docxology/template_sia/releases/tag/v0.1.2" }, "2025_BiofirmDevelopmentWith": { "year": "2025", "topic": "BiofirmDevelopmentWith", "name": "Biofirm Development with First Principles First and the Active Inference Institute at the Applied Active Inference Symposium 2024", "description": "Multiple presentations given during the Active Inference Institute's 4th annual Applying Active Inference Symposium, 2024 over the course of November 13th-15th 2024 ", "authors": "John Clippinger, Andrew Pashea, Daniel Friedman", "abstract": "", "keywords": [], "doi": "10.5281/zenodo.14861596" }, "2025_ConCatEnate": { "year": "2025", "topic": "ConCatEnate", "name": "Con-cat-enate: emulation of Cat Hippocampus", "description": "Updated added a number of pages at the start (from page 2) that describes the entire system and whats missing apologies for not giving this overview of what would happen given those missing parts and the purpose of demo0.01 which substitute those parts to see whether the break down of the 10 miliscond to (longer period) with each handwritten sim, does stack chems as mentioned; and thus imply correct behavior some of the documents mentioning demo 0.01 and its motivation wasnt included (mentioned in the catpilot) therefore might cause confusion (ontop of also not really mentioning what would each of the missing part cause,) much apologies part 1 and part 2 updated of the emulated cat goes along with these documents: pilot: https://zenodo.org/records/14737043 beacons: https://zenodo.org/records/14737060 bevcyc: https://zenodo.org/records/14737076 ", "authors": "Andrew Djuwidja, Daniel Friedman", "abstract": "Updated added a number of pages at the start (from page 2) that describes the entire system and whats missing apologies for not giving this overview of what would happen given those missing parts and the purpose of demo0.01 which substitute those parts to see whether the break down of the 10 miliscond to (longer period) with each handwritten sim, does stack chems as mentioned; and thus imply correct behavior some of the documents mentioning demo 0.01 and its motivation wasnt included (mentioned in the catpilot) therefore might cause confusion (ontop of also not really mentioning what would each of the missing part cause,) much apologies part 1 and part 2 updated of the emulated cat goes along with these documents: pilot: https://zenodo.org/records/14737043 beacons: https://zenodo.org/records/14737060 bevcyc: https://zenodo.org/records/14737076 ", "keywords": [], "doi": "10.5281/zenodo.14738798", "domain": "Computational" }, "2025_Graphspeak": { "year": "2025", "topic": "Graphspeak", "name": "Graphspeak: of language and handshake", "description": "An experiment on decomposing language onto initially established graph-theory representation applied then onto some pre-selected transformations (commonly used context/tool related transmutes) (t1,t2,t3) then upon some layers of decomposition finding out the shared context / what we label as a handshake; for the purpose of education / others", "authors": "andrew djuwidja, Daniel Friedman", "abstract": "An experiment on decomposing language onto initially established graph-theory representation applied then onto some pre-selected transformations (commonly used context/tool related transmutes) (t1,t2,t3) then upon some layers of decomposition finding out the shared context / what we label as a handshake; for the purpose of education / others", "keywords": [], "doi": "10.5281/zenodo.14737157" }, "2025_BevCycPositPrimitive": { "year": "2025", "topic": "BevCycPositPrimitive", "name": "BevCyc - posit on primitive drivers of creatures", "description": "Posits a bev-cyc as primitive driver of creatures / to use in agentic system represents a cycle of homeostasis to accomplish by divisible action sequences; that are actually states that break down onto muscle % tension follows / posits to experiment / emulate ideas of morphogenesis / morphospace (dr levin's) on differing scale of time & checkpoints to run with a modulator layer ; to then apply in either a static | n n n n n n | autofilling nodes for inputs from hippocampus systems; or a decreasing one that emulates stage-of-state or the cycle involved", "authors": "andrew djuwidja, Daniel Friedman", "abstract": "BehavioralCycle / CYC / “ from epigenetic-sequence / epigenetic-cycle” transcribed onto ROS-like / gymnasium like Nodes-of-action-sequence; but in actual essence; is just an empty node-scaffold To fill with action-context-pairs that is designed so that they are decomposable Onto the lowest level (which is muscle tension %) “ BevCYC is a framework that implements a decomposable Chain of Node ( N – N – N ) Representing an Action Sequence / Concept Sequence That is based on the Epigenetic Cycle ideations.", "keywords": [], "doi": "10.5281/zenodo.14737076" }, "2025_BeaconsPreEmulation": { "year": "2025", "topic": "BeaconsPreEmulation", "name": "Beacons - pre emulation of social cortex", "description": "primitive emulation of social cortex currently only about tagging object types and labeled objects in simulations and having them refer to a table of category and response, which might include a sim of their own sensitive to cues such as mood or transferable cues of when the object type is called; then \"emulated\" in the mind a pulse-to-response only version of the associated objects for this purpose ( should update with more social cortex posits soon )", "authors": "andrew djuwidja, Daniel Friedman", "abstract": "primitive emulation of social cortex currently only about tagging object types and labeled objects in simulations and having them refer to a table of category and response, which might include a sim of their own sensitive to cues such as mood or transferable cues of when the object type is called; then \"emulated\" in the mind a pulse-to-response only version of the associated objects for this purpose ( should update with more social cortex posits soon )", "keywords": [], "doi": "10.5281/zenodo.14737060", "domain": "Computational" }, "2025_ConCatEnate2": { "year": "2025", "topic": "ConCatEnate2", "name": "Con-cat-enate: pilot overview", "description": "Short overview of the concatenate amateur cat Combined with Beacons Bevcyc and demo 0.01 plan", "authors": "andrew djuwidja, Daniel Friedman", "abstract": "Short overview of the concatenate amateur cat Combined with Beacons Bevcyc and demo 0.01 plan", "keywords": [], "doi": "10.5281/zenodo.14737043" }, "2024_PushPull": { "year": "2024", "topic": "PushPull", "name": "Push and Pull: A priming sequence", "description": "The primary objectives of this Push and Pull document are to explore the decoupling of cognitive ( covert , attentional) and bodily ( overt ) behaviors , and to provide a sequence of hands-on computer exercises for further exploration . The priming sequence of exercises is presented to inform an individual’s understanding of how their attention interacts with computer use movements, such as controlling the on-screen cursor with mouse, touchpad, or eye movements. What is presented here is only an initial sequence to complement other and future development of “hand-I” uncouplings, in principle and in practice.", "authors": "Daniel Ari Friedman", "abstract": "The primary objectives of this Push and Pull document are to explore the decoupling of cognitive ( covert , attentional) and bodily ( overt ) behaviors , and to provide a sequence of hands-on computer exercises for further exploration . The priming sequence of exercises is presented to inform an individual’s understanding of how their attention interacts with computer use movements, such as controlling the on-screen cursor with mouse, touchpad, or eye movements. What is presented here is only an initial sequence to complement other and future development of “hand-I” uncouplings, in principle and in practice.", "keywords": [], "doi": "10.5281/zenodo.10659375" }, "2023_NaturalAIBased": { "year": "2023", "topic": "NaturalAIBased", "name": "A Natural AI Based on The Science of Computational Physics, Biology and Neuroscience: Policy and Societal Significance", "description": "Letter on: \"A Natural AI Based on The Science of Computational Physics, Biology and Neuroscience: Policy and Societal Significance\". v1 released on December 12, 2023. ", "authors": "John Clippinger, Bert de Vries, Beth Noveck, Chris Fields, Cory Slater, Daniel Ari Friedman, David A. Silbersweig, Francesco Lapenta, Holly Grimm, Jeff Emmett, Joshua Shane, Karl Friston, Martin Nkafu Nkemnkia, Matthew Brown, Matthew Pirkowski, Michael Levin, Michael Zargham, Nguyen Anh Tuan, Krishnashree Achuthan, Thomas Patterson, Scott L. David, Thomas Kehler, Virginia Bleu Knight, Yasuhide Nakayama", "abstract": "Letter on: \"A Natural AI Based on The Science of Computational Physics, Biology and Neuroscience: Policy and Societal Significance\". v1 released on December 12, 2023. ", "keywords": [ "Active Inference", "AI", "Natural", "Policy" ], "doi": "10.5281/zenodo.10360148" }, "2023_ATLAS2": { "year": "2023", "topic": "ATLAS2", "name": "ATLAS: A Question Oriented Approach to the Use of Pattern Languages in Knowledge Management", "description": "The ATLAS system, evolving since the late 1990s, stands as a dynamic and comprehensive knowledge management tool that intends to address the complexities of modern information supply chains. The antecedent to ATLAS was the Atlas of Risk, an informal assemblage of various risks associated with digital interactions. Here we provide an initial specification for digital prototypes and paper-and-pencil implementations of a matured ATLAS architecture which integrates pattern language approaches with question-oriented procedures to manage and interpret meaning and context. The ATLAS system facilitates the management and communication of nuanced data sets and knowledge bases with an eye towards interoperability without the need for fully shared standards. The development of ATLAS, driven by the need for enhanced data interoperability and shared understanding in an increasingly complex and volatile digital landscape, reflects a profound, community response to the challenges of information environments and the fragility of extreme specialization. ATLAS's ongoing evolution showcases its adaptability and significance in the realms of data analysis, knowledge management, and cognitive security, and this first release of a technical specification establishes a foundation for a transition from prototype to scale-appropriate implementation.", "authors": "R.J. Cordes, Scott David, Daniel Friedman, Alexandra Mikhailova, Andrew Penland, Sam Young, Colten Zacharias", "abstract": "The ATLAS system, evolving since the late 1990s, stands as a dynamic and comprehensive knowledge management tool that intends to address the complexities of modern information supply chains. The antecedent to ATLAS was the Atlas of Risk, an informal assemblage of various risks associated with digital interactions. Here we provide an initial specification for digital prototypes and paper-and-pencil implementations of a matured ATLAS architecture which integrates pattern language approaches with question-oriented procedures to manage and interpret meaning and context. The ATLAS system facilitates the management and communication of nuanced data sets and knowledge bases with an eye towards interoperability without the need for fully shared standards. The development of ATLAS, driven by the need for enhanced data interoperability and shared understanding in an increasingly complex and volatile digital landscape, reflects a profound, community response to the challenges of information environments and the fragility of extreme specialization. ATLAS's ongoing evolution showcases its adaptability and significance in the realms of data analysis, knowledge management, and cognitive security, and this first release of a technical specification establishes a foundation for a transition from prototype to scale-appropriate implementation.", "keywords": [], "doi": "10.5281/zenodo.10362561" }, "2023_TranscriptActiveInference": { "year": "2023", "topic": "TranscriptActiveInference", "name": "Transcript of Active Inference GuestStream 049.1: \"Clickbait, consciousness science, and responsible journalism\"", "description": "Transcript from livestream on July 25, 2023 at the Active Inference Institute. YouTube watch link: https://www.youtube.com/watch?v=dUXfgzKHV1c Repository with updated transcripts and accessory files: https://github.com/ActiveInferenceInstitute/ActiveInferenceJournal/tree/main/GuestStream/GuestStream_049 Information on the Institute: https://www.activeinference.org/", "authors": "Megan A. K. Peters, Nora Bradford, Daniel Friedman", "abstract": "Transcript from livestream on July 25, 2023 at the Active Inference Institute. YouTube watch link: https://www.youtube.com/watch?v=dUXfgzKHV1c Repository with updated transcripts and accessory files: https://github.com/ActiveInferenceInstitute/ActiveInferenceJournal/tree/main/GuestStream/GuestStream_049 Information on the Institute: https://www.activeinference.org/", "keywords": [ "Consciousness", "Science", "Communication" ], "doi": "10.5281/zenodo.8229512" }, "2023_ModernNostrIndex": { "year": "2023", "topic": "ModernNostrIndex", "name": "Modern Nostr Index Card-based Knowledge Engineering", "description": "Some concepts explored related to Knowledge Engineering, Nostr, Large Language Models, Complexity, and more. ", "authors": "Andrew Claros, Daniel Friedman", "abstract": "Some concepts explored related to Knowledge Engineering, Nostr, Large Language Models, Complexity, and more. ", "keywords": [ "Nostr", "Complexity", "Large Language Model", "Knowledge Engineering" ], "doi": "10.5281/zenodo.8118156" }, "2023_SlidesIris": { "year": "2023", "topic": "SlidesIris", "name": "Slides for Iris", "description": "Some initial slides from today. For context see: @speakerjohnash", "authors": "Daniel Ari Friedman", "abstract": "", "keywords": [], "doi": "10.5281/zenodo.7838653" }, "2022_CanonicalNeuralNetworks": { "year": "2022", "topic": "CanonicalNeuralNetworks", "name": "Canonical neural networks perform active inference", "description": "Transcript of a three-session series of discussions of the paper "Canonical neural networks perform active inference" by Takuya Isomura, Hideaki Shimazaki & Karl J. Friston. https://www.nature.com/articles/s42003-021-02994-2 LS #051.0: Background and context. https://www.youtube.com/watch?v=ZASG-rtkXDk LS #051.1: First participatory group discussion. https://www.youtube.com/watch?v=IM_NlUzyq8M LS #051.2: Second participatory group discussion. https://www.youtube.com/watch?v=hY_CajLpt9Q", "authors": "Takuya Isomura, Daniel Friedman", "abstract": "Transcript of a three-session series of discussions of the paper "Canonical neural networks perform active inference" by Takuya Isomura, Hideaki Shimazaki & Karl J. Friston. https://www.nature.com/articles/s42003-021-02994-2 LS #051.0: Background and context. https://www.youtube.com/watch?v=ZASG-rtkXDk LS #051.1: First participatory group discussion. https://www.youtube.com/watch?v=IM_NlUzyq8M LS #051.2: Second participatory group discussion. https://www.youtube.com/watch?v=hY_CajLpt9Q", "keywords": [], "doi": "10.5281/zenodo.7400536" }, "2022_InteroceptionAsModeling": { "year": "2022", "topic": "InteroceptionAsModeling", "name": "Interoception as modeling, allostasis as control", "description": "Transcript of discussions of the 2022 paper “Interoception as modeling, allostasis as control” by Eli Sennesh, Jordan Theriault, Dana Brooks, Jan-Willemvan de Meent, Lisa Feldman Barrett, & Karen S. Quigley https://www.sciencedirect.com/science/article/abs/pii/S0301051121002350 Session 050.0, October 17, 2022 https://www.youtube.com/watch?v=l7r0ISlr-Hc Session 050.1, October 20, 2022 https://www.youtube.com/watch?v=tGd-mgSdbio Session 050.2, November 3, 2022 https://www.youtube.com/watch?v=4o-LmkycAC0 ", "authors": "Eli Sennesh, Jordan Theriault, Dave Douglass, Ian Tennant, Dean Tickles, Daniel Friedman", "abstract": "Transcript of discussions of the 2022 paper “Interoception as modeling, allostasis as control” by Eli Sennesh, Jordan Theriault, Dana Brooks, Jan-Willemvan de Meent, Lisa Feldman Barrett, & Karen S. Quigley https://www.sciencedirect.com/science/article/abs/pii/S0301051121002350 Session 050.0, October 17, 2022 https://www.youtube.com/watch?v=l7r0ISlr-Hc Session 050.1, October 20, 2022 https://www.youtube.com/watch?v=tGd-mgSdbio Session 050.2, November 3, 2022 https://www.youtube.com/watch?v=4o-LmkycAC0 ", "keywords": [], "doi": "10.5281/zenodo.7400709" }, "2022_Transcript": { "year": "2022", "topic": "Transcript", "name": "Transcript of: Mark Solms, \"Consciousness as Precision Optimization: Some Physiological and Philosophical Considerations\", ActInf GuestStream #016", "description": "This document is an enhanced transcript of the live presentations and group discussions with Mark Solms in 2022 at the Active Inference Institute. The focus is the 2018 paper "How and Why Consciousness Arises: Some Considerations from Physics and Physiology" by Mark Solms and Karl Friston.", "authors": "Mark Solms, David S Douglass, Stephen Sillett, Daniel Ari Friedman", "abstract": "This document is an enhanced transcript of the live presentations and group discussions with Mark Solms in 2022 at the Active Inference Institute. The focus is the 2018 paper "How and Why Consciousness Arises: Some Considerations from Physics and Physiology" by Mark Solms and Karl Friston.", "keywords": [ "Active Inference", "Consciousness", "Neuroanatomy", "Psychology", "Free Energy Principle", "Livestream" ], "doi": "10.5281/zenodo.7267947" }, "2022_WorkedExampleBayesian": { "year": "2022", "topic": "WorkedExampleBayesian", "name": "A Worked Example of the Bayesian Mechanics of Classical Objects", "description": "Transcripts of discussions of the 2022 preprint "A Worked Example of the Bayesian Mechanics of Classical Objects" by Dalton A R Sakthivadivel. https://arxiv.org/abs/2206.12996 Session 049.0, September 30, 2022 https://www.youtube.com/watch?v=OtX2Fpzn7KA Session 049.1, October 5, 2022 https://www.youtube.com/watch?v=dAtC-Enmc8M Session 049.2, October 12, 2022 https://www.youtube.com/watch?v=2SuBJBEg9LI ", "authors": "Dalton AR Sakthivadivel, Ali Rahmjoo, Jakub Smékal, Daniel Friedman", "abstract": "Transcripts of discussions of the 2022 preprint "A Worked Example of the Bayesian Mechanics of Classical Objects" by Dalton A R Sakthivadivel. https://arxiv.org/abs/2206.12996 Session 049.0, September 30, 2022 https://www.youtube.com/watch?v=OtX2Fpzn7KA Session 049.1, October 5, 2022 https://www.youtube.com/watch?v=dAtC-Enmc8M Session 049.2, October 12, 2022 https://www.youtube.com/watch?v=2SuBJBEg9LI ", "keywords": [], "doi": "10.5281/zenodo.7400786" }, "2022_TranscriptDiscussions": { "year": "2022", "topic": "TranscriptDiscussions", "name": "Transcript of discussions on: \"Communication as Socially Extended Active Inference: An Ecological Approach to Communicative Behavior\"", "description": "Discussion with an author of the 2021 paper “Communication as Socially Extended Active Inference: An Ecological Approach to Communicative Behavior” by Remi Tison & Pierre Poirier. https://www.tandfonline.com/doi/abs/10.1080/10407413.2021.1965480 Session #048.0, September 2, 2022 https://www.youtube.com/watch?v=zqiZjjY9H7M Session 048.1, September 7, 2022 https://www.youtube.com/watch?v=nWFM5zrdmG8 Session 048.1, September 7, 2022 https://www.youtube.com/watch?v=nWFM5zrdmG8", "authors": "Rémi Tison, Dean Tickles, Bleu Knight, Daniel Friedman", "abstract": "Discussion with an author of the 2021 paper “Communication as Socially Extended Active Inference: An Ecological Approach to Communicative Behavior” by Remi Tison & Pierre Poirier. https://www.tandfonline.com/doi/abs/10.1080/10407413.2021.1965480 Session #048.0, September 2, 2022 https://www.youtube.com/watch?v=zqiZjjY9H7M Session 048.1, September 7, 2022 https://www.youtube.com/watch?v=nWFM5zrdmG8 Session 048.1, September 7, 2022 https://www.youtube.com/watch?v=nWFM5zrdmG8", "keywords": [], "doi": "10.5281/zenodo.7401875" }, "2022_TrackingPublicSensemaking": { "year": "2022", "topic": "TrackingPublicSensemaking", "name": "Tracking Public Sensemaking through Rhetorical Annotation of Image Memes", "description": "Political polarization and declining trust in institutions are driving societal destabilization and radicalization. Recently there has been increased interest in online misinformation intervention and deterrence, for example through the use of machine learning on language use. We argue that addressing crises in the information environment will require a sharper situational awareness and a deeper understanding of how beliefs emerge and crystallize, as well as greater connectivity in the work of teams and organizations in order to reduce the effects of bias and partisanship in collection and analysis. Image memes play an increasingly important role in public sensemaking and discourse and the emergence of public beliefs. Despite their significance, image memes have proven to be a very difficult category of artifact to collect, classify, and analyze in aggregate. In this white paper, the function and form of image memes are discussed, the challenges of performing image meme collection and analysis within the context of emergent, interdisciplinary teams are detailed, and requirements and recommendations for alleviating these challenges are offered.", "authors": "Mridula Mascarenhas, RJ Cordes, Bleu Knight, Sarah Murphy, Daniel A. Friedman", "abstract": "Political polarization and declining trust in institutions are driving societal destabilization and radicalization. Recently there has been increased interest in online misinformation intervention and deterrence, for example through the use of machine learning on language use. We argue that addressing crises in the information environment will require a sharper situational awareness and a deeper understanding of how beliefs emerge and crystallize, as well as greater connectivity in the work of teams and organizations in order to reduce the effects of bias and partisanship in collection and analysis. Image memes play an increasingly important role in public sensemaking and discourse and the emergence of public beliefs. Despite their significance, image memes have proven to be a very difficult category of artifact to collect, classify, and analyze in aggregate. In this white paper, the function and form of image memes are discussed, the challenges of performing image meme collection and analysis within the context of emergent, interdisciplinary teams are detailed, and requirements and recommendations for alleviating these challenges are offered.", "keywords": [ "Sensemaking", "Memes", "Knowledge Management", "Narrative", "Rhetorical Analysis" ], "doi": "10.5281/zenodo.6904427" }, "2021_Transcript": { "year": "2021", "topic": "Transcript", "name": "Transcript of: Karl Friston, 1st Applied Active Inference Symposium, Active Inference Lab, June 21, 2021", "description": "On June 21st, 2021, Active Inference Lab ( activeinference.org/ ) hosted its first Applied Active Inference Symposium, featuring Professor Karl Friston. The Symposium was structured in three sections, corresponding to the Organizational Units of the Active Inference Lab: Education, Communication, and Tools. This publication reflects an edited and enriched transcript of the proceedings of the Symposium.", "authors": "Karl Friston, David Standish Douglass, Maria Luiza Iennaco de Vasconcelos, Stephen Sillett, Lorena Sganzerla, Dean Tickles, Ivan Metelkin, Alex Vyatkin, Daniel Ari Friedman", "abstract": "On June 21st, 2021, Active Inference Lab ( activeinference.org/ ) hosted its first Applied Active Inference Symposium, featuring Professor Karl Friston. The Symposium was structured in three sections, corresponding to the Organizational Units of the Active Inference Lab: Education, Communication, and Tools. This publication reflects an edited and enriched transcript of the proceedings of the Symposium.", "keywords": [ "Active Inference", "Free Energy Principle", "Symposium", "ActInfLab", "Bayesian Inference", "Education", "Communication", "Tools", "Generative Model" ], "doi": "10.5281/zenodo.5797072" }, "2021_CollaborativeWritingCatechism": { "year": "2021", "topic": "CollaborativeWritingCatechism", "name": "Collaborative Writing for Catechism-Based Teams", "description": "Asynchronous and remote collaborative written projects (research, field guides, code, etc.) in emergent, interdisciplinary teams can be an incredibly productive and enjoyable pursuit. The convergence of diverse perspectives, personalities, and expertise in the rapid production of written deliverables can provide immense value and insight, not just to the situation, problem, or opportunity space the team was formed to address, but also to the disciplines each author brought to the table. Yet, asynchronous and remote collaborative writing can also be rather perilous. Managing deadlines, handling disputes, communicating, staying on mission, managing resources, collective editing (and over-editing), and avoiding contradiction are only some of the challenges. While the presence of a catechism-styled operations order and the use of a team “Facilitator” can greatly improve the likelihood of success, making sure all of the authors have an alignment on protocol and etiquette while writing as a catechism-based team prevents unnecessary misunderstandings and keeps things on schedule. This "3-Paragraph Order" for Collaborative Writing, or C-3PO, is meant to rapidly onboard authors to collaborative writing procedures and etiquette. ", "authors": "Richard J. Cordes, Daniel Ari Friedman", "abstract": "Asynchronous and remote collaborative written projects (research, field guides, code, etc.) in emergent, interdisciplinary teams can be an incredibly productive and enjoyable pursuit. The convergence of diverse perspectives, personalities, and expertise in the rapid production of written deliverables can provide immense value and insight, not just to the situation, problem, or opportunity space the team was formed to address, but also to the disciplines each author brought to the table. Yet, asynchronous and remote collaborative writing can also be rather perilous. Managing deadlines, handling disputes, communicating, staying on mission, managing resources, collective editing (and over-editing), and avoiding contradiction are only some of the challenges. While the presence of a catechism-styled operations order and the use of a team “Facilitator” can greatly improve the likelihood of success, making sure all of the authors have an alignment on protocol and etiquette while writing as a catechism-based team prevents unnecessary misunderstandings and keeps things on schedule. This "3-Paragraph Order" for Collaborative Writing, or C-3PO, is meant to rapidly onboard authors to collaborative writing procedures and etiquette. ", "keywords": [ "Remote Teams", "Collaborative Writing", "Catechisms", "OPORDs" ], "doi": "10.5281/zenodo.4633921" }, "2020_ReimaginingMaps": { "year": "2020", "topic": "ReimaginingMaps", "name": "Reimagining Maps", "description": "Reimagining Maps was written after participation in a National Geospatial-Intelligence Agency Incubator hosted on Polyplexus. The field of cartography sits at the intersection of applied mathematics, engineering, geology, geography, user experience, and graphic design. Methodologies and concepts from cartography have been creatively applied in a variety of fields, such as the application of spatial mapping techniques to information in knowledge management, or the use of itinerary visualization methods in non-spatial journeys such as learning maps in learning management systems. These fields have been subjected to their own forms of development and evolution leading to new methodologies and concepts somewhat removed from their origins. Cartography itself has undergone a great deal of technology-driven development but would look very different today had it been developed as a new field through the creative application of methodologies and concepts from those it inspired. As the modern information and logistical context presents new challenges and thus new demands for maps, we propose a “reimagining of maps” through an interdisciplinary synthesis inspired by the interdisciplinary origins of maps themselves.", "authors": "Richard J. Cordes, Daniel Ari Friedman, Mikel Maron", "abstract": "Reimagining Maps was written after participation in a National Geospatial-Intelligence Agency Incubator hosted on Polyplexus. The field of cartography sits at the intersection of applied mathematics, engineering, geology, geography, user experience, and graphic design. Methodologies and concepts from cartography have been creatively applied in a variety of fields, such as the application of spatial mapping techniques to information in knowledge management, or the use of itinerary visualization methods in non-spatial journeys such as learning maps in learning management systems. These fields have been subjected to their own forms of development and evolution leading to new methodologies and concepts somewhat removed from their origins. Cartography itself has undergone a great deal of technology-driven development but would look very different today had it been developed as a new field through the creative application of methodologies and concepts from those it inspired. As the modern information and logistical context presents new challenges and thus new demands for maps, we propose a “reimagining of maps” through an interdisciplinary synthesis inspired by the interdisciplinary origins of maps themselves.", "keywords": [ "Maps", "Cartography", "Remote Teams", "Interdisciplinary Research", "Process Mapping", "Knowledge Management Systems", "Instantaneous Remote Teams", "Intelligence Production", "OSINT" ], "doi": "10.5281/zenodo.4170026" }, "2020_InfiniteGamesInfinite": { "year": "2020", "topic": "InfiniteGamesInfinite", "name": "Infinite Games for Infinite Teams", "description": "Infinite Games for Infinite Teams was published by and in response to the DARPA Polyplexus Citizen Incubator: “Inventing a Remote Culture to Deal with Pandemics”, and was done so with the intent of discussing the questions outlined below. How are global online narratives constructed and received in 2020? Why are the processes of narrative design and culture production so important for security and governance? What is possible now or soon that was not possible before? What approaches could catalyze assessment, design, and deployment of online narratives in real-time? Why is it so important to have meme-detection systems that are culturally-aware, interlingual, intermodal, and human-in-the-loop? What does it look like to take a Complex Adaptive Systems (CAS) approach to the neuromemetics of narrative co-construction and agenda-setting? How could a CAS approach be used to support specifically-defined cultural/institutional/national/global interests? How do we find, formalize, and quantify goals or outcomes within a CAS framework? How can we diagnose, perturb, and create narratives through gameplay? What might a “design science for memes” look like? How is this present work continuous with and contrasting with previous work in innovation, generative games, and LARPing? How can music, sound, art, and other techniques amplify narrative impact ?", "authors": "Daniel Friedman, RJ Cordes", "abstract": "Infinite Games for Infinite Teams was published by and in response to the DARPA Polyplexus Citizen Incubator: “Inventing a Remote Culture to Deal with Pandemics”, and was done so with the intent of discussing the questions outlined below. How are global online narratives constructed and received in 2020? Why are the processes of narrative design and culture production so important for security and governance? What is possible now or soon that was not possible before? What approaches could catalyze assessment, design, and deployment of online narratives in real-time? Why is it so important to have meme-detection systems that are culturally-aware, interlingual, intermodal, and human-in-the-loop? What does it look like to take a Complex Adaptive Systems (CAS) approach to the neuromemetics of narrative co-construction and agenda-setting? How could a CAS approach be used to support specifically-defined cultural/institutional/national/global interests? How do we find, formalize, and quantify goals or outcomes within a CAS framework? How can we diagnose, perturb, and create narratives through gameplay? What might a “design science for memes” look like? How is this present work continuous with and contrasting with previous work in innovation, generative games, and LARPing? How can music, sound, art, and other techniques amplify narrative impact ?", "keywords": [], "doi": "10.5281/zenodo.12601675" }, "2026_MusicNeverStopped": { "year": "2026", "topic": "MusicNeverStopped", "name": "The Music Never Stopped: A Grateful Data Compendium with a Category-Theoretic Interpretation", "description": "We present a modular, citation-bound data compendium for the Grateful Dead universe — shows, songs, performances, personnel timelines, venues, recordings, and reception — and a category-theoretic interpretation of the performance graph. The work is grounded in the archival reality that Grateful Dead history is both institutional and participatory: UCSC's Grateful Dead Archive and the Internet Archive collection preserve formal and community records , while taping and trading scholarship shows why setlists and recording metadata are cultural evidence, not merely fan trivia . The surrounding source dossier also binds the non-quantitative historical frame -- formation and Acid Test context, Wall of Sound engineering, live recording/liveness scholarship, Deadhead sociology, studio-era reception, and public recognition -- to checked sources rather than to folklore alone . The compendium integrates nine primary sources (Setlist.fm , The SetList Program , the Mark Leone CMU setlist archive , GDsets , gdshowsdb , the Internet Archive Live Music Archive , the Alex Allan / whitegum lyric finder , the official band site , and Wikipedia ) with four reference sources (Britannica , the lineup-changes guide , Dodd and Trist's The Complete Annotated Grateful Dead Lyrics , and the Grateful Stats front-end ) and secondary corpora and community discussions . Each source is parsed by an independently testable reference module written against the documented record shape; the committed compendium under `data/archival/` is the dataset reported here (3341 ingested shows (gdshowsdb + truckin gap-fill; community literature estimates ~2318 canonical concerts), 645 songs, 912 venues, 40757 performance rows). A runtime completeness audit and figure-validation gate certify referential integrity and non-degenerate outputs on every pipeline run. Integration is a deterministic, sort-keyed merge over canonical slugs; registered figures also emit CSV/JSON data tables, and a first-principles claim ledger classifies each major result by irreducible input, hard constraint, assumption, validation artifact, and interpretation limit. Exploratory repertoire/uncertainty panels are labelled as pattern-discovery rather than causal inference. We then exhibit four small but real categorical constructions, situated against transformational and categorical music-theory precedents : a poset category of dates, a discrete category of shows, a monotone cumulative setlist functor and lineup functor from dates into sets, and a span representation that takes each performance to be the apex of a span between its show and its song. Wide pullbacks over a fixed show recover the show's setlist; wide pullbacks over a fixed song recover the song's performance history. The active-band roster, by contrast, is a non-monotone presheaf on the date poset — a categorical formalization of the familiar fact that members come and go. The artefacts in this paper come from the committed archival snapshot; all source-ingestion modules are written against the real source shape so that `scripts/00_fetch_sources.py --online --write-archival` refreshes the full snapshot. --- Associated artifacts GitHub release: The Music Never Stopped: A Grateful Data Compendium (v1.0.0) (https://github.com/docxology/grateful_data/releases/tag/v1.0.0) PDF SHA-256: 2d42bfd04e0b32e4871d5bcbe2c65bcce58cca336da240af7c6fd03a6563a709
", "authors": "Daniel Ari Friedman", "abstract": "We present a modular, citation-bound data compendium for the Grateful Dead universe — shows, songs, performances, personnel timelines, venues, recordings, and reception — and a category-theoretic interpretation of the performance graph. The work is grounded in the archival reality that Grateful Dead history is both institutional and participatory: UCSC's Grateful Dead Archive and the Internet Archive collection preserve formal and community records , while taping and trading scholarship shows why setlists and recording metadata are cultural evidence, not merely fan trivia . The surrounding source dossier also binds the non-quantitative historical frame -- formation and Acid Test context, Wall of Sound engineering, live recording/liveness scholarship, Deadhead sociology, studio-era reception, and public recognition -- to checked sources rather than to folklore alone . The compendium integrates nine primary sources (Setlist.fm , The SetList Program , the Mark Leone CMU setlist archive , GDsets , gdshowsdb , the Internet Archive Live Music Archive , the Alex Allan / whitegum lyric finder , the official band site , and Wikipedia ) with four reference sources (Britannica , the lineup-changes guide , Dodd and Trist's The Complete Annotated Grateful Dead Lyrics , and the Grateful Stats front-end ) and secondary corpora and community discussions . Each source is parsed by an independently testable reference module written against the documented record shape; the committed compendium under `data/archival/` is the dataset reported here (3341 ingested shows (gdshowsdb + truckin gap-fill; community literature estimates ~2318 canonical concerts), 645 songs, 912 venues, 40757 performance rows). A runtime completeness audit and figure-validation gate certify referential integrity and non-degenerate outputs on every pipeline run. Integration is a deterministic, sort-keyed merge over canonical slugs; registered figures also emit CSV/JSON data tables, and a first-principles claim ledger classifies each major result by irreducible input, hard constraint, assumption, validation artifact, and interpretation limit. Exploratory repertoire/uncertainty panels are labelled as pattern-discovery rather than causal inference. We then exhibit four small but real categorical constructions, situated against transformational and categorical music-theory precedents : a poset category of dates, a discrete category of shows, a monotone cumulative setlist functor and lineup functor from dates into sets, and a span representation that takes each performance to be the apex of a span between its show and its song. Wide pullbacks over a fixed show recover the show's setlist; wide pullbacks over a fixed song recover the song's performance history. The active-band roster, by contrast, is a non-monotone presheaf on the date poset — a categorical formalization of the familiar fact that members come and go. The artefacts in this paper come from the committed archival snapshot; all source-ingestion modules are written against the real source shape so that `scripts/00_fetch_sources.py --online --write-archival` refreshes the full snapshot. --- Associated artifacts GitHub release: The Music Never Stopped: A Grateful Data Compendium (v1.0.0) (https://github.com/docxology/grateful_data/releases/tag/v1.0.0) PDF SHA-256: 2d42bfd04e0b32e4871d5bcbe2c65bcce58cca336da240af7c6fd03a6563a709
", "keywords": [ "grateful dead", "setlist data", "category theory", "music information retrieval", "reproducible data compendium" ], "doi": "10.5281/zenodo.20482025", "github_release_url": "https://github.com/docxology/grateful_data/releases/tag/v1.0.0" }, "2026_DeterministicTestbedSelf": { "year": "2026", "topic": "DeterministicTestbedSelf", "name": "A Deterministic Testbed for Self-Organizing Agent-Team Coordination", "description": "Recent work on AutoScientists coordinates self-organizing teams of language-model agents through a small set of shared mechanisms: a champion-and-experiment-log shared state, a registry of retired dead-end directions, effect-size ranking of candidate directions, noise-band confirmation of claimed improvements, and stagnation-driven reorganization of teams. This exemplar provides a deterministic, standalone reference implementation of those mechanisms and studies them honestly as a testbed rather than as a performance claim.\n\nWe make the comparison fair by holding the total number of objective evaluations fixed: coordinated teams partition a single sequential experiment budget rather than adding parallel compute. Under that matched budget, coordination cannot — and in our results does not — beat a single-thread baseline on the final champion metric; we report the actual numbers and claim no speedup. What the testbed does demonstrate are two distinct, independently measurable benefits. First, noise-robustness: because the objective is stochastic, a single observed gain can be a draw of evaluation noise, so we separate the reported champion metric from the clean noise-free ground truth and show that noise-band confirmation shrinks the gap between them by roughly an order of magnitude — with confirmation on, the final champion's reported metric sits $0.0012$ above its clean value, against $0.0156$ with confirmation removed, while every configuration reaches the same clean optimum. Second, search hygiene: the dead-end registry, consulted by the proposer, cuts redundant re-probes of retired directions from $36$ to $0$ and halts at $36$ of the $60$ experiments — the same clean answer, reached with less waste. A per-mechanism ablation isolates each component's contribution, and the language-model proposer is a clean plug-in seam: a deterministic rule-based agent drives the reproducible figures, and a live Hermes agent (served by Ollama) can be swapped in without touching the coordination loop.\n\n---\nAssociated artifacts\nGitHub release: v1.0.2 (https://github.com/docxology/template_autoscientists/releases/tag/v1.0.2)\nDOI: https://doi.org/10.5281/zenodo.20533669\nZenodo: https://zenodo.org/records/20533669\nPDF SHA-256: 0af391375b14eb397812a8050657e2980fbc3a768e6fb108aa2f7eff46773e16", "authors": "Daniel Ari Friedman", "abstract": "Recent work on AutoScientists coordinates self-organizing teams of language-model agents through a small set of shared mechanisms: a champion-and-experiment-log shared state, a registry of retired dead-end directions, effect-size ranking of candidate directions, noise-band confirmation of claimed improvements, and stagnation-driven reorganization of teams. This exemplar provides a deterministic, standalone reference implementation of those mechanisms and studies them honestly as a testbed rather than as a performance claim.\n\nWe make the comparison fair by holding the total number of objective evaluations fixed: coordinated teams partition a single sequential experiment budget rather than adding parallel compute. Under that matched budget, coordination cannot — and in our results does not — beat a single-thread baseline on the final champion metric; we report the actual numbers and claim no speedup. What the testbed does demonstrate are two distinct, independently measurable benefits. First, noise-robustness: because the objective is stochastic, a single observed gain can be a draw of evaluation noise, so we separate the reported champion metric from the clean noise-free ground truth and show that noise-band confirmation shrinks the gap between them by roughly an order of magnitude — with confirmation on, the final champion's reported metric sits $0.0012$ above its clean value, against $0.0156$ with confirmation removed, while every configuration reaches the same clean optimum. Second, search hygiene: the dead-end registry, consulted by the proposer, cuts redundant re-probes of retired directions from $36$ to $0$ and halts at $36$ of the $60$ experiments — the same clean answer, reached with less waste. A per-mechanism ablation isolates each component's contribution, and the language-model proposer is a clean plug-in seam: a deterministic rule-based agent drives the reproducible figures, and a live Hermes agent (served by Ollama) can be swapped in without touching the coordination loop.\n\n---\nAssociated artifacts\nGitHub release: v1.0.2 (https://github.com/docxology/template_autoscientists/releases/tag/v1.0.2)\nDOI: https://doi.org/10.5281/zenodo.20533669\nZenodo: https://zenodo.org/records/20533669\nPDF SHA-256: 0af391375b14eb397812a8050657e2980fbc3a768e6fb108aa2f7eff46773e16", "keywords": [ "agent coordination", "scientific discovery", "noise-band confirmation", "ablation study", "reproducible research", "language-model agents" ], "doi": "10.5281/zenodo.20533669", "github_release_url": "https://github.com/docxology/template_autoscientists/releases/tag/v1.0.2" }, "2026_RecoveringLLMPersona": { "year": "2026", "topic": "RecoveringLLMPersona", "name": "Recovering LLM-Persona Accuracies from Unlabeled Votes", "description": "Algebraic (NTQR) evaluation infers how accurate a group of noisy classifiers was on a finite test using only their responses — no answer key. We test this end to end on real large language models. Three trader \"personas\" (optimistic, neutral, pessimistic), instantiated as system prompts, each make a binary bullish/bearish call on the same 64 market scenarios; we run the identical trio through six locally-hosted models via Ollama. For each model we recover per-persona, per-label accuracy with ErrorIndependentEvaluation (unsupervised) and score it against the authored ground truth (supervised), which is used only as a check. On the five models whose three judges all varied (mistral:latest, gemma4:latest, gemma3:4b, gemma2:2b, granite4.1:3b), the unsupervised algebra recovered persona accuracies to a mean absolute error of 0.012, within the 0.102 sampling-noise floor across all six per-label accuracy terms, with no labels -- including a persona's genuinely poor bullish accuracy of 0.57, recovered as 0.59. The other model collapsed at least one persona into a constant classifier (a judge that voted one way on all 64 scenarios), which makes the error-independent algebra unsolvable. The central, non-obvious result: inter-judge disagreement does not imply evaluability. Aggregate disagreement separated this run only because the unevaluable model(s) collapsed to 0.00; the five evaluable models spanned 0.03–0.23. What gates evaluation is a per-judge condition — every judge must vary (and answer) — not an ensemble one. We formalize this as a label-free evaluability diagnostic (a judge whose modal-vote fraction reaches 1.0 is a constant classifier; an unparseable vote is an abstention) that predicted exactly which models would be evaluable, before any solve and without ground truth. This is a concrete instance of the safety property the NTQR logic promises: it warns you when an ensemble is not good enough to be evaluated. A scenario bootstrap puts a 95% CI of [0.000, 0.038] on the recovery MAE (well inside the 0.102 noise floor), and a deterministic synthetic study generalizes the recovery beyond the finite set of real evaluable models — error falls like 1/√Q (slope -0.58, stable across ensembles) — while mapping two honest limits: the built-in failure alarm catches anti-correlated judges with no false positives yet can miss positively-correlated (shared-training) errors, and the two-solution tie-break inverts once judges are no longer clearly better than random — exactly where simple majority-voting evaluation, though biased, is the more robust fallback. --- Associated artifacts GitHub release: v1.0.0 (https://github.com/docxology/ntqr_llm/releases/tag/v1.0.0) DOI: https://doi.org/10.5281/zenodo.20498699 Zenodo: https://zenodo.org/records/20498699 PDF SHA-256: e1196698427f9fe04d1f3071705adb6e5459983649c78d7f5d074756e989148b
", "authors": "Daniel Ari Friedman", "abstract": "Algebraic (NTQR) evaluation infers how accurate a group of noisy classifiers was on a finite test using only their responses — no answer key. We test this end to end on real large language models. Three trader \"personas\" (optimistic, neutral, pessimistic), instantiated as system prompts, each make a binary bullish/bearish call on the same 64 market scenarios; we run the identical trio through six locally-hosted models via Ollama. For each model we recover per-persona, per-label accuracy with ErrorIndependentEvaluation (unsupervised) and score it against the authored ground truth (supervised), which is used only as a check. On the five models whose three judges all varied (mistral:latest, gemma4:latest, gemma3:4b, gemma2:2b, granite4.1:3b), the unsupervised algebra recovered persona accuracies to a mean absolute error of 0.012, within the 0.102 sampling-noise floor across all six per-label accuracy terms, with no labels -- including a persona's genuinely poor bullish accuracy of 0.57, recovered as 0.59. The other model collapsed at least one persona into a constant classifier (a judge that voted one way on all 64 scenarios), which makes the error-independent algebra unsolvable. The central, non-obvious result: inter-judge disagreement does not imply evaluability. Aggregate disagreement separated this run only because the unevaluable model(s) collapsed to 0.00; the five evaluable models spanned 0.03–0.23. What gates evaluation is a per-judge condition — every judge must vary (and answer) — not an ensemble one. We formalize this as a label-free evaluability diagnostic (a judge whose modal-vote fraction reaches 1.0 is a constant classifier; an unparseable vote is an abstention) that predicted exactly which models would be evaluable, before any solve and without ground truth. This is a concrete instance of the safety property the NTQR logic promises: it warns you when an ensemble is not good enough to be evaluated. A scenario bootstrap puts a 95% CI of [0.000, 0.038] on the recovery MAE (well inside the 0.102 noise floor), and a deterministic synthetic study generalizes the recovery beyond the finite set of real evaluable models — error falls like 1/√Q (slope -0.58, stable across ensembles) — while mapping two honest limits: the built-in failure alarm catches anti-correlated judges with no false positives yet can miss positively-correlated (shared-training) errors, and the two-solution tie-break inverts once judges are no longer clearly better than random — exactly where simple majority-voting evaluation, though biased, is the more robust fallback. --- Associated artifacts GitHub release: v1.0.0 (https://github.com/docxology/ntqr_llm/releases/tag/v1.0.0) DOI: https://doi.org/10.5281/zenodo.20498699 Zenodo: https://zenodo.org/records/20498699 PDF SHA-256: e1196698427f9fe04d1f3071705adb6e5459983649c78d7f5d074756e989148b
", "keywords": [ "algebraic evaluation", "NTQR", "unsupervised evaluation", "evaluation on unlabeled data", "LLM-as-judge", "error-independent evaluation", "ensemble evaluability", "constant classifier", "AI safety warning light", "reproducible research", "answer-key-free recovery", "local large language models" ], "doi": "10.5281/zenodo.20498699", "github_release_url": "https://github.com/docxology/ntqr_llm/releases/tag/v1.0.0" }, "2026_Triplicate": { "year": "2026", "topic": "Triplicate", "name": "The Triplicate: A Data-Driven Large-Format Newspaper Layout Engine", "description": "We present template_newspaper, a pure-Python engine that renders a complete\ntwelve-page, large-format newspaper to a print-ready PDF from structured YAML\ncontent. The exemplar edition is The Triplicate, a homage to the historic\nnewspaper of Crescent City, California (founded 1879). The engine demonstrates\ndesigned multi-column page layout — nameplate and ears, spanning headlines,\nflowing column frames with an optional rail, drop caps, pull quotes, ruled\nmodular boxes, data tables, halftone \"engraving\" figures and running folios —\nwhile keeping a strict separation between content (data) and engine (code):\na new title is a data edit, never a code change. The layout strategy is a hybrid\nin which fixed furniture is drawn directly on the canvas to establish where\nthe column grid begins, after which body copy flows through ReportLab frames\nthat split paragraphs across columns automatically. The project obeys the\nresearch-template monorepo contract and is discovered and executed by the same\norchestration pipeline as its code- and prose-focused siblings.\n\n---\nAssociated artifacts\nGitHub release: v1.0.2 (https://github.com/docxology/template_newspaper/releases/tag/v1.0.2)\nDOI: https://doi.org/10.5281/zenodo.20533675\nZenodo: https://zenodo.org/records/20533675\nPDF SHA-256: 6c693dbd80a4234d30d99f6a890191ff47faf197a0636753fa9785588c550a77", "authors": "Daniel Ari Friedman", "abstract": "We present template_newspaper, a pure-Python engine that renders a complete\ntwelve-page, large-format newspaper to a print-ready PDF from structured YAML\ncontent. The exemplar edition is The Triplicate, a homage to the historic\nnewspaper of Crescent City, California (founded 1879). The engine demonstrates\ndesigned multi-column page layout — nameplate and ears, spanning headlines,\nflowing column frames with an optional rail, drop caps, pull quotes, ruled\nmodular boxes, data tables, halftone \"engraving\" figures and running folios —\nwhile keeping a strict separation between content (data) and engine (code):\na new title is a data edit, never a code change. The layout strategy is a hybrid\nin which fixed furniture is drawn directly on the canvas to establish where\nthe column grid begins, after which body copy flows through ReportLab frames\nthat split paragraphs across columns automatically. The project obeys the\nresearch-template monorepo contract and is discovered and executed by the same\norchestration pipeline as its code- and prose-focused siblings.\n\n---\nAssociated artifacts\nGitHub release: v1.0.2 (https://github.com/docxology/template_newspaper/releases/tag/v1.0.2)\nDOI: https://doi.org/10.5281/zenodo.20533675\nZenodo: https://zenodo.org/records/20533675\nPDF SHA-256: 6c693dbd80a4234d30d99f6a890191ff47faf197a0636753fa9785588c550a77", "keywords": [ "newspaper layout", "typography", "reportlab", "reproducible publishing", "document engineering" ], "doi": "10.5281/zenodo.20533675", "github_release_url": "https://github.com/docxology/template_newspaper/releases/tag/v1.0.2" }, "2026_TemplateTextbook": { "year": "2026", "topic": "TemplateTextbook", "name": "The Template Textbook", "description": "A modular, fillable scaffold for book-length technical works: a data-driven manuscript (parts/chapters/labs/questions), a tested computational backbone, deterministic figures and Mermaid diagrams, and a content scaffold/validation engine.\n\n---\nAssociated artifacts\nGitHub release: v0.1.2 (https://github.com/docxology/template_textbook/releases/tag/v0.1.2)\nDOI: https://doi.org/10.5281/zenodo.20533125\nZenodo: https://zenodo.org/records/20533125\nPDF SHA-256: 7b67cb2d9118f0a72001395f0a94c529313804d7e5cdf7f6ca2dc9f7ab57d168", "authors": "Daniel Ari Friedman", "abstract": "A modular, fillable scaffold for book-length technical works: a data-driven manuscript (parts/chapters/labs/questions), a tested computational backbone, deterministic figures and Mermaid diagrams, and a content scaffold/validation engine.\n\n---\nAssociated artifacts\nGitHub release: v0.1.2 (https://github.com/docxology/template_textbook/releases/tag/v0.1.2)\nDOI: https://doi.org/10.5281/zenodo.20533125\nZenodo: https://zenodo.org/records/20533125\nPDF SHA-256: 7b67cb2d9118f0a72001395f0a94c529313804d7e5cdf7f6ca2dc9f7ab57d168", "keywords": [], "doi": "10.5281/zenodo.20533125", "github_release_url": "https://github.com/docxology/template_textbook/releases/tag/v0.1.2" }, "2026_ITrace": { "year": "2026", "topic": "ITrace", "name": "iTrace: verification-first webcam eye-movement analysis", "description": "iTrace is an MIT-licensed Python toolkit for webcam-derived gaze, saccade, pupil, and quality diagnostics. Version 0.4.1 is a diagnostic v1 release: the pure NumPy/SciPy core is algorithmically verified against synthetic and closed-loop oracles, the optional webcam shell exports derived records, and the empirical ledger covers a single-participant, single-device, five-session diagnostic pilot. This release does not claim reference-device accuracy, cross-device generality, population generality, or webcam-accuracy validation.", "authors": "Daniel Ari Friedman", "abstract": "iTrace is an MIT-licensed Python toolkit for webcam-derived gaze, saccade, pupil, and quality diagnostics. Version 0.4.1 is a diagnostic v1 release: the pure NumPy/SciPy core is algorithmically verified against synthetic and closed-loop oracles, the optional webcam shell exports derived records, and the empirical ledger covers a single-participant, single-device, five-session diagnostic pilot. This release does not claim reference-device accuracy, cross-device generality, population generality, or webcam-accuracy validation.", "keywords": [ "eye-tracking", "webcam", "gaze", "saccades", "pupillometry", "open-source", "diagnostic-pilot" ], "doi": "10.5281/zenodo.20614909", "github_release_url": "https://github.com/docxology/itrace/releases/tag/v0.4.1" }, "2026_DemoCreate": { "year": "2026", "topic": "DemoCreate", "name": "DemoCreate: Declarative Audio-Visual Demo Generation for Software", "description": "DemoCreate generates audio-visual demos of software — codebase tours, website walkthroughs, and terminal/CLI demos — from a single declarative, deterministic spine. A Demo is an ordered action stream plus narration chunks, merging CodeVideo's event-sourced virtual-IDE model with VSpeak's chunk/trigger model. Every heavy backend (TTS via Kokoro/Chatterbox, transcription via Whisper, capture via mss/Playwright, animation via Manim, assembly via MoviePy/ffmpeg) sits behind an abstract interface with a pure-Python deterministic default, so the package produces a real demo with only light dependencies and upgrades when extras are installed. On-screen actions are anchored to spoken trigger words via TTS→STT synchronization: narration audio is generated, transcribed back to word-level timestamps, and used to align the action stream with the narration.
", "authors": "Daniel Ari Friedman", "abstract": "DemoCreate generates audio-visual demos of software — codebase tours, website walkthroughs, and terminal/CLI demos — from a single declarative, deterministic spine. A Demo is an ordered action stream plus narration chunks, merging CodeVideo's event-sourced virtual-IDE model with VSpeak's chunk/trigger model. Every heavy backend (TTS via Kokoro/Chatterbox, transcription via Whisper, capture via mss/Playwright, animation via Manim, assembly via MoviePy/ffmpeg) sits behind an abstract interface with a pure-Python deterministic default, so the package produces a real demo with only light dependencies and upgrades when extras are installed. On-screen actions are anchored to spoken trigger words via TTS→STT synchronization: narration audio is generated, transcribed back to word-level timestamps, and used to align the action stream with the narration.
", "keywords": [ "demo-generation", "screencast", "text-to-speech", "code-walkthrough", "video", "narration", "whisper", "manim", "playwright", "reproducible", "deterministic", "tts-stt-synchronization", "virtual-ide" ], "doi": "10.5281/zenodo.20693216", "github_release_url": "https://github.com/docxology/democreate/releases/tag/v0.6.2" }, "2026_ENTO": { "year": "2026", "topic": "ENTO", "name": "ENTO: an ENcrypted, Typed, Omnitrack container format for multimodal research data", "description": "ENTO (ENcrypted, Typed, Omnitrack) is a flat ZIP container format and reference implementation for bundling heterogeneous research artifacts — time series, genomics slices, spectrograms, provenance proofs — into a single verifiable file. Each track is sealed under per-track AES-256-GCM authenticated encryption with format+track associated-data binding and PADMÉ length padding. The default wire format is 0.4.0; formats 0.2.0, 0.3.0, and 0.3.1 remain read/write compatibility profiles. Graded observability levels control how much manifest metadata a recipient sees, and an optional hash-chained proof export provides tamper-evident lineage. Verification deliberately separates key-authenticated integrity from keyless corruption detection.
This deposit is the 0.4 manuscript release candidate together with the MIT-licensed reference implementation source. Planned code home: https://github.com/docxology/entofile.
", "authors": "Daniel Ari Friedman", "abstract": "ENTO (ENcrypted, Typed, Omnitrack) is a flat ZIP container format and reference implementation for bundling heterogeneous research artifacts — time series, genomics slices, spectrograms, provenance proofs — into a single verifiable file. Each track is sealed under per-track AES-256-GCM authenticated encryption with format+track associated-data binding and PADMÉ length padding. The default wire format is 0.4.0; formats 0.2.0, 0.3.0, and 0.3.1 remain read/write compatibility profiles. Graded observability levels control how much manifest metadata a recipient sees, and an optional hash-chained proof export provides tamper-evident lineage. Verification deliberately separates key-authenticated integrity from keyless corruption detection.
This deposit is the 0.4 manuscript release candidate together with the MIT-licensed reference implementation source. Planned code home: https://github.com/docxology/entofile.
", "keywords": [ "research data formats", "authenticated encryption", "AES-256-GCM", "reproducible research", "multimodal containers" ], "doi": "10.5281/zenodo.20396328", "github_release_url": "https://github.com/docxology/entofile/releases/tag/v0.4" }, "2026_GeneralizedNotationNotationGNN": { "year": "2026", "topic": "GeneralizedNotationNotationGNN", "name": "GeneralizedNotationNotation (GNN)", "description": "Generalized Notation Notation (GNN) is a text-based language designed to standardize the representation and communication of Active Inference generative models. It aims to enhance clarity, reproducibility, and interoperability in the field of Active Inference and cognitive modeling. GNN provides a structured way to describe complex models, making them human-readable and machine-parsable. It supports a \"Triple Play\" approach: text-based models, graphical model visualizations, and a blueprint for executable cognitive models.", "authors": "Daniel Ari Friedman", "abstract": "Active Inference offers a unifying account of perception, learning, and action under the free energy principle, yet the generative models at its core are still communicated ad hoc: scattered across prose descriptions, bespoke notebooks, and framework-specific code that rarely agree. This fragmentation makes published models hard to reproduce, compare, or port between tools, and it raises the barrier for newcomers learning the formalism. GeneralizedNotationNotation (GNN) addresses this gap with a standardized, human- and machine-readable text language for specifying Active Inference generative models, paired with a 25-step processing pipeline that transforms a single specification into validation, visualization, simulation, and analysis artifacts. A GNN file denotes a generative model, and the language lets that model take the several shapes the field actually uses: the notation blocks a file declares — categorical A/B/C/D[/E] tensors, linear-Gaussian system matrices F/H/Q/R with a Gaussian prior, per-agent and per-level compositions — fix its model kind, which the pipeline classifies structurally and carries through rendering and execution, recording explicitly which registered backends can render and run a model of each kind and which report it unsupported. GNN's \"Triple Play\" treats each model as three coordinated views — a textual specification, graphical visualizations, and executable cognitive models — so that one source yields consistent outputs across modalities. The framework spans 32 exemplar specifications (27 discrete-state and 5 continuous linear-Gaussian) across 9 model families and 10 registered rendering backends, with explicit gates for cross-format semantic fidelity and cross-framework reliability. GNN 3.0.0 layered safe-by-design long-running orchestration on top of this language and pipeline — durable observation streams, resumable run sessions, and auditable container plans that generate, validate, and replay data only, with no live infrastructure mutation. We describe the language, the model kinds it denotes, the pipeline architecture, and the validation that confirms specifications round-trip faithfully across formats and execute across multiple simulation backends — with coverage gaps recorded as explicit profiled-unsupported statuses rather than silently omitted — establishing GNN as reproducible, interoperable infrastructure for communicating Active Inference models.", "keywords": [ "active inference", "generative models", "cognitive modeling", "notation system", "reproducibility", "computational neuroscience", "bayesian inference", "standards", "gnn", "python" ], "doi": "10.5281/zenodo.7803313" }, "2026_COGANT": { "year": "2026", "topic": "COGANT", "name": "COGANT: Deterministic Codebase-to-GNN Translation", "description": "COGANT (Codebase-to-GNN Translation) deterministically converts software repositories into structured Active Inference artifacts expressed in the Active Inference Institute's Generalized Notation Notation (GNN). It is an evidence compiler: it propagates reviewable program facts through a finite fixpoint rule pipeline and emits graph, matrix, provenance, visualization, and round-trip artifacts with confidence and provenance, rather than a single opaque embedding. A reverse synthesizer reconstructs a runnable Python package from an emitted GNN bundle, closing a forward-reverse-forward evaluation loop. This is the first public release (v0.6.0). Source: https://github.com/ActiveInferenceInstitute/COGANT", "authors": "Daniel Ari Friedman", "abstract": "COGANT (Codebase-to-GNN Translation) deterministically converts software repositories into structured Active Inference artifacts expressed in the Active Inference Institute's Generalized Notation Notation (GNN). It is an evidence compiler: it propagates reviewable program facts through a finite fixpoint rule pipeline and emits graph, matrix, provenance, visualization, and round-trip artifacts with confidence and provenance, rather than a single opaque embedding. A reverse synthesizer reconstructs a runnable Python package from an emitted GNN bundle, closing a forward-reverse-forward evaluation loop. This is the first public release (v0.6.0). Source: https://github.com/ActiveInferenceInstitute/COGANT", "keywords": [ "program analysis", "Generalized Notation Notation", "GNN", "intermediate representation", "code property graph", "active inference", "reproducible research", "codebase-to-model translation", "cognitive ecosystem modeling" ], "doi": "10.5281/zenodo.20705350", "github_release_url": "https://github.com/ActiveInferenceInstitute/COGANT/releases/tag/v0.6.0" }, "2026_AGEINT": { "year": "2026", "topic": "AGEINT", "name": "AGEINT: Agentic Intelligence", "description": "Synthetic Analytic Tradecraft (AGEINT, or Agentic Intelligence), is a local curriculum-and-assurance atlas for teaching bounded AI-agent support inside intelligence education by making the machinery of Synthetic Analytic Tradecraft visible on the page. It converts SIST Guide TOC and Bibliography into 16 parts, 51 modules, 9 methods appendices, 20 named AGEINT patterns, and 312 parsed source-guide references without renumbering inherited source identities, then asks the reader to inspect the same things an instructor or assurance reviewer would inspect: the part map that frames the domain, the module overview that names the learner task, the source spine that separates official, standards, scholarly, public-domain, practitioner, vendor, and provenance-only support while separating source quality from narrative fluency, the evidence packet that a student would retain, the reviewer challenge that should break a weak claim, and the validators that prevent a polished artifact from masquerading as a verified one. A learner does not enter through a slogan. The learner opens a module, sees which authority and source keys are allowed to carry the claim, reads a textbook primer that distinguishes observation from inference, confidence from probability, and source quality from fluency, works through a synthetic practice studio using classroom records, public declassified examples, owned-lab logs, toy datasets, rendered figures, or tabletop incidents, and then produces a bounded artifact with caveats, assumptions, alternatives, excluded actions, human review, rollback evidence, and refresh triggers written into the record before reuse. The method contract in shows that flow as a claim-to-evidence pathway rather than as an aspirational ethics statement: authority must be named before the task begins, source support must be traceable before a claim is promoted, unsafe action must be replaced by safe substitution, and agentic assistance must remain a drafting, retrieval, comparison, simulation, critique, or audit aid whose output is never treated as self-authenticating . AGEINT remains synthetic in its fixtures, not in its standards: the word synthetic is therefore a control, not a downgrade. AGEINT keeps the fixture safe while keeping the standard difficult: HUMINT, SIGINT, OSINT, GEOINT and IMINT, FININT, counterintelligence, cyber threat intelligence, cognitive security, agent orchestration, active inference, source verification, public-sector governance, privacy review, and industrial-control-system defense can be discussed because the student is not handed live targets, evasion recipes, exploit instructions, manipulation playbooks, covert-action procedures, or unsafe cyber-physical steps. When a source topic could invite misuse, the module turns the risky motif into a provenance card, detection-coverage note, rights-impact worksheet, model or dataset card, source-refresh memo, release gate, risk-exception log, remediation backlog, or debrief protocol, and the exercise fails if it cannot show where authority, data boundary, tool permission, uncertainty, and review enter the workflow. The source layer is intentionally conservative: Perplexity and similar discovery tools may suggest candidates, but the manuscript cites verified official, standards, public-domain, or scholarly anchors encoded in the source corpus and rebuilt into BibTeX; Practitioner, vendor, and blog sources inherited through the source guide, plus social or other guide-inherited rows, can preserve provenance context without becoming foundational support for governance, rights, safety, empirical, statistical, or performance claims unless a stronger verified source bears the specific point. The same source posture is visible in the counts: 10 source-quality anchors cover the governing standards and assurance spine, while 462 curated intelligence research anchors span the domain lanes that route claim-bearing prose to direct evidence rather than decorative citations. A strong AGEINT artifact is therefore not an uninspected essay. It is an evidence packet that names the question, allowed inputs, excluded actions, source keys, claim type, caveats, competing explanations, confidence basis, prompt or run card, tool allowlist, data boundary, stop condition, reviewer challenge, safety boundary, refresh trigger, and human disposition. It also contains negative controls: stale citations that should be caught, weak-source-only claims that should be downgraded, figure positions or colors that should not be mistaken for quantitative evidence, overbroad statistical language that should fail, and operational substitutions that should halt until an instructor or reviewer approves a safer artifact. A reviewer can trace a finished packet in both directions: from a polished sentence back to the claim class, source family, cited anchor, caveat, and source-refresh duty, or from a source row forward to the modules, figures, tables, and exercises that rely on it. A student can see why a governance claim needs law, policy, standard, or official guidance support; why a cyber or industrial-control-system scenario must remain defensive and synthetic; why a social or cognitive-security lesson must become resilience education rather than persuasion practice; why a vendor or practitioner note can motivate a question but cannot by itself carry a public-rights or safety assertion; why a figure caption must say whether arrows are explanatory or quantitative; and why a model-generated draft is only useful after it has been constrained by authority, reviewed by a person, and attached to evidence that another person can contest. The early orientation pages, part maps, chapter landmarks, appendices, bibliography atlas, source-lane map, and generated reports are designed to make that trace visible instead of leaving it to instructor memory. They show the learner what to keep: the source keys behind each claim, the reason a safer substitute was chosen, the assumption that would change the answer, the dissent that should not be smoothed away, the review note that records who accepted residual risk, the rollback path if a source or permission changes, and the refresh trigger that turns a current claim back into a review item. They also show what the curriculum refuses to keep: unverifiable authority theater, live-target instructions, metric-looking diagrams without units or denominators, statistical decorations without empirical design, citation clusters that do not bear the sentence they decorate, and agent outputs whose provenance cannot be reconstructed. The technical build is part of that scholarship. Curriculum shards generate semantic manuscript files; cross-references use label-backed section and figure links, and display equations are typeset from source for reproducible math; citations route through Pandoc keys; figures are registry-backed PNG assets with captions, alt text, long descriptions, provenance, hashes, and visual-semantics metadata; source anchors carry explicit lane and tier fields; claim calibration audits high-risk empirical, statistical, governance, safety, visualization, artifact-readiness, and formalism language; scholarship and source-metadata reports expose review warnings instead of hiding them; PDF audits check stale output, URI targets, file actions, and banned filler language. The analysis-validation matrix in names which claim classes require empirical evidence, direct source-family support, negative controls, figure semantics, or rendered-artifact checks before they can be treated as ready, and that matrix is a boundary on what the manuscript can honestly say. AGEINT is not a benchmark and does not claim to measure AGEINT performance, model capability, analyst replacement, learning outcomes, operational effectiveness, public-sector impact, statistical significance, or safety performance; page counts, citation counts, figure counts, validator passes, and link audits are artifact telemetry, not empirical outcome evidence. Its strongest claim is methodological and inspectable: agentic assistance can be taught inside intelligence education when every reuse path is forced through authority, source support, safe substitution, evidence packet, negative controls, human review, rollback, and refresh triggers, and when the safest failure mode is to stop, document the unresolved condition, and ask for review. The resulting work should be read as a Synthetic Analytic Tradecraft atlas, not as a public-release certificate or a disguised operational manual: the reader can see the source key behind a sentence, the caveat behind a confidence statement, the assumption behind a scenario, the blocked use behind a tempting automation, the challenge behind a polished answer, the caption that limits a visual, the validator that rejects an overclaim, and the refresh duty that keeps current-source prose from becoming stale authority.
", "authors": "Daniel Ari Friedman", "abstract": "Synthetic Analytic Tradecraft (AGEINT, or Agentic Intelligence), is a local curriculum-and-assurance atlas for teaching bounded AI-agent support inside intelligence education by making the machinery of Synthetic Analytic Tradecraft visible on the page. It converts SIST Guide TOC and Bibliography into 16 parts, 51 modules, 9 methods appendices, 20 named AGEINT patterns, and 312 parsed source-guide references without renumbering inherited source identities, then asks the reader to inspect the same things an instructor or assurance reviewer would inspect: the part map that frames the domain, the module overview that names the learner task, the source spine that separates official, standards, scholarly, public-domain, practitioner, vendor, and provenance-only support while separating source quality from narrative fluency, the evidence packet that a student would retain, the reviewer challenge that should break a weak claim, and the validators that prevent a polished artifact from masquerading as a verified one. A learner does not enter through a slogan. The learner opens a module, sees which authority and source keys are allowed to carry the claim, reads a textbook primer that distinguishes observation from inference, confidence from probability, and source quality from fluency, works through a synthetic practice studio using classroom records, public declassified examples, owned-lab logs, toy datasets, rendered figures, or tabletop incidents, and then produces a bounded artifact with caveats, assumptions, alternatives, excluded actions, human review, rollback evidence, and refresh triggers written into the record before reuse. The method contract in shows that flow as a claim-to-evidence pathway rather than as an aspirational ethics statement: authority must be named before the task begins, source support must be traceable before a claim is promoted, unsafe action must be replaced by safe substitution, and agentic assistance must remain a drafting, retrieval, comparison, simulation, critique, or audit aid whose output is never treated as self-authenticating . AGEINT remains synthetic in its fixtures, not in its standards: the word synthetic is therefore a control, not a downgrade. AGEINT keeps the fixture safe while keeping the standard difficult: HUMINT, SIGINT, OSINT, GEOINT and IMINT, FININT, counterintelligence, cyber threat intelligence, cognitive security, agent orchestration, active inference, source verification, public-sector governance, privacy review, and industrial-control-system defense can be discussed because the student is not handed live targets, evasion recipes, exploit instructions, manipulation playbooks, covert-action procedures, or unsafe cyber-physical steps. When a source topic could invite misuse, the module turns the risky motif into a provenance card, detection-coverage note, rights-impact worksheet, model or dataset card, source-refresh memo, release gate, risk-exception log, remediation backlog, or debrief protocol, and the exercise fails if it cannot show where authority, data boundary, tool permission, uncertainty, and review enter the workflow. The source layer is intentionally conservative: Perplexity and similar discovery tools may suggest candidates, but the manuscript cites verified official, standards, public-domain, or scholarly anchors encoded in the source corpus and rebuilt into BibTeX; Practitioner, vendor, and blog sources inherited through the source guide, plus social or other guide-inherited rows, can preserve provenance context without becoming foundational support for governance, rights, safety, empirical, statistical, or performance claims unless a stronger verified source bears the specific point. The same source posture is visible in the counts: 10 source-quality anchors cover the governing standards and assurance spine, while 462 curated intelligence research anchors span the domain lanes that route claim-bearing prose to direct evidence rather than decorative citations. A strong AGEINT artifact is therefore not an uninspected essay. It is an evidence packet that names the question, allowed inputs, excluded actions, source keys, claim type, caveats, competing explanations, confidence basis, prompt or run card, tool allowlist, data boundary, stop condition, reviewer challenge, safety boundary, refresh trigger, and human disposition. It also contains negative controls: stale citations that should be caught, weak-source-only claims that should be downgraded, figure positions or colors that should not be mistaken for quantitative evidence, overbroad statistical language that should fail, and operational substitutions that should halt until an instructor or reviewer approves a safer artifact. A reviewer can trace a finished packet in both directions: from a polished sentence back to the claim class, source family, cited anchor, caveat, and source-refresh duty, or from a source row forward to the modules, figures, tables, and exercises that rely on it. A student can see why a governance claim needs law, policy, standard, or official guidance support; why a cyber or industrial-control-system scenario must remain defensive and synthetic; why a social or cognitive-security lesson must become resilience education rather than persuasion practice; why a vendor or practitioner note can motivate a question but cannot by itself carry a public-rights or safety assertion; why a figure caption must say whether arrows are explanatory or quantitative; and why a model-generated draft is only useful after it has been constrained by authority, reviewed by a person, and attached to evidence that another person can contest. The early orientation pages, part maps, chapter landmarks, appendices, bibliography atlas, source-lane map, and generated reports are designed to make that trace visible instead of leaving it to instructor memory. They show the learner what to keep: the source keys behind each claim, the reason a safer substitute was chosen, the assumption that would change the answer, the dissent that should not be smoothed away, the review note that records who accepted residual risk, the rollback path if a source or permission changes, and the refresh trigger that turns a current claim back into a review item. They also show what the curriculum refuses to keep: unverifiable authority theater, live-target instructions, metric-looking diagrams without units or denominators, statistical decorations without empirical design, citation clusters that do not bear the sentence they decorate, and agent outputs whose provenance cannot be reconstructed. The technical build is part of that scholarship. Curriculum shards generate semantic manuscript files; cross-references use label-backed section and figure links, and display equations are typeset from source for reproducible math; citations route through Pandoc keys; figures are registry-backed PNG assets with captions, alt text, long descriptions, provenance, hashes, and visual-semantics metadata; source anchors carry explicit lane and tier fields; claim calibration audits high-risk empirical, statistical, governance, safety, visualization, artifact-readiness, and formalism language; scholarship and source-metadata reports expose review warnings instead of hiding them; PDF audits check stale output, URI targets, file actions, and banned filler language. The analysis-validation matrix in names which claim classes require empirical evidence, direct source-family support, negative controls, figure semantics, or rendered-artifact checks before they can be treated as ready, and that matrix is a boundary on what the manuscript can honestly say. AGEINT is not a benchmark and does not claim to measure AGEINT performance, model capability, analyst replacement, learning outcomes, operational effectiveness, public-sector impact, statistical significance, or safety performance; page counts, citation counts, figure counts, validator passes, and link audits are artifact telemetry, not empirical outcome evidence. Its strongest claim is methodological and inspectable: agentic assistance can be taught inside intelligence education when every reuse path is forced through authority, source support, safe substitution, evidence packet, negative controls, human review, rollback, and refresh triggers, and when the safest failure mode is to stop, document the unresolved condition, and ask for review. The resulting work should be read as a Synthetic Analytic Tradecraft atlas, not as a public-release certificate or a disguised operational manual: the reader can see the source key behind a sentence, the caveat behind a confidence statement, the assumption behind a scenario, the blocked use behind a tempting automation, the challenge behind a polished answer, the caption that limits a visual, the validator that rejects an overclaim, and the refresh duty that keeps current-source prose from becoming stale authority.
", "keywords": [ "agentic intelligence", "AGEINT", "AI agents", "intelligence tradecraft", "cognitive security", "structured analytic techniques", "active inference", "model context protocol", "multi-agent systems", "operational governance" ], "doi": "10.5281/zenodo.20732274", "github_release_url": "https://github.com/docxology/AGEINT/releases/tag/v0.1.0" }, "2026_PolicyDistillationAs": { "year": "2026", "topic": "PolicyDistillationAs", "name": "On-Policy Distillation as Active Inference in Finite Variational Models", "description": "This paper formulates on-policy distillation as active inference in finite variational models, with exact claims only for declared objects and interpretive claims explicitly bounded outside them. In the construction, the intractable teacher policy plays the role of the generative model $p(o,s)$, the tractable student policy is the approximate posterior $q(s)$, and the per-token reverse-KL distillation loss is variational free energy up to the evidence constant, $F = D_{\\mathrm{KL}}(q\\,\\|\\,p(s\\mid o)) - \\log p(o)$, whose KL target is the teacher-induced posterior $p(s\\mid o)\\propto p(o,s)$ . The title's \"as\" is therefore a scoped mathematical correspondence rather than the slogan OPD = Active Inference. Variational free energy names the realized-rollout distillation loss; expected free energy remains the planning-side objective by which the pymdp agent selects actions . On-policy student rollouts generate the observations on which the posterior is scored, connecting the construction to induced-distribution mismatch in imitation learning and exposure-bias analyses while preserving their different objectives, empirical regimes, and contested severity . Privileged traces and feedback play the role that train-time-only information plays in the LUPI/distillation lineage . Four deterministic witnesses instantiate the correspondence. A Bernoulli-Ising oracle couples a teacher's privileged variable to the answer through $\\lambda$, making $I(\\lambda)$ the teacher-student mutual information and the finite free-energy gap the toy distillation objective; the closed-form and independently recomputed mutual-information sweeps agree to machine precision (RMSE 2.1e-16 nats). A pymdp T-maze rollout supplies the on-policy student that samples its own observations under a privileged cue . A two-agent classroom pits a privileged teacher (cue validity 0.98) against an on-policy student (cue validity 0.5), measuring teacher belief entropy 0.247 nats versus student 0.347 nats and a mean reverse-KL distillation signal of 6.28 nats. A four-state/two-action sequential-shift witness shows teacher-forced train loss 0.333 nats underestimating student-induced test loss 0.409 nats, with deterministic on-policy correction reducing it to 0.096 nats. These are toy, generated findings, not production-LLM measurements. Recent privileged-context, context-distillation, adaptive-teacher, freshness-aware OPD, RLHF/instruction-tuning, self-generated reasoning, Qwen OPD-vs-RL, and Thinking Machines replication reports remain external context rather than reproduced results . The supplemental sheaf/provenance layer keeps that boundary operational: every reported number is hydrated from a generated artifact, every figure is source-bound, and 16 / 16 invariant checks pass before rendering. --- Associated artifacts GitHub release: v1.0.2 (https://github.com/ActiveInferenceInstitute/on_policy_distillation/releases/tag/v1.0.2) DOI: https://doi.org/10.5281/zenodo.20749817 Zenodo: https://zenodo.org/records/20749817 PDF SHA-256: c6b5ec494915e6e046f24cf723f8dbbf93a5b168544daed3cca14c089d4087aa
", "authors": "Daniel Ari Friedman", "abstract": "This paper formulates on-policy distillation as active inference in finite variational models, with exact claims only for declared objects and interpretive claims explicitly bounded outside them. In the construction, the intractable teacher policy plays the role of the generative model $p(o,s)$, the tractable student policy is the approximate posterior $q(s)$, and the per-token reverse-KL distillation loss is variational free energy up to the evidence constant, $F = D_{\\mathrm{KL}}(q\\,\\|\\,p(s\\mid o)) - \\log p(o)$, whose KL target is the teacher-induced posterior $p(s\\mid o)\\propto p(o,s)$ . The title's \"as\" is therefore a scoped mathematical correspondence rather than the slogan OPD = Active Inference. Variational free energy names the realized-rollout distillation loss; expected free energy remains the planning-side objective by which the pymdp agent selects actions . On-policy student rollouts generate the observations on which the posterior is scored, connecting the construction to induced-distribution mismatch in imitation learning and exposure-bias analyses while preserving their different objectives, empirical regimes, and contested severity . Privileged traces and feedback play the role that train-time-only information plays in the LUPI/distillation lineage . Four deterministic witnesses instantiate the correspondence. A Bernoulli-Ising oracle couples a teacher's privileged variable to the answer through $\\lambda$, making $I(\\lambda)$ the teacher-student mutual information and the finite free-energy gap the toy distillation objective; the closed-form and independently recomputed mutual-information sweeps agree to machine precision (RMSE 2.1e-16 nats). A pymdp T-maze rollout supplies the on-policy student that samples its own observations under a privileged cue . A two-agent classroom pits a privileged teacher (cue validity 0.98) against an on-policy student (cue validity 0.5), measuring teacher belief entropy 0.247 nats versus student 0.347 nats and a mean reverse-KL distillation signal of 6.28 nats. A four-state/two-action sequential-shift witness shows teacher-forced train loss 0.333 nats underestimating student-induced test loss 0.409 nats, with deterministic on-policy correction reducing it to 0.096 nats. These are toy, generated findings, not production-LLM measurements. Recent privileged-context, context-distillation, adaptive-teacher, freshness-aware OPD, RLHF/instruction-tuning, self-generated reasoning, Qwen OPD-vs-RL, and Thinking Machines replication reports remain external context rather than reproduced results . The supplemental sheaf/provenance layer keeps that boundary operational: every reported number is hydrated from a generated artifact, every figure is source-bound, and 16 / 16 invariant checks pass before rendering. --- Associated artifacts GitHub release: v1.0.2 (https://github.com/ActiveInferenceInstitute/on_policy_distillation/releases/tag/v1.0.2) DOI: https://doi.org/10.5281/zenodo.20749817 Zenodo: https://zenodo.org/records/20749817 PDF SHA-256: c6b5ec494915e6e046f24cf723f8dbbf93a5b168544daed3cca14c089d4087aa
", "keywords": [ "on-policy distillation", "active inference", "self-distillation", "privileged information", "free energy principle", "reverse KL divergence", "pymdp", "sophisticated inference" ], "doi": "10.5281/zenodo.20747834", "github_release_url": "https://github.com/ActiveInferenceInstitute/on_policy_distillation/releases/tag/v1.0.2" }, "2026_CogSecSkills": { "year": "2026", "topic": "CogSecSkills", "name": "CogSecSkills: Multiharness Cognitive Security Skill Library", "description": "CogSecSkills is a defensive, harness-neutral agent-interface library that turns the human doctrine of cognitive security and analytic tradecraft into dependable, inspectable, agent-usable skills, distributed as an open repository from github.com/docxology/CogSecSkills; the motivation is an information environment in which mis-, dis-, and malinformation are analytically distinct but operationally entangled, false content can diffuse rapidly at platform scale, and credible source evaluation increasingly demands explicit expert practice rather than page-bound reading alone, so that agents asked to weigh competing hypotheses, trace provenance, or critically review a claim need a repeatable procedure and a stable tool-use contract rather than improvisation. The live generated catalogue reports one hundred implemented skills across seven taxonomy groups — Structured Analytic Techniques, Cognitive Security, Critical Review and Assurance, OSINT and Source Integrity, Counterintelligence and Deception Detection, Information Environment and Influence Analysis, and Research and Synthesis Methods — and the library is organized as a Plan, Build, and Teach system in which a registry declares the catalogue, a definitions layer owns the substance and quality controls of each skill, a skills tree exposes the harness-facing build, and a vendored educational upstream named AGEINT explains why each technique exists and how to use it responsibly. The central design choice is to make the reusable skill contract smaller than any one agent interface yet stricter than a prompt collection: each skill is owned by a single canonical definition that declares triggers, inputs, outputs, per-skill reference metadata, group-aware quality controls, and a closed vocabulary of neutral tool verbs before any harness-specific adapter is considered, and that definition is rendered deterministically into a harness-neutral specification, a human-readable skill description, an executable workflow, and one adapter per configured harness whose default members are Claude, Codex, and Hermes, so that portability becomes a property the test suite proves rather than a hope, and installation is concrete rather than interpretive — clone the public repository, install or run the Python package, run validation, point the agent harness at a chosen skill, execute its workflow, and bind runtime tools through the named harness adapter, regenerating adapters from a configuration file for any non-default harness. The quality discipline is the core of the contribution: an automated audit checks that every skill's defensive boundaries, misuse redirects, evidence requirements, confidence rubrics, privacy and legal constraints, uncertainty handling, failure modes, and negative controls are not merely present but specific to that skill and not reused across the corpus, while an evidence ladder adds curated safe-use and unsafe-redirect scenarios with expected defensive response-shape contracts, reviewed expected answers, and one source-owned worked example per skill, and a generated quality dashboard, supplemental catalogue, metadata matrix, data exports, and a family of deterministic figures — taxonomy counts, the hundred-skill atlas, tool-verb coverage, AGEINT topic crosswalks, the Plan-Build-Teach flow, reference density, harness coverage, and a cover-page installation route — are all produced directly from the live registry and skill specifications so that every visual stays synchronized with the source tree. The evidence boundary is deliberately and explicitly repository-local and reproducible within the checked-out project state, following reproducible-computing, open-data stewardship, and software-citation norms for explicit workflows, version specificity, and citable artifacts; every claim is backed by source files, canonical definitions, generated supplements and figures, the generated dashboard, and project-local verification commands together with a focused test suite and a manuscript renderer, and the work positions CogSecSkills as a validated interface between reasoning, tool use, and defensive output discipline rather than as a claim that any particular model runtime behaves correctly in the field, so its figures, supplements, scenarios, worked examples, and dashboard should be read as synchronized views of the current library state and never as independent measurements of operational performance.\n\n---\nAssociated artifacts\nGitHub release: v1.0.0 (https://github.com/docxology/CogSecSkills/releases/tag/v1.0.0)\nDOI: https://doi.org/10.5281/zenodo.20804585\nZenodo: https://zenodo.org/records/20804585\nPDF SHA-256: 1a99a2e474b07d21593094b7a3b4fd923197904056189a53b206126506b1040d", "authors": "Daniel Ari Friedman", "abstract": "CogSecSkills is a defensive, harness-neutral agent-interface library that turns the human doctrine of cognitive security and analytic tradecraft into dependable, inspectable, agent-usable skills, distributed as an open repository from github.com/docxology/CogSecSkills; the motivation is an information environment in which mis-, dis-, and malinformation are analytically distinct but operationally entangled, false content can diffuse rapidly at platform scale, and credible source evaluation increasingly demands explicit expert practice rather than page-bound reading alone, so that agents asked to weigh competing hypotheses, trace provenance, or critically review a claim need a repeatable procedure and a stable tool-use contract rather than improvisation. The live generated catalogue reports one hundred implemented skills across seven taxonomy groups — Structured Analytic Techniques, Cognitive Security, Critical Review and Assurance, OSINT and Source Integrity, Counterintelligence and Deception Detection, Information Environment and Influence Analysis, and Research and Synthesis Methods — and the library is organized as a Plan, Build, and Teach system in which a registry declares the catalogue, a definitions layer owns the substance and quality controls of each skill, a skills tree exposes the harness-facing build, and a vendored educational upstream named AGEINT explains why each technique exists and how to use it responsibly. The central design choice is to make the reusable skill contract smaller than any one agent interface yet stricter than a prompt collection: each skill is owned by a single canonical definition that declares triggers, inputs, outputs, per-skill reference metadata, group-aware quality controls, and a closed vocabulary of neutral tool verbs before any harness-specific adapter is considered, and that definition is rendered deterministically into a harness-neutral specification, a human-readable skill description, an executable workflow, and one adapter per configured harness whose default members are Claude, Codex, and Hermes, so that portability becomes a property the test suite proves rather than a hope, and installation is concrete rather than interpretive — clone the public repository, install or run the Python package, run validation, point the agent harness at a chosen skill, execute its workflow, and bind runtime tools through the named harness adapter, regenerating adapters from a configuration file for any non-default harness. The quality discipline is the core of the contribution: an automated audit checks that every skill's defensive boundaries, misuse redirects, evidence requirements, confidence rubrics, privacy and legal constraints, uncertainty handling, failure modes, and negative controls are not merely present but specific to that skill and not reused across the corpus, while an evidence ladder adds curated safe-use and unsafe-redirect scenarios with expected defensive response-shape contracts, reviewed expected answers, and one source-owned worked example per skill, and a generated quality dashboard, supplemental catalogue, metadata matrix, data exports, and a family of deterministic figures — taxonomy counts, the hundred-skill atlas, tool-verb coverage, AGEINT topic crosswalks, the Plan-Build-Teach flow, reference density, harness coverage, and a cover-page installation route — are all produced directly from the live registry and skill specifications so that every visual stays synchronized with the source tree. The evidence boundary is deliberately and explicitly repository-local and reproducible within the checked-out project state, following reproducible-computing, open-data stewardship, and software-citation norms for explicit workflows, version specificity, and citable artifacts; every claim is backed by source files, canonical definitions, generated supplements and figures, the generated dashboard, and project-local verification commands together with a focused test suite and a manuscript renderer, and the work positions CogSecSkills as a validated interface between reasoning, tool use, and defensive output discipline rather than as a claim that any particular model runtime behaves correctly in the field, so its figures, supplements, scenarios, worked examples, and dashboard should be read as synchronized views of the current library state and never as independent measurements of operational performance.\n\n---\nAssociated artifacts\nGitHub release: v1.0.0 (https://github.com/docxology/CogSecSkills/releases/tag/v1.0.0)\nDOI: https://doi.org/10.5281/zenodo.20804585\nZenodo: https://zenodo.org/records/20804585\nPDF SHA-256: 1a99a2e474b07d21593094b7a3b4fd923197904056189a53b206126506b1040d", "keywords": [ "cognitive security", "agent skills", "analytic tradecraft", "structured analytic techniques", "multiharness" ], "doi": "10.5281/zenodo.21513316", "github_release_url": "https://github.com/docxology/CogSecSkills/releases/tag/v1.0.0", "doi_url": "https://doi.org/10.5281/zenodo.21513316", "zenodo_record": "https://zenodo.org/records/21513316", "record_id": "21520558", "title": "CogSecSkills: Multiharness Agentic Skills for Cognitive Security" }, "2026_TemplateMadlib": { "year": "2026", "topic": "TemplateMadlib", "name": "Template Madlib: Deterministic Token Injection for Conditional IMRAD Manuscripts", "description": "This exemplar asks whether a reviewable pipeline can hydrate a complete IMRAD manuscript from configuration-owned lexical data while preserving an audit trail that remains readable before and after rendering. The project deliberately keeps playful Mad Lib mechanics inside a serious reproducibility contract: the manuscript shell names large placeholders, the config declares allowable language, and the source code decides what text is emitted.\n\nThe committed seed is 431 and the current schema expands 22 slot rule(s) into 40 token choice(s) across 10 lexicon categories. The configured narrative moves are state the manuscript-generation problem, name the deterministic intervention, and summarize the audit surface. The central hypothesis is: Deterministic lexical injection can generate a complete conditional IMRAD manuscript while preserving token provenance, section intent, and audit-ready method evidence.\n\nThe result is not a claim that lexical substitution creates scholarship. It is a worked template for conditional manuscript assembly: section enablement, token provenance, figure registration, and unresolved-placeholder checks all become inspectable artifacts before the shared renderer produces PDF, HTML, and slides.\n\n---\nAssociated artifacts\nGitHub release: v0.1.1 (https://github.com/docxology/template_madlib/releases/tag/v0.1.1)\nDOI: https://doi.org/10.5281/zenodo.20786638\nZenodo: https://zenodo.org/records/20786638\nPDF SHA-256: d9248f4f372fc3baf21cbf5cc5cdb7daffe0e22e62ac6fa2e3a697a26f3308b6", "authors": "Daniel Ari Friedman", "abstract": "This exemplar asks whether a reviewable pipeline can hydrate a complete IMRAD manuscript from configuration-owned lexical data while preserving an audit trail that remains readable before and after rendering. The project deliberately keeps playful Mad Lib mechanics inside a serious reproducibility contract: the manuscript shell names large placeholders, the config declares allowable language, and the source code decides what text is emitted.\n\nThe committed seed is 431 and the current schema expands 22 slot rule(s) into 40 token choice(s) across 10 lexicon categories. The configured narrative moves are state the manuscript-generation problem, name the deterministic intervention, and summarize the audit surface. The central hypothesis is: Deterministic lexical injection can generate a complete conditional IMRAD manuscript while preserving token provenance, section intent, and audit-ready method evidence.\n\nThe result is not a claim that lexical substitution creates scholarship. It is a worked template for conditional manuscript assembly: section enablement, token provenance, figure registration, and unresolved-placeholder checks all become inspectable artifacts before the shared renderer produces PDF, HTML, and slides.\n\n---\nAssociated artifacts\nGitHub release: v0.1.1 (https://github.com/docxology/template_madlib/releases/tag/v0.1.1)\nDOI: https://doi.org/10.5281/zenodo.20786638\nZenodo: https://zenodo.org/records/20786638\nPDF SHA-256: d9248f4f372fc3baf21cbf5cc5cdb7daffe0e22e62ac6fa2e3a697a26f3308b6", "keywords": [ "madlib generation", "token injection", "conditional manuscripts", "reproducible research", "IMRAD" ], "doi": "10.5281/zenodo.20786638", "github_release_url": "https://github.com/docxology/template_madlib/releases/tag/v0.1.1" }, "2026_RealizingEmptiness": { "year": "2026", "topic": "RealizingEmptiness", "name": "Realizing Emptiness: Operational Surrogates for No-Self-Evidence, QRF Opacification, and Bayesian Model Reduction", "description": "There is no statute, treatise, or law-school casebook titled \"Entomological Law.\" The phrase names a synthetic field — the convergence zone where the six-legged world repeatedly forces the legal system to answer questions it was not designed for: Can a fly testify? Who owns a swarm? Is a bumblebee a fish? Can you patent a mosquito? May a court excommunicate a weevil? Does a cricket suffer? May insects be used in war? This reference compiles that field as a machine-readable and reproducible artifact organized around the legal role an insect occupies in a given dispute. It encodes 8 legal roles — witness, regulated threat, protected subject, property, invention, defendant, moral patient, and weapon — and binds to them 18 landmark decisions, 43 statutes and treaties across 9 categories and 10 jurisdictions, 24 insect taxa, 13 certifying and regulatory institutions, 44 historical milestones spanning 3676 years, and 5 cross-domain themes that knit the roles together. Every count in this prose is generated from the source registries under src/, legal propositions are source-bound in the bibliography, every externally-sourced statistic written as a numeral is bound to a verification record in the claim ledger, and every figure caption is emitted from a source-owned caption registry. The result is both a map of a genuinely transdisciplinary field and a reproducibility contract: the same version-controlled inputs regenerate the inventories, validation report, analytical figures, manuscript variables, and paper while preserving explicit caveats about registry scope, jurisdictional reach, and the boundary between what the offline gates prove and what only a live source check can confirm. --- Associated artifacts GitHub release: Entomological Law (v1.0.0) (https://github.com/docxology/EntoLaw/releases/tag/v1.0.0) DOI: https://doi.org/10.5281/zenodo.21137276 Zenodo: https://zenodo.org/records/21137276 PDF SHA-256: 5a1891704c285491c97297dea66e9a4ea019b2dabddcc28cb5aeb18d6a335225
", "authors": "Daniel Ari Friedman", "abstract": "There is no statute, treatise, or law-school casebook titled \"Entomological Law.\" The phrase names a synthetic field — the convergence zone where the six-legged world repeatedly forces the legal system to answer questions it was not designed for: Can a fly testify? Who owns a swarm? Is a bumblebee a fish? Can you patent a mosquito? May a court excommunicate a weevil? Does a cricket suffer? May insects be used in war? This reference compiles that field as a machine-readable and reproducible artifact organized around the legal role an insect occupies in a given dispute. It encodes 8 legal roles — witness, regulated threat, protected subject, property, invention, defendant, moral patient, and weapon — and binds to them 18 landmark decisions, 43 statutes and treaties across 9 categories and 10 jurisdictions, 24 insect taxa, 13 certifying and regulatory institutions, 44 historical milestones spanning 3676 years, and 5 cross-domain themes that knit the roles together. Every count in this prose is generated from the source registries under src/, legal propositions are source-bound in the bibliography, every externally-sourced statistic written as a numeral is bound to a verification record in the claim ledger, and every figure caption is emitted from a source-owned caption registry. The result is both a map of a genuinely transdisciplinary field and a reproducibility contract: the same version-controlled inputs regenerate the inventories, validation report, analytical figures, manuscript variables, and paper while preserving explicit caveats about registry scope, jurisdictional reach, and the boundary between what the offline gates prove and what only a live source check can confirm. --- Associated artifacts GitHub release: Entomological Law (v1.0.0) (https://github.com/docxology/EntoLaw/releases/tag/v1.0.0) DOI: https://doi.org/10.5281/zenodo.21137276 Zenodo: https://zenodo.org/records/21137276 PDF SHA-256: 5a1891704c285491c97297dea66e9a4ea019b2dabddcc28cb5aeb18d6a335225
", "keywords": [ "entomological law", "legal entomology", "forensic entomology", "endangered species", "invasive species", "insect welfare", "gene drive", "biological weapons convention", "reproducible legal scholarship" ], "doi": "10.5281/zenodo.21137276", "github_release_url": "https://github.com/docxology/EntoLaw/releases/tag/v1.0.0" }, "2026_ShapeBetween": { "year": "2026", "topic": "ShapeBetween", "name": "The Shape Between: A Full-Page Illustrated Storybook Template", "description": "template_storybook demonstrates a public, standalone picture-book workflow in\nthe research template repository. The bundled project renders a deterministic\nfourteen-page storybook subtitled A geometric fable of belonging, bracing, and\nreciprocal form: a clear cover, a publication-and-acknowledgements page, and\ntwelve story pages in which a child tetrahedron raised by cubes meets a child\ncube raised by tetrahedra. The story uses a large reciprocal symbol, a\ntetrahedron-inside-cube stability spread, a shadow-projection lesson, a\ntensegrity lantern, and a vector garden to frame belonging without sameness:\nsquare and triangle families remain distinct while the space between them\nbecomes navigable.\n\nThe project separates story data, rendering logic, and orchestration. Story\ntext, characters, page order, palettes, and overlay choices live in\ncontent/story.yaml; character generation and illustration live in\nsrc/storybook/; one script renders the cover, each page script renders a\nnumbered page, and a final script assembles the PDF. This makes the exemplar a\nforkable pattern for full-page creative artifacts rather than standard\nmanuscript-centered figure generation.\n\n---\nAssociated artifacts\nGitHub release: The Shape Between v0.1.0 (https://github.com/docxology/template_storybook/releases/tag/v0.1.0)\nDOI: https://doi.org/10.5281/zenodo.21176001\nZenodo: https://zenodo.org/records/21176001\nPDF SHA-256: 7fb0ed775db212cd9f4b5b578bef8900c9f925bc0c5cc047331eb6900679eeda", "authors": "Daniel Ari Friedman", "abstract": "template_storybook demonstrates a public, standalone picture-book workflow in\nthe research template repository. The bundled project renders a deterministic\nfourteen-page storybook subtitled A geometric fable of belonging, bracing, and\nreciprocal form: a clear cover, a publication-and-acknowledgements page, and\ntwelve story pages in which a child tetrahedron raised by cubes meets a child\ncube raised by tetrahedra. The story uses a large reciprocal symbol, a\ntetrahedron-inside-cube stability spread, a shadow-projection lesson, a\ntensegrity lantern, and a vector garden to frame belonging without sameness:\nsquare and triangle families remain distinct while the space between them\nbecomes navigable.\n\nThe project separates story data, rendering logic, and orchestration. Story\ntext, characters, page order, palettes, and overlay choices live in\ncontent/story.yaml; character generation and illustration live in\nsrc/storybook/; one script renders the cover, each page script renders a\nnumbered page, and a final script assembles the PDF. This makes the exemplar a\nforkable pattern for full-page creative artifacts rather than standard\nmanuscript-centered figure generation.\n\n---\nAssociated artifacts\nGitHub release: The Shape Between v0.1.0 (https://github.com/docxology/template_storybook/releases/tag/v0.1.0)\nDOI: https://doi.org/10.5281/zenodo.21176001\nZenodo: https://zenodo.org/records/21176001\nPDF SHA-256: 7fb0ed775db212cd9f4b5b578bef8900c9f925bc0c5cc047331eb6900679eeda", "keywords": [ "storybook", "illustration", "procedural graphics", "reportlab", "reproducible publishing" ], "doi": "10.5281/zenodo.21176000", "github_release_url": "https://github.com/docxology/template_storybook/releases/tag/v0.1.0" }, "2026_AutopoieticProjectGeneration": { "year": "2026", "topic": "AutopoieticProjectGeneration", "name": "Autopoietic Project Generation", "description": "template_autopoiesis is a combinatoric grammar that deterministically generates\nwhole runnable projects — not files or snippets, but complete, independently\ntestable child repositories with their own kernel source, tests, analysis\nentry point, and manuscript. A single integer seed plus a grammar of orthogonal\nslots (primitive domain, analytical track, section set, and three\npresentation/provenance slots) selects one child from a combinatoric product\nspace of 360 nominal (45 content-distinct)\nconfigurations, via a SHA-256 digest of the seed and slot identity — with no\nrandom-number generator anywhere in the expansion path.\n\nProject generators routinely claim completeness, determinism, and\ntraceability without making any of the three independently checkable. This\nexemplar treats each claim as a structural property to verify rather than a\nrhetorical one to assert: verify_child() recomputes a tree hash from the\nfiles actually on disk and compares it against the recorded provenance,\nrather than trusting a value the same run wrote down; a honesty manifest\ninspects the live source AST to confirm every claim in this manuscript\nresolves to a real function in a real file; and a per-domain mutation gate\nchecks that the acceptance tests reject a constant-success stub before\ntrusting that they accept the real kernel. The same discipline governs this\ndocument itself — every number below is substituted at render time from a\nlive measurement rather than hand-typed as a literal.\n\nAcross 5 heterogeneous primitive domains\n(- optimization\n- dynamics\n- statistics\n- signal\n- graph), 493 tests exercise both fixed ground-truth\nchecks and Hypothesis-driven property invariants at 96.28% branch\ncoverage, with an explicit negative control per domain distinguishing the\nreal kernel from a deliberately-wrong one.\n\nGeneration pipeline\n\nGrammar product space\n\n- Domain count: 5\n- Effective product size: 45\n- Total product size: 360\n- Reserved slots: 3 (figure_profile, qr_profile, integrity_profile)\n- Grammar hash: f84a8f9dbcb18e37\n- Tests: 493 · Coverage: 96.28%\n\n---\nAssociated artifacts\nGitHub release: Autopoietic Project Generation (v1.0.1) — improved abstract (https://github.com/docxology/template/releases/tag/v1.0.1)\nDOI: https://doi.org/10.5281/zenodo.21227869\nZenodo: https://zenodo.org/records/21227869\nPDF SHA-256: 84145371f10c7ee97a75c53d78917d247fd6bd987d2c1764ee437b6fbdef8f51", "authors": "Daniel Ari Friedman", "abstract": "template_autopoiesis is a combinatoric grammar that deterministically generates\nwhole runnable projects — not files or snippets, but complete, independently\ntestable child repositories with their own kernel source, tests, analysis\nentry point, and manuscript. A single integer seed plus a grammar of orthogonal\nslots (primitive domain, analytical track, section set, and three\npresentation/provenance slots) selects one child from a combinatoric product\nspace of 360 nominal (45 content-distinct)\nconfigurations, via a SHA-256 digest of the seed and slot identity — with no\nrandom-number generator anywhere in the expansion path.\n\nProject generators routinely claim completeness, determinism, and\ntraceability without making any of the three independently checkable. This\nexemplar treats each claim as a structural property to verify rather than a\nrhetorical one to assert: verify_child() recomputes a tree hash from the\nfiles actually on disk and compares it against the recorded provenance,\nrather than trusting a value the same run wrote down; a honesty manifest\ninspects the live source AST to confirm every claim in this manuscript\nresolves to a real function in a real file; and a per-domain mutation gate\nchecks that the acceptance tests reject a constant-success stub before\ntrusting that they accept the real kernel. The same discipline governs this\ndocument itself — every number below is substituted at render time from a\nlive measurement rather than hand-typed as a literal.\n\nAcross 5 heterogeneous primitive domains\n(- optimization\n- dynamics\n- statistics\n- signal\n- graph), 493 tests exercise both fixed ground-truth\nchecks and Hypothesis-driven property invariants at 96.28% branch\ncoverage, with an explicit negative control per domain distinguishing the\nreal kernel from a deliberately-wrong one.\n\nGeneration pipeline\n\nGrammar product space\n\n- Domain count: 5\n- Effective product size: 45\n- Total product size: 360\n- Reserved slots: 3 (figure_profile, qr_profile, integrity_profile)\n- Grammar hash: f84a8f9dbcb18e37\n- Tests: 493 · Coverage: 96.28%\n\n---\nAssociated artifacts\nGitHub release: Autopoietic Project Generation (v1.0.1) — improved abstract (https://github.com/docxology/template/releases/tag/v1.0.1)\nDOI: https://doi.org/10.5281/zenodo.21227869\nZenodo: https://zenodo.org/records/21227869\nPDF SHA-256: 84145371f10c7ee97a75c53d78917d247fd6bd987d2c1764ee437b6fbdef8f51", "keywords": [ "autopoiesis", "combinatoric grammar", "deterministic generation", "project synthesis", "reproducible research", "infrastructure automation" ], "doi": "10.5281/zenodo.21227869", "github_release_url": "https://github.com/docxology/template_autopoiesis/releases/tag/v1.0.1" }, "2026_TemplatePitchDeck": { "year": "2026", "topic": "TemplatePitchDeck", "name": "template_pitch_deck: Reproducible, Validated Pitch-Deck Generation", "description": "Research groups routinely need to pitch their work — to funders, partners, or\ncollaborators — yet pitch decks are almost never treated as reproducible\nresearch artifacts: they are hand-assembled in proprietary slide tools,\ncontain unverifiable claims, and cannot be regenerated when the underlying\nfacts change. template_pitch_deck closes that gap. It generates six\nartifacts from one token-resolved content source — short, medium, and long\ndecks, each in both PDF and PPTX — with every numeric claim traced back to a\nlive introspection of the repository it describes, every {{TOKEN}}\nsubstitution verified to have actually landed, and every sentence checked\nagainst a denylist of pitch-deck clichés.\n\nThe flagship content pitches template_template (../../template_template/), this\nmonorepo's own autopoietic meta-project, to a meta-science and\nscience-integrity audience — the kind of pitch an organization like the\nActive Inference Institute or COGSEC would actually hand to a funder. The\nrendering engine itself is new, reusable infrastructure:\ninfrastructure/rendering/slide_deck.py (ReportLab, PDF) and\ninfrastructure/rendering/pptx_deck.py (python-pptx) both consume the same\nDeckContent model, so a PDF and a PPTX built from identical content carry\nidentical slide counts and identical text — verified by direct read-back of\nboth file formats, not by inspection.\n\nKeywords: pitch deck, slide generation, reproducible research\ncommunication, meta-science infrastructure, token validation, PPTX, PDF\nrendering.\n\n---\nAssociated artifacts\nGitHub release: v1.0.0 (https://github.com/docxology/template-pitch-deck/releases/tag/v1.0.0)\nDOI: https://doi.org/10.5281/zenodo.21281509\nZenodo: https://zenodo.org/records/21281509\nPDF SHA-256: 66941950e2307d1e9717c0045f3db3196d7d2542c3f5ffceced00438f653c55b", "authors": "Daniel Ari Friedman", "abstract": "Research groups routinely need to pitch their work — to funders, partners, or\ncollaborators — yet pitch decks are almost never treated as reproducible\nresearch artifacts: they are hand-assembled in proprietary slide tools,\ncontain unverifiable claims, and cannot be regenerated when the underlying\nfacts change. template_pitch_deck closes that gap. It generates six\nartifacts from one token-resolved content source — short, medium, and long\ndecks, each in both PDF and PPTX — with every numeric claim traced back to a\nlive introspection of the repository it describes, every {{TOKEN}}\nsubstitution verified to have actually landed, and every sentence checked\nagainst a denylist of pitch-deck clichés.\n\nThe flagship content pitches template_template (../../template_template/), this\nmonorepo's own autopoietic meta-project, to a meta-science and\nscience-integrity audience — the kind of pitch an organization like the\nActive Inference Institute or COGSEC would actually hand to a funder. The\nrendering engine itself is new, reusable infrastructure:\ninfrastructure/rendering/slide_deck.py (ReportLab, PDF) and\ninfrastructure/rendering/pptx_deck.py (python-pptx) both consume the same\nDeckContent model, so a PDF and a PPTX built from identical content carry\nidentical slide counts and identical text — verified by direct read-back of\nboth file formats, not by inspection.\n\nKeywords: pitch deck, slide generation, reproducible research\ncommunication, meta-science infrastructure, token validation, PPTX, PDF\nrendering.\n\n---\nAssociated artifacts\nGitHub release: v1.0.0 (https://github.com/docxology/template-pitch-deck/releases/tag/v1.0.0)\nDOI: https://doi.org/10.5281/zenodo.21281509\nZenodo: https://zenodo.org/records/21281509\nPDF SHA-256: 66941950e2307d1e9717c0045f3db3196d7d2542c3f5ffceced00438f653c55b", "keywords": [ "pitch deck", "slide generation", "reproducible research communication", "meta-science infrastructure", "science integrity", "token validation", "PPTX", "PDF rendering" ], "doi": "10.5281/zenodo.21281509", "github_release_url": "https://github.com/docxology/template-pitch-deck/releases/tag/v1.0.2" }, "2026_ReproducibleLiteratureSynthesis": { "year": "2026", "topic": "ReproducibleLiteratureSynthesis", "name": "Reproducible Literature Synthesis with infrastructure/search and infrastructure/reference", "description": "This paper documents template_search_project, the literature-search exemplar shipped with the Research Project Template (https://github.com/docxology/template). The project demonstrates two configurable, reproducible pipelines sharing the same configuration file and the same infrastructure/search/ + infrastructure/reference/ modules. The standard pipeline (scripts/run_search_pipeline.py) handles a single SearchQuery end-to-end. The deep-search pipeline (scripts/run_deep_search.py, see ) fans out across a list of keywords (each capped at 100 papers per keyword from deep_search.max_results_per_keyword in manuscript/config.yaml), fully enriches every paper with its abstract and PDF fulltext, and (optionally) uses the local LLM to write a multi-section reading note for every paper. When a deep-search aggregate exists, the latest run covered 3 keyword(s) with unique paper(s) after cross-keyword deduplication. Both turn a free-text topic into: 1. a deduplicated, year-filtered set of papers drawn from arXiv, Crossref, optional local corpora, and (opt-in) Paperclip (https://paperclip.gxl.ai/); 2. a Pandoc-compatible references.bib byte-identical in style to the canonical exemplar in template_code_project (../../template_code_project/manuscript/references.bib) (file manuscript/references.bib); 3. cached abstracts and (optionally) extracted PDF full text, written to disk under stable per-paper identifiers; and 4. an LLM-synthesised reading report assembled from per-paper analyses and a cross-corpus thematic synthesis, all produced by a local Ollama model with pinned seed and temperature. All discovery logic lives in infrastructure/search/literature/ (source on GitHub (https://github.com/docxology/template/tree/main/infrastructure/search/literature)); all export logic lives in infrastructure/reference/citation/ (source on GitHub (https://github.com/docxology/template/tree/main/infrastructure/reference/citation)); LLM synthesis reuses the existing infrastructure/llm/ (source on GitHub (https://github.com/docxology/template/tree/main/infrastructure/llm)) bridge. The project itself contains only thin orchestration, manuscript prose, and a test suite — perfectly mirroring the two-layer architecture the template enforces. The motivating concern is reproducibility: a query at time $t_0$ should produce the same results at time $t_1$ unless the cache is explicitly invalidated. This is achieved by deterministic search caching keyed on canonical query identity, on-disk caching of every fetched abstract / PDF, and pinned LLM seeds. The same manuscript/config.yaml that drives the pipeline is also the only configuration any reviewer needs. Run snapshot. With the bundled manuscript/config.yaml, the most recent pipeline execution evaluated the query \"reproducible research optimization\" against local, returned 6 deduplicated paper(s) (4 carrying a DOI, 6 carrying an abstract), and recorded backend errors: none. Resolve `{{…}} tokens by running scripts/z_generate_manuscript_variables.py after run_search_pipeline.py; the script writes output/data/manuscript_variables.json and resolved markdown under output/manuscript/`, which the PDF-rendering stage prefers when present. Keywords: literature search, BibTeX automation, reproducible research, local LLM synthesis, scientific infrastructure", "authors": "Daniel Ari Friedman", "abstract": "This paper documents template_search_project, the literature-search exemplar shipped with the Research Project Template (https://github.com/docxology/template). The project demonstrates two configurable, reproducible pipelines sharing the same configuration file and the same infrastructure/search/ + infrastructure/reference/ modules. The standard pipeline (scripts/run_search_pipeline.py) handles a single SearchQuery end-to-end. The deep-search pipeline (scripts/run_deep_search.py, see ) fans out across a list of keywords (each capped at 100 papers per keyword from deep_search.max_results_per_keyword in manuscript/config.yaml), fully enriches every paper with its abstract and PDF fulltext, and (optionally) uses the local LLM to write a multi-section reading note for every paper. When a deep-search aggregate exists, the latest run covered 3 keyword(s) with unique paper(s) after cross-keyword deduplication. Both turn a free-text topic into: 1. a deduplicated, year-filtered set of papers drawn from arXiv, Crossref, optional local corpora, and (opt-in) Paperclip (https://paperclip.gxl.ai/); 2. a Pandoc-compatible references.bib byte-identical in style to the canonical exemplar in template_code_project (../../template_code_project/manuscript/references.bib) (file manuscript/references.bib); 3. cached abstracts and (optionally) extracted PDF full text, written to disk under stable per-paper identifiers; and 4. an LLM-synthesised reading report assembled from per-paper analyses and a cross-corpus thematic synthesis, all produced by a local Ollama model with pinned seed and temperature. All discovery logic lives in infrastructure/search/literature/ (source on GitHub (https://github.com/docxology/template/tree/main/infrastructure/search/literature)); all export logic lives in infrastructure/reference/citation/ (source on GitHub (https://github.com/docxology/template/tree/main/infrastructure/reference/citation)); LLM synthesis reuses the existing infrastructure/llm/ (source on GitHub (https://github.com/docxology/template/tree/main/infrastructure/llm)) bridge. The project itself contains only thin orchestration, manuscript prose, and a test suite — perfectly mirroring the two-layer architecture the template enforces. The motivating concern is reproducibility: a query at time $t_0$ should produce the same results at time $t_1$ unless the cache is explicitly invalidated. This is achieved by deterministic search caching keyed on canonical query identity, on-disk caching of every fetched abstract / PDF, and pinned LLM seeds. The same manuscript/config.yaml that drives the pipeline is also the only configuration any reviewer needs. Run snapshot. With the bundled manuscript/config.yaml, the most recent pipeline execution evaluated the query \"reproducible research optimization\" against local, returned 6 deduplicated paper(s) (4 carrying a DOI, 6 carrying an abstract), and recorded backend errors: none. Resolve `{{…}} tokens by running scripts/z_generate_manuscript_variables.py after run_search_pipeline.py; the script writes output/data/manuscript_variables.json and resolved markdown under output/manuscript/`, which the PDF-rendering stage prefers when present. Keywords: literature search, BibTeX automation, reproducible research, local LLM synthesis, scientific infrastructure", "keywords": [ "literature search", "automated reference management", "BibTeX", "reproducible research", "local LLM synthesis" ], "doi": "10.5281/zenodo.21298894" }, "2026_RegisteredReportTemplate": { "year": "2026", "topic": "RegisteredReportTemplate", "name": "Registered Report Template: Preregistration, Deviations, and Claim Boundaries", "description": "This document is a template, not an empirical study. It demonstrates the registered-report workflow end to end: locking a preregistration, validating its completeness, executing the registered analysis plan against deterministic demonstration data, and reporting confirmatory and exploratory claims through an explicit deviation ledger. Every quantity reported here is produced by the tested code in src/registered_report/ and regenerated by scripts/generate_figures.py; none is hand-entered or illustrative. The demonstration binds a single confirmatory hypothesis (H1) to one registered outcome (primary_score) analysed by a two-sided label-permutation test. Run on a seeded two-group dataset (seed = 20260709, n = 24 per group), the registered test yields an observed mean difference of 1.003 with a two-sided permutation p-value of 0.0005 (0 of 2000 shuffles at least as extreme), significant at the preregistered alpha = 0.05. A deliberately introduced secondary endpoint and an alternative model are carried only as documented deviations, keeping the confirmatory claim boundary intact. The purpose is to give forks a working, auditable skeleton in which planned analyses, frozen hypotheses, deviations, and post-run claims are separable and machine-checkable.", "authors": "Daniel Ari Friedman", "abstract": "This document is a template, not an empirical study. It demonstrates the registered-report workflow end to end: locking a preregistration, validating its completeness, executing the registered analysis plan against deterministic demonstration data, and reporting confirmatory and exploratory claims through an explicit deviation ledger. Every quantity reported here is produced by the tested code in src/registered_report/ and regenerated by scripts/generate_figures.py; none is hand-entered or illustrative. The demonstration binds a single confirmatory hypothesis (H1) to one registered outcome (primary_score) analysed by a two-sided label-permutation test. Run on a seeded two-group dataset (seed = 20260709, n = 24 per group), the registered test yields an observed mean difference of 1.003 with a two-sided permutation p-value of 0.0005 (0 of 2000 shuffles at least as extreme), significant at the preregistered alpha = 0.05. A deliberately introduced secondary endpoint and an alternative model are carried only as documented deviations, keeping the confirmatory claim boundary intact. The purpose is to give forks a working, auditable skeleton in which planned analyses, frozen hypotheses, deviations, and post-run claims are separable and machine-checkable.", "keywords": [ "registered report", "preregistration", "replication", "deviation ledger" ], "doi": "10.5281/zenodo.21298892" }, "2026_RedactedReportTemplate": { "year": "2026", "topic": "RedactedReportTemplate", "name": "Redacted Report Template: Disclosure Control and Release Audit", "description": "This exemplar demonstrates a complete disclosure-control pipeline for sanitized public release reports. The methodology combines classification-ceiling enforcement, source-protection validation, mosaic-risk scoring, and TPM-backed sealed sidecars across a sixteen-variant visual proof matrix. Four redaction styles—blackout, whiteout, grayout, and blur—are rendered across four PDF backgrounds—white, gray, black, and blur—yielding sixteen base proof PDFs. Each receives nine steganographic security methods including SHA-256/SHA-512 hash manifests, diagonal watermark overlays, footer provenance stamps, invisible text, QR and Code128 barcodes, PDF Info and XMP metadata, and embedded manifest attachments. Optional Kmyth TPM sealing wraps each hash manifest and steganography PDF in a .ski sidecar sealed against the TPM2-TSS storage hierarchy, bound to PCR selections and policy or-values. The release gate requires three reviewer roles—originator, classification reviewer, and release authority—each providing a non-empty rationale. A source-safe redaction ledger records SHA-256 hashes of each redacted span without exposing source text, and a segment hash manifest compares source and public SHA-256 digests for reproducible audit. The comprehensive release packet combines sanitized text, audit findings, ledger, hashes, review gate status, and paragraph-level audit tables into a single JSON-ready export. This exemplar confirms that visual presentation choices remain orthogonal to the release gate: the same source-safe decisions drive every output variant.", "authors": "Daniel Ari Friedman", "abstract": "This exemplar demonstrates a complete disclosure-control pipeline for sanitized public release reports. The methodology combines classification-ceiling enforcement, source-protection validation, mosaic-risk scoring, and TPM-backed sealed sidecars across a sixteen-variant visual proof matrix. Four redaction styles—blackout, whiteout, grayout, and blur—are rendered across four PDF backgrounds—white, gray, black, and blur—yielding sixteen base proof PDFs. Each receives nine steganographic security methods including SHA-256/SHA-512 hash manifests, diagonal watermark overlays, footer provenance stamps, invisible text, QR and Code128 barcodes, PDF Info and XMP metadata, and embedded manifest attachments. Optional Kmyth TPM sealing wraps each hash manifest and steganography PDF in a .ski sidecar sealed against the TPM2-TSS storage hierarchy, bound to PCR selections and policy or-values. The release gate requires three reviewer roles—originator, classification reviewer, and release authority—each providing a non-empty rationale. A source-safe redaction ledger records SHA-256 hashes of each redacted span without exposing source text, and a segment hash manifest compares source and public SHA-256 digests for reproducible audit. The comprehensive release packet combines sanitized text, audit findings, ledger, hashes, review gate status, and paragraph-level audit tables into a single JSON-ready export. This exemplar confirms that visual presentation choices remain orthogonal to the release gate: the same source-safe decisions drive every output variant.", "keywords": [ "redaction", "disclosure control", "release audit", "source protection" ], "doi": "10.5281/zenodo.21298890" }, "2026_PoolsRulesTools": { "year": "2026", "topic": "PoolsRulesTools", "name": "Pools, Rules, and Tools: A Template-Integrated Resource Architecture", "description": "", "authors": "Daniel Ari Friedman", "abstract": "Research software repositories in monorepo configurations accumulate three categories of shared resources that individual projects must consume without re-implementing discovery logic: data pools (bibliographies, contacts, datasets), governance rules (style guides, coverage thresholds, citation schemas), and executable tools (code executors, validators, skill invocations). Without a canonical integration pattern, projects either duplicate discovery logic or silently ignore resources that fail to load — both outcomes degrade reproducibility and collaborative cohesion [Wilson et al., 2014, Taschuk and Wilson, 2017]. This paper presents template_pools_rules_tools, a meta-project exemplar that demonstrates how a single project can programmatically discover, validate, and exercise all three resource categories with zero tight coupling to any specific resource instance. The exemplar comprises eight Python modules — three resource readers (fonds_reader, rules_applier, tools_invoker), an orchestrator (integration), a semantic rule evaluator (strong_rule_evaluator), a figure generator (figures), a manuscript-token generator (manuscript_variables), and shared type definitions (type_defs) — plus six thin orchestration scripts and a fully token-injected manuscript pipeline. The architecture (fig. 1) separates resource ownership from resource consumption. Resources live in top-level fonds/, rules/, and tools/ directories and are never modified by consumers. Each resource exposes a typed manifest (fonds.yaml, rules.yaml, tools.yaml) that the corresponding reader module uses for discovery and validation. All readers implement graceful fallbacks: they return None or empty collections when a resource is absent, log a warning via the standard library logging module, and allow the integration pipeline to continue. This revision extends the original three-figure presentation to eight content figures plus a cover illustration — a fond taxonomy (fig. 2), a rule hierarchy (fig. 3), a tool invocation contract (fig. 4), a three-level resilience diagram (fig. 8), and a script pipeline flow (fig. 6) — so that every structural claim in the prose has a corresponding visual. In a representative pipeline run, the integration demo loaded 3 fonds, validated 2 rule sets, discovered 3 tools, and processed 8 bibliography entries — all reported as structured JSON that populates manuscript variable tokens at render time. Tests covering the eight src/ modules (across nine test files) achieve well above the required >=90% combined line coverage and use real file paths rather than mocks, ensuring that reported counts are genuine — run uv run pytest … --cov-report=term for the current test count and coverage percentage rather than trusting a number printed here. The template_pools_rules_tools exemplar provides a reference implementation that any project in the template repository can consult when designing its own resource-consumption layer.", "keywords": [], "doi": "10.5281/zenodo.21298888" }, "2026_IllegalStatesMostly": { "year": "2026", "topic": "IllegalStatesMostly", "name": "Illegal States, Mostly Unrepresentable", "description": "This paper presents a strongly-typed, decentralized multiagent simulation — an ant-robot colony — as the computational exemplar of the Research Project Template (https://github.com/docxology/template). Each colony member is an Agent that owns exactly one real, on-disk SQLite database and one in-process, fault-injectable protocol endpoint; no agent ever touches another agent's storage or network state. The implementation lives under projects/templates/template_formal/src/template_formal/; the demo pipeline is orchestrated by scripts/02_run_analysis.py. The paper's central claim is methodological, not a typing-features showcase: static typing's honest value in Python is edit-time/CI-time error prevention on structurally representable invariants, and nothing more. Nominal identifiers (AgentId, MessageId, TxnId as distinct NewType wrappers), a tagged-union Result[T, E] ADT with match-exhaustiveness, and a session-typed protocol state machine (IdleSession → HandshakingSession → EstablishedSession → ClosedSession) each make an illegal program a type error, verified by a real mypy --strict subprocess run against six known-bad negative-control fixtures plus three known-good positive-control fixtures (tests/mypy_fixtures/). Where the type system cannot help — reusing a consumed transaction handle, reusing a consumed protocol-phase instance, or receiving malformed bytes off an untyped network boundary — the implementation runtime-guards instead, and the manuscript says so explicitly rather than eliding the distinction. We also frame, without over-claiming, two additional lenses: the per-agent storage schema as a functor $\\mathrm{Schema} \\to \\mathbf{Set}$ in the sense of @fong2018seven, and each agent's per-tick decision as an approximate minimizer of a closed-form expected-free-energy quantity in the spirit of @friston2005theory, bridged to collective organization via the Memory Evolutive Systems framework of . Both framings are declared as design lenses, not machine-checked mathematical results — the paper is explicit about which of its claims are proofs and which are analogies. Contributions are architectural, epistemic, and empirical. Architecturally: a zero-mock test suite (tests/) covering ADT exhaustiveness, affine-handle reuse, session-type phase transitions, seeded fault injection over a real in-process bus, and a three-agent colony integration test exhibiting a real stigmergic positive-feedback mechanism (deliberately not overclaimed as \"emergence\" — see @sec:results-discussion). Epistemically: an explicit \"What mypy --strict proves vs. what is a runtime discipline\" section (@sec:honesty-line) that pins every strong claim to the ISC (Ideal-State Criterion) number of its paired negative-control test, so the claim-to-evidence mapping is auditable rather than asserted. Empirically: eight pre-registered analyses grouped across three experiment families, falsifiable experiments (@sec:results-discussion) — a decay-rate sweep revealing a real, non-monotonic threshold effect (near-zero convergence below decay $\\approx 0.35$, a $100\\%$ plateau at moderate decay, and a measurable decline at total evaporation); a random-choice null-model comparison showing the real mechanism's Wilson-bounded convergence rate ($93.3\\%$) does not overlap a chance baseline's ($0.67\\%$); and a heterogeneity-magnitude sweep showing convergence rate decreases strictly monotonically as agent preferences spread wider — each stated with its falsification criterion before its real, seeded result, using genuinely new stdlib-only infrastructure (colony/nullmodel.py, colony/sweep.py) rather than one-off scripts. Keywords: strongly typed programming, session types, algebraic data types, category theory, Active Inference, multiagent systems, affine types, illegal state unrepresentable.", "authors": "Daniel Ari Friedman", "abstract": "This paper presents a strongly-typed, decentralized multiagent simulation — an ant-robot colony — as the computational exemplar of the Research Project Template (https://github.com/docxology/template). Each colony member is an Agent that owns exactly one real, on-disk SQLite database and one in-process, fault-injectable protocol endpoint; no agent ever touches another agent's storage or network state. The implementation lives under projects/templates/template_formal/src/template_formal/; the demo pipeline is orchestrated by scripts/02_run_analysis.py. The paper's central claim is methodological, not a typing-features showcase: static typing's honest value in Python is edit-time/CI-time error prevention on structurally representable invariants, and nothing more. Nominal identifiers (AgentId, MessageId, TxnId as distinct NewType wrappers), a tagged-union Result[T, E] ADT with match-exhaustiveness, and a session-typed protocol state machine (IdleSession → HandshakingSession → EstablishedSession → ClosedSession) each make an illegal program a type error, verified by a real mypy --strict subprocess run against six known-bad negative-control fixtures plus three known-good positive-control fixtures (tests/mypy_fixtures/). Where the type system cannot help — reusing a consumed transaction handle, reusing a consumed protocol-phase instance, or receiving malformed bytes off an untyped network boundary — the implementation runtime-guards instead, and the manuscript says so explicitly rather than eliding the distinction. We also frame, without over-claiming, two additional lenses: the per-agent storage schema as a functor $\\mathrm{Schema} \\to \\mathbf{Set}$ in the sense of @fong2018seven, and each agent's per-tick decision as an approximate minimizer of a closed-form expected-free-energy quantity in the spirit of @friston2005theory, bridged to collective organization via the Memory Evolutive Systems framework of . Both framings are declared as design lenses, not machine-checked mathematical results — the paper is explicit about which of its claims are proofs and which are analogies. Contributions are architectural, epistemic, and empirical. Architecturally: a zero-mock test suite (tests/) covering ADT exhaustiveness, affine-handle reuse, session-type phase transitions, seeded fault injection over a real in-process bus, and a three-agent colony integration test exhibiting a real stigmergic positive-feedback mechanism (deliberately not overclaimed as \"emergence\" — see @sec:results-discussion). Epistemically: an explicit \"What mypy --strict proves vs. what is a runtime discipline\" section (@sec:honesty-line) that pins every strong claim to the ISC (Ideal-State Criterion) number of its paired negative-control test, so the claim-to-evidence mapping is auditable rather than asserted. Empirically: eight pre-registered analyses grouped across three experiment families, falsifiable experiments (@sec:results-discussion) — a decay-rate sweep revealing a real, non-monotonic threshold effect (near-zero convergence below decay $\\approx 0.35$, a $100\\%$ plateau at moderate decay, and a measurable decline at total evaporation); a random-choice null-model comparison showing the real mechanism's Wilson-bounded convergence rate ($93.3\\%$) does not overlap a chance baseline's ($0.67\\%$); and a heterogeneity-magnitude sweep showing convergence rate decreases strictly monotonically as agent preferences spread wider — each stated with its falsification criterion before its real, seeded result, using genuinely new stdlib-only infrastructure (colony/nullmodel.py, colony/sweep.py) rather than one-off scripts. Keywords: strongly typed programming, session types, algebraic data types, category theory, Active Inference, multiagent systems, affine types, illegal state unrepresentable.", "keywords": [ "strongly typed programming", "session types", "algebraic data types", "category theory", "active inference", "multiagent systems", "affine types", "illegal state unrepresentable" ], "doi": "10.5281/zenodo.21298885" }, "2026_DataDescriptorTemplate": { "year": "2026", "topic": "DataDescriptorTemplate", "name": "Data Descriptor Template: Schema, Provenance, and Release Readiness", "description": "This exemplar demonstrates a data descriptor workflow in which the schema, file inventory, provenance chain, license boundary, and validation gate are treated as first-class research artifacts rather than afterthoughts. It ships a small, public, synthetic demonstration dataset (two CSV files under data/fixtures/) and a machine-readable descriptor (data/example_descriptor.json) that declares each file's media type, sha256 checksum, and row count alongside a six-field data dictionary with typed constraints. A tested validation library (src/data_descriptor/) checks the descriptor's shape, safety, and completeness; recomputes each declared checksum and row count against the bytes on disk; and emits a deterministic, metadata-only release manifest suitable for pre-publication review. Every figure and quantitative claim in this manuscript is produced by that library and regenerated on demand, so the prose describes structure and provenance rather than transcribing values that would drift. This is a template with a demonstration dataset: it makes no scientific claim about the data, only about how to describe and release a dataset responsibly.", "authors": "Daniel Ari Friedman", "abstract": "This exemplar demonstrates a data descriptor workflow in which the schema, file inventory, provenance chain, license boundary, and validation gate are treated as first-class research artifacts rather than afterthoughts. It ships a small, public, synthetic demonstration dataset (two CSV files under data/fixtures/) and a machine-readable descriptor (data/example_descriptor.json) that declares each file's media type, sha256 checksum, and row count alongside a six-field data dictionary with typed constraints. A tested validation library (src/data_descriptor/) checks the descriptor's shape, safety, and completeness; recomputes each declared checksum and row count against the bytes on disk; and emits a deterministic, metadata-only release manifest suitable for pre-publication review. Every figure and quantitative claim in this manuscript is produced by that library and regenerated on demand, so the prose describes structure and provenance rather than transcribing values that would drift. This is a template with a demonstration dataset: it makes no scientific claim about the data, only about how to describe and release a dataset responsibly.", "keywords": [ "data descriptor", "FAIR data", "provenance", "schema validation" ], "doi": "10.5281/zenodo.21298883", "domain": "Computational" }, "2026_PriorCognitiveArt": { "year": "2026", "topic": "PriorCognitiveArt", "name": "Prior Cognitive Art", "description": "A prior is not explained by stacking more priors; it is located by mechanism, function, history, and fixed-point organization. The paper's precise thesis is procedural: before asking why\na prior exists, identify which explanatory kind is being requested and name the\nrule that will terminate the explanation. This working paper argues that Tinbergen's four questions\nare not stacked levels in one causal chain. They are crossed axes: proximate\nversus ultimate, and static versus developmental. Mechanism and function ask\nwhat a prior is doing now; ontogeny and phylogeny ask how such organization came\nto be across different timescales.\n\nThe paper treats the familiar \"prior on a prior\" problem in hierarchical Bayes\nas structurally parallel to an ontogenetic account that says one prior selects\nanother. Both postpone the question unless they terminate. Three termination\nfamilies organize the paper: pragmatic closure, selection closure, and\nfixed-point termination. They are organizing families, not an exhaustive\ntaxonomy of every possible explanation. A final upstream pass asks what comes\nbefore the first prior and answers with a constraint stack -- viability,\nallostasis, co-homeostasis, development, and niche support -- rather than with a\nhidden meta-prior. The project uses plain text, deterministic conceptual visualizations, and one deterministic illustrative simulation trace.\nIts supplement adds generated, auto-numbered formal claims and a closed symbol\nglossary as text-integrity artifacts rather than empirical machinery.\nThe paper includes one deterministic illustrative simulation trace over authored formal states; it does not run stochastic simulations, synthetic-data experiments, empirical estimates, or performance benchmarks.\n\nThe \"art\" in the title names the craft at stake: arranging explanatory kinds\n(proximate/ultimate, static/developmental, selection/fixed-point) so that no\nsingle kind is mistaken for the whole, and rendering that arrangement as\ndeterministic figures and checkable formalism rather than as empirical simulation evidence. It\nalso marks a bounded link to aesthetic practice: artworks can stage encounters\nwith expectations, material affordances, ambiguity, and meaning, but this paper\ntreats that link as a conceptual analogy, not as an empirical theory of art.\n\n---\nAssociated artifacts\nGitHub release: Prior Cognitive Art v0.1.0 (https://github.com/docxology/prior_cognitive_art/releases/tag/v0.1.0)\nDOI: https://doi.org/10.5281/zenodo.21316510\nZenodo: https://zenodo.org/records/21316510\nPDF SHA-256: 62aa1d8d4af75f4b5bdc894516720bdf61ae68a4be27f44aefc09c2dd2fbba67", "authors": "Daniel Ari Friedman", "abstract": "A prior is not explained by stacking more priors; it is located by mechanism, function, history, and fixed-point organization. The paper's precise thesis is procedural: before asking why\na prior exists, identify which explanatory kind is being requested and name the\nrule that will terminate the explanation. This working paper argues that Tinbergen's four questions\nare not stacked levels in one causal chain. They are crossed axes: proximate\nversus ultimate, and static versus developmental. Mechanism and function ask\nwhat a prior is doing now; ontogeny and phylogeny ask how such organization came\nto be across different timescales.\n\nThe paper treats the familiar \"prior on a prior\" problem in hierarchical Bayes\nas structurally parallel to an ontogenetic account that says one prior selects\nanother. Both postpone the question unless they terminate. Three termination\nfamilies organize the paper: pragmatic closure, selection closure, and\nfixed-point termination. They are organizing families, not an exhaustive\ntaxonomy of every possible explanation. A final upstream pass asks what comes\nbefore the first prior and answers with a constraint stack -- viability,\nallostasis, co-homeostasis, development, and niche support -- rather than with a\nhidden meta-prior. The project uses plain text, deterministic conceptual visualizations, and one deterministic illustrative simulation trace.\nIts supplement adds generated, auto-numbered formal claims and a closed symbol\nglossary as text-integrity artifacts rather than empirical machinery.\nThe paper includes one deterministic illustrative simulation trace over authored formal states; it does not run stochastic simulations, synthetic-data experiments, empirical estimates, or performance benchmarks.\n\nThe \"art\" in the title names the craft at stake: arranging explanatory kinds\n(proximate/ultimate, static/developmental, selection/fixed-point) so that no\nsingle kind is mistaken for the whole, and rendering that arrangement as\ndeterministic figures and checkable formalism rather than as empirical simulation evidence. It\nalso marks a bounded link to aesthetic practice: artworks can stage encounters\nwith expectations, material affordances, ambiguity, and meaning, but this paper\ntreats that link as a conceptual analogy, not as an empirical theory of art.\n\n---\nAssociated artifacts\nGitHub release: Prior Cognitive Art v0.1.0 (https://github.com/docxology/prior_cognitive_art/releases/tag/v0.1.0)\nDOI: https://doi.org/10.5281/zenodo.21316510\nZenodo: https://zenodo.org/records/21316510\nPDF SHA-256: 62aa1d8d4af75f4b5bdc894516720bdf61ae68a4be27f44aefc09c2dd2fbba67", "keywords": [ "priors", "Tinbergen's four questions", "hierarchical Bayes", "free energy principle", "Markov blankets", "cognitive science", "conceptual visualization" ], "doi": "10.5281/zenodo.21316510", "github_release_url": "https://github.com/docxology/prior_cognitive_art/releases/tag/v0.1.0" }, "2026_ActiveFractalRabbit": { "year": "2026", "topic": "ActiveFractalRabbit", "name": "Active FractalRabbit: A Synthetic Benchmark for Belief Filtering Under Sparse Waypoint Observations", "description": "Sparse waypoint analysis is privacy-sensitive: it must separate movement from irregular reporting, missingness, spatial coarsening, and corruption while preserving uncertainty about hidden location. Active FractalRabbit provides a controlled, artifact-bound benchmark whose headline lane uses a deterministic project-local synthetic FractalRabbit-format fixture; a separately retained lane exercises pinned open-source software from the National Security Agency as an independent simulator surface. The benchmark converts sporadic reports into categorical evidence, fits explicit hidden-state generative models, and compares transparent temporal, Markov, sequence, state-space, neural, latent-state, and active inference predictors under matched information sets. Under noisy partial-observability, Active Inference is the lowest-loss implemented predictor: it clearly leads point-estimate and raw-observation families and sits in a statistical tie with the strongest non-AIF belief-preserving comparator. The shared mechanism is soft Bayesian marginalization, which preserves probability across plausible cells instead of committing early to one state. Point estimates suffice for clean observations, an online base-rate predictor leads under regime switching, transparent temporal and disclosed kinematic controls anchor sparse reporting gaps, and withholding location sharply limits specific-cell recovery from metadata. The partially observable Markov decision process (POMDP) formulation also exposes variational and expected-free-energy diagnostics for belief, minimization, and integrity. These results establish a regime-specific synthetic model map and a reproducible evidence chain. The present contract covers synthetic software behavior; separate evidence protocols govern privacy and empirical evaluation. Code, fixtures, manuscript source, and the release manifest are public at github.com/ActiveInferenceInstitute/active_fractal_rabbit (https://github.com/ActiveInferenceInstitute/active_fractal_rabbit).\n\n---\nAssociated artifacts\nGitHub release: v0.2.0 (https://github.com/ActiveInferenceInstitute/active_fractal_rabbit/releases/tag/v0.2.0)\nDOI: https://doi.org/10.5281/zenodo.21330636\nZenodo: https://zenodo.org/records/21330636\nPDF SHA-256: d676159d149a12a1e990457329ad4cf52d2d8ab4ffcf09f55b42bad8e0c3c052", "authors": "Daniel Ari Friedman", "abstract": "Sparse waypoint analysis is privacy-sensitive: it must separate movement from irregular reporting, missingness, spatial coarsening, and corruption while preserving uncertainty about hidden location. Active FractalRabbit provides a controlled, artifact-bound benchmark whose headline lane uses a deterministic project-local synthetic FractalRabbit-format fixture; a separately retained lane exercises pinned open-source software from the National Security Agency as an independent simulator surface. The benchmark converts sporadic reports into categorical evidence, fits explicit hidden-state generative models, and compares transparent temporal, Markov, sequence, state-space, neural, latent-state, and active inference predictors under matched information sets. Under noisy partial-observability, Active Inference is the lowest-loss implemented predictor: it clearly leads point-estimate and raw-observation families and sits in a statistical tie with the strongest non-AIF belief-preserving comparator. The shared mechanism is soft Bayesian marginalization, which preserves probability across plausible cells instead of committing early to one state. Point estimates suffice for clean observations, an online base-rate predictor leads under regime switching, transparent temporal and disclosed kinematic controls anchor sparse reporting gaps, and withholding location sharply limits specific-cell recovery from metadata. The partially observable Markov decision process (POMDP) formulation also exposes variational and expected-free-energy diagnostics for belief, minimization, and integrity. These results establish a regime-specific synthetic model map and a reproducible evidence chain. The present contract covers synthetic software behavior; separate evidence protocols govern privacy and empirical evaluation. Code, fixtures, manuscript source, and the release manifest are public at github.com/ActiveInferenceInstitute/active_fractal_rabbit (https://github.com/ActiveInferenceInstitute/active_fractal_rabbit).\n\n---\nAssociated artifacts\nGitHub release: v0.2.0 (https://github.com/ActiveInferenceInstitute/active_fractal_rabbit/releases/tag/v0.2.0)\nDOI: https://doi.org/10.5281/zenodo.21330636\nZenodo: https://zenodo.org/records/21330636\nPDF SHA-256: d676159d149a12a1e990457329ad4cf52d2d8ab4ffcf09f55b42bad8e0c3c052", "keywords": [], "doi": "10.5281/zenodo.21330636", "github_release_url": "https://github.com/ActiveInferenceInstitute/active_fractal_rabbit/releases/tag/v0.2.0" }, "2026_FourfoldVision": { "year": "2026", "topic": "FourfoldVision", "name": "Fourfold Vision: William Blake, Buckminster Fuller, and the Geometry of Omnirational Seeing", "description": "Published by Synergetics University on 14 July 2026, The Fuller Conjecture asks whether \"there exists a line of reasoning that presents Fuller's synergetic vision as a philosophical system.\" This paper tests one qualified route through that problem. It places William Blake's fourfold vision beside Buckminster Fuller's Synergetics without claiming shared vocabulary, historical influence, or Blakean anticipation of Quadray: Blake supplies a historically situated problem of plural seeing, while Quadray and tetrahedral geometry supply a reproducible model of relational aspects, partial views, and reconstruction. The formal result is exact: for any non-degenerate convex tetrahedron observed by a strictly exterior camera, one frame exposes 1, 2, or 3 faces, never 4; an atlas can preserve all 4 across successive views. The project combines an argument graph, exact rational geometry, deterministic visualizations, a rights-aware corpus ledger, and a dependency-free viewer. \"Synchronous omni-rational form\" names this operational integration—a relational whole retained across partial views, sequences, source boundaries, and explicit limits—not a historical Blakean or Fullerian doctrine.", "authors": "Daniel Ari Friedman", "abstract": "Published by Synergetics University on 14 July 2026, The Fuller Conjecture asks whether \"there exists a line of reasoning that presents Fuller's synergetic vision as a philosophical system.\" This paper tests one qualified route through that problem. It places William Blake's fourfold vision beside Buckminster Fuller's Synergetics without claiming shared vocabulary, historical influence, or Blakean anticipation of Quadray: Blake supplies a historically situated problem of plural seeing, while Quadray and tetrahedral geometry supply a reproducible model of relational aspects, partial views, and reconstruction. The formal result is exact: for any non-degenerate convex tetrahedron observed by a strictly exterior camera, one frame exposes 1, 2, or 3 faces, never 4; an atlas can preserve all 4 across successive views. The project combines an argument graph, exact rational geometry, deterministic visualizations, a rights-aware corpus ledger, and a dependency-free viewer. \"Synchronous omni-rational form\" names this operational integration—a relational whole retained across partial views, sequences, source boundaries, and explicit limits—not a historical Blakean or Fullerian doctrine.", "keywords": [], "doi": "10.5281/zenodo.21388456" }, "2026_DuckRabbit": { "year": "2026", "topic": "DuckRabbit", "name": "DuckRabbit: Typed Multimodal Illusion Generator", "description": "DuckRabbit is typed, deterministic research software by Daniel Ari Friedman\n(Active Inference Institute) for constructing reproducible visual, auditory,\ntemporal, and audiovisual stimulus families. The intended public release will\nbe available at the following repository:\n\nhttps://github.com/docxology/DuckRabbit\n\nDuckRabbit is released under the MIT License.\n\nIts basic unit is an immutable request containing an illusion identifier,\nvalidated parameters, a seed, and an encoding specification. The request yields\na canonical artifact, objective media measurements, and a versioned provenance\nmanifest before any delivery codec is selected. This separation makes the\nstimulus a testable computational object while reserving claims about\nperception for controlled observer protocols.\n\nThe release situates its engineering choices within a deliberately broad\nhistorical foundation spanning Greek, Arabic/Islamicate, Chinese, and\nearly-modern work on optics, visual inference, and cross-sensory knowledge,\nalongside later psychophysics and illusion research. These traditions motivate\nquestions about construction, observation, and evidence; they are contextual\nprecedents, not evidence that a generated file reproduces an ancient,\nearly-modern, or clinical observation.\n\nVersion 0.5.0 contains 17 implemented\ngenerators and 18 catalog entries, supported by\n36 source records and 18 evidence records\nin a checked-in audit snapshot. The package generates\n15 publication figures and 10\nmachine-derived tables from the live registry. It also provides a typed\nobserver-study harness and a transparent synthetic diagnostic: a\nhand-specified feature observer with serialized weights, temperature, analytic\ncalibration, and human_data=false. No participant data are bundled; synthetic model output is explicitly nonhuman.\n\nDuckRabbit verifies physical stimulus properties, media round trips, hashes,\ntiming, and declared provenance. It does not infer a universal percept, effect\nsize, or cross-device perceptual equivalence from a generated artifact. Its\ncontribution is a reproducibility and epistemic boundary: source-backed family\ndescriptions, deterministic media facts, and future observer hypotheses remain\ndifferent typed records rather than being collapsed into one claim. DuckRabbit\nis software for reproducible stimulus construction and audit, not a claim that\na generated file alone produces a universal perceptual effect.\n\n---\nAssociated artifacts\nGitHub release: v0.5.0 (https://github.com/docxology/DuckRabbit/releases/tag/v0.5.0)\nDOI: https://doi.org/10.5281/zenodo.21419693\nZenodo: https://zenodo.org/records/21419693\nPDF SHA-256: 3afefaeeccb5616448cc6da83b5807dd996ccc96bfff113d635b8da140baea06", "authors": "Daniel Ari Friedman", "abstract": "DuckRabbit is typed, deterministic research software by Daniel Ari Friedman\n(Active Inference Institute) for constructing reproducible visual, auditory,\ntemporal, and audiovisual stimulus families. The intended public release will\nbe available at the following repository:\n\nhttps://github.com/docxology/DuckRabbit\n\nDuckRabbit is released under the MIT License.\n\nIts basic unit is an immutable request containing an illusion identifier,\nvalidated parameters, a seed, and an encoding specification. The request yields\na canonical artifact, objective media measurements, and a versioned provenance\nmanifest before any delivery codec is selected. This separation makes the\nstimulus a testable computational object while reserving claims about\nperception for controlled observer protocols.\n\nThe release situates its engineering choices within a deliberately broad\nhistorical foundation spanning Greek, Arabic/Islamicate, Chinese, and\nearly-modern work on optics, visual inference, and cross-sensory knowledge,\nalongside later psychophysics and illusion research. These traditions motivate\nquestions about construction, observation, and evidence; they are contextual\nprecedents, not evidence that a generated file reproduces an ancient,\nearly-modern, or clinical observation.\n\nVersion 0.5.0 contains 17 implemented\ngenerators and 18 catalog entries, supported by\n36 source records and 18 evidence records\nin a checked-in audit snapshot. The package generates\n15 publication figures and 10\nmachine-derived tables from the live registry. It also provides a typed\nobserver-study harness and a transparent synthetic diagnostic: a\nhand-specified feature observer with serialized weights, temperature, analytic\ncalibration, and human_data=false. No participant data are bundled; synthetic model output is explicitly nonhuman.\n\nDuckRabbit verifies physical stimulus properties, media round trips, hashes,\ntiming, and declared provenance. It does not infer a universal percept, effect\nsize, or cross-device perceptual equivalence from a generated artifact. Its\ncontribution is a reproducibility and epistemic boundary: source-backed family\ndescriptions, deterministic media facts, and future observer hypotheses remain\ndifferent typed records rather than being collapsed into one claim. DuckRabbit\nis software for reproducible stimulus construction and audit, not a claim that\na generated file alone produces a universal perceptual effect.\n\n---\nAssociated artifacts\nGitHub release: v0.5.0 (https://github.com/docxology/DuckRabbit/releases/tag/v0.5.0)\nDOI: https://doi.org/10.5281/zenodo.21419693\nZenodo: https://zenodo.org/records/21419693\nPDF SHA-256: 3afefaeeccb5616448cc6da83b5807dd996ccc96bfff113d635b8da140baea06", "keywords": [ "perceptual illusions", "cognitive taxonomy", "audio-visual stimuli", "deterministic generation", "typed parameters", "research software", "reproducible research", "psychophysics" ], "doi": "10.5281/zenodo.21419693", "github_release_url": "https://github.com/docxology/DuckRabbit/releases/tag/v0.5.0" }, "2026_SynthOBSFractiSynth": { "year": "2026", "topic": "SynthOBSFractiSynth", "name": "SynthOBS & FractiSynth: A Golden-Ratio OBS Broadcast Console and Native Transducer", "description": "A tested Python reference engine, native libobs plugin, and obspython bridge for a telemetry-driven broadcast console. Includes deterministic figures, a versioned live OBS evidence bundle, fail-closed telemetry contracts, and a research-grade technical design manuscript. 1217 tests passing, 96.09% coverage on src/synthobs. Source: https://github.com/docxology/SynthOBS/releases/tag/v1.618.0. This version replaces the PDF with one that carries its own DOI on the cover/citation page (the v1 PDF referenced the DOI only externally).", "authors": "Daniel Ari Friedman", "abstract": "A tested Python reference engine, native libobs plugin, and obspython bridge for a telemetry-driven broadcast console. Includes deterministic figures, a versioned live OBS evidence bundle, fail-closed telemetry contracts, and a research-grade technical design manuscript. 1217 tests passing, 96.09% coverage on src/synthobs. Source: https://github.com/docxology/SynthOBS/releases/tag/v1.618.0. This version replaces the PDF with one that carries its own DOI on the cover/citation page (the v1 PDF referenced the DOI only externally).", "keywords": [ "OBS Studio", "libobs", "reproducible research software", "space-weather telemetry", "real-time digital signal processing", "software provenance", "software citation" ], "doi": "10.5281/zenodo.21418782", "doi_url": "https://doi.org/10.5281/zenodo.21418782", "zenodo_record": "https://zenodo.org/records/21418782", "record_id": "21418953" }, "2026_ActiveInferencePower": { "year": "2026", "topic": "ActiveInferencePower", "name": "Active Inference Power Suite: Conditional Statistical Power under Controlled Generative Settings", "description": "Statistical power is an investigator-facing operating characteristic of an adaptive-study design. Before simulation, the investigator fixes an agent-side model, evaluator-side process, testing setting, policy, and replication plan. Each embedded agent acts only on visible history; simulated hidden truth is retained for scoring. active_inference_power makes that conditional estimand inspectable. The suite combines fixed-horizon procedures, analytic references, dependence/calibration experiments, and a discrete-state active-inference agent checked against a binary oracle. It extends this to action loops with sensing reliability, latent context, target choice, cost, and stopping. The study distinguishes model-relative posterior belief from calibrated p-value and likelihood-ratio e-process evidence. It compares Benjamini–Hochberg (BH) false discovery rate (FDR) procedures with family-wise error rate (FWER) alternatives, and separates either evidence object from online FDR procedure-specific accounting. Results are scenario-indexed finite-simulation estimates with Monte Carlo standard error (MCSE) and declared error, dependence, filtration, and optional-stopping boundaries; they do not assign a universal power value to an agent, task environment, or active inference. Instead, they support auditable comparisons among explicitly declared adaptive-study designs. Contracts, seed schedules, certificates, figures, claim ledger, and rendered manuscript form a linked evidence chain, allowing readers to trace each claim to its design and artifact. Source and release materials are available at the verified GitHub repository ActiveInferenceInstitute/active_inference_power.
\nActive Inference Power Suite v1.0.0 is a source-bound release of an adaptive-study design suite. It reports scenario- and policy-indexed finite-simulation operating characteristics; it does not claim a universal \"power of active inference.\"
\n24fa25a4f29affcfd92c8c001ff6487a0c36960c6b4f38ed4419984fc8743cbfThe uploaded PDF, exact tag-derived source archive, release manifest, renderer provenance, and final review receipt make the release auditable. The evidence boundary remains finite, scenario-specific simulation rather than a universal theorem or deployment claim.
", "authors": "Daniel Ari Friedman", "abstract": "Statistical power is an investigator-facing operating characteristic of an adaptive-study design. Before simulation, the investigator fixes an agent-side model, evaluator-side process, testing setting, policy, and replication plan. Each embedded agent acts only on visible history; simulated hidden truth is retained for scoring. active_inference_power makes that conditional estimand inspectable. The suite combines fixed-horizon procedures, analytic references, dependence/calibration experiments, and a discrete-state active-inference agent checked against a binary oracle. It extends this to action loops with sensing reliability, latent context, target choice, cost, and stopping. The study distinguishes model-relative posterior belief from calibrated p-value and likelihood-ratio e-process evidence. It compares Benjamini–Hochberg (BH) false discovery rate (FDR) procedures with family-wise error rate (FWER) alternatives, and separates either evidence object from online FDR procedure-specific accounting. Results are scenario-indexed finite-simulation estimates with Monte Carlo standard error (MCSE) and declared error, dependence, filtration, and optional-stopping boundaries; they do not assign a universal power value to an agent, task environment, or active inference. Instead, they support auditable comparisons among explicitly declared adaptive-study designs. Contracts, seed schedules, certificates, figures, claim ledger, and rendered manuscript form a linked evidence chain, allowing readers to trace each claim to its design and artifact. Source and release materials are available at the verified GitHub repository ActiveInferenceInstitute/active_inference_power.
\nActive Inference Power Suite v1.0.0 is a source-bound release of an adaptive-study design suite. It reports scenario- and policy-indexed finite-simulation operating characteristics; it does not claim a universal \"power of active inference.\"
\n24fa25a4f29affcfd92c8c001ff6487a0c36960c6b4f38ed4419984fc8743cbfThe uploaded PDF, exact tag-derived source archive, release manifest, renderer provenance, and final review receipt make the release auditable. The evidence boundary remains finite, scenario-specific simulation rather than a universal theorem or deployment claim.
", "keywords": [ "multiple testing", "false discovery rate", "Benjamini-Hochberg", "statistical power", "active inference", "pymdp", "sequential hypothesis testing", "reproducible research" ], "doi": "10.5281/zenodo.21695160", "github_release_url": "https://github.com/ActiveInferenceInstitute/active_inference_power/releases/tag/v1.0.0" }, "2026_WitnessRegister": { "year": "2026", "topic": "WitnessRegister", "name": "The Witness Register: Co-Registration Without Aggregation", "description": "A shared register that co-registers independent instruments' report envelopes without aggregating them. It stores each report's envelope verbatim, records cross-instrument relations as separate describing records, keeps history append-only and sealed, and — only when asked, for one declared next use — emits a bounded posture that always points back at the state that earned it. It never parses, compares, ranks, averages, or merges any instrument's native status. Subtitle: A shared register for line report envelopes that never ranks, merges, or overrides the instruments it holds Code is MIT licensed; prose and figures are CC BY 4.0. See LICENSE in the repository.", "authors": "Daniel Ari Friedman", "abstract": "A shared register that co-registers independent instruments' report envelopes without aggregating them. It stores each report's envelope verbatim, records cross-instrument relations as separate describing records, keeps history append-only and sealed, and — only when asked, for one declared next use — emits a bounded posture that always points back at the state that earned it. It never parses, compares, ranks, averages, or merges any instrument's native status. Subtitle: A shared register for line report envelopes that never ranks, merges, or overrides the instruments it holds Code is MIT licensed; prose and figures are CC BY 4.0. See LICENSE in the repository.", "keywords": [ "co-registration", "append-only log", "provenance", "non-compensatory decision rules", "boundary objects", "research infrastructure", "open science" ], "doi": "10.5281/zenodo.21754245" }, "2026_LineSet": { "year": "2026", "topic": "LineSet", "name": "The Line Set: Holding Instruments Apart", "description": "A thin reader that declares what a set of small evaluative instruments is, reads whichever sibling packages are installed, and checks one narrow property: that no two of them have given the same spelling to different things. It adds no instrument of its own and computes no aggregate; a legible reading says only that the declared vocabularies did not overlap. Subtitle: A Declaration, a Reader, and a Non-Overlap Contract for a Growing Set of Small Instruments Code is MIT licensed; prose and figures are CC BY 4.0. See LICENSE in the repository.", "authors": "Daniel Ari Friedman", "abstract": "A thin reader that declares what a set of small evaluative instruments is, reads whichever sibling packages are installed, and checks one narrow property: that no two of them have given the same spelling to different things. It adds no instrument of its own and computes no aggregate; a legible reading says only that the declared vocabularies did not overlap. Subtitle: A Declaration, a Reader, and a Non-Overlap Contract for a Growing Set of Small Instruments Code is MIT licensed; prose and figures are CC BY 4.0. See LICENSE in the repository.", "keywords": [ "modularity", "information hiding", "separation of concerns", "boundary objects", "namespace collision", "declarative registry", "reproducible review", "open science" ], "doi": "10.5281/zenodo.21754243" }, "2026_WhiteLine": { "year": "2026", "topic": "WhiteLine", "name": "White Line: A Typed Ledger for the Edge of the Claim", "description": "A typed ledger for absence: epistemic gaps, ethical restraint, and contemplative negative space. It records what is missing, withheld, or unresolved under a total caution order, decays stale namings past their review horizons, and refuses to infer why anything is absent. Withholding is recorded as a boundary, never mined as missing evidence. Subtitle: Keeping missing evidence, ethical boundaries, and open questions from becoming unsupported claims Code is MIT licensed; prose and figures are CC BY 4.0. See LICENSE in the repository.", "authors": "Daniel Ari Friedman", "abstract": "A typed ledger for absence: epistemic gaps, ethical restraint, and contemplative negative space. It records what is missing, withheld, or unresolved under a total caution order, decays stale namings past their review horizons, and refuses to infer why anything is absent. Withholding is recorded as a boundary, never mined as missing evidence. Subtitle: Keeping missing evidence, ethical boundaries, and open questions from becoming unsupported claims Code is MIT licensed; prose and figures are CC BY 4.0. See LICENSE in the repository.", "keywords": [ "absence", "epistemic gaps", "missing evidence", "withheld material", "uncertainty", "negative results", "research ethics", "open science" ], "doi": "10.5281/zenodo.21754241" }, "2026_PersonalRedLines": { "year": "2026", "topic": "PersonalRedLines", "name": "Personal Red Lines for Development", "description": "A versioned, evidence-gated personal security boundary and explicit No document for dual-use development work. It requires a complete, reviewable action intake before returning compliance, distinguishes outside-scope work from compliance, and records uncertainty as a blocking result rather than a permission. It is a personal auditability aid, not enforcement, legal compliance, semantic safety classification, or external certification. Subtitle: An Evidence-Gated Personal Security Boundary and Explicit No Document for Dual-Use Development Code is MIT licensed; prose and figures are CC BY 4.0. See LICENSE in the repository.", "authors": "Daniel Ari Friedman", "abstract": "A versioned, evidence-gated personal security boundary and explicit No document for dual-use development work. It requires a complete, reviewable action intake before returning compliance, distinguishes outside-scope work from compliance, and records uncertainty as a blocking result rather than a permission. It is a personal auditability aid, not enforcement, legal compliance, semantic safety classification, or external certification. Subtitle: An Evidence-Gated Personal Security Boundary and Explicit No Document for Dual-Use Development Code is MIT licensed; prose and figures are CC BY 4.0. See LICENSE in the repository.", "keywords": [ "red lines", "AI governance", "dual-use", "cognitive security", "hash-based canary", "precommitment", "open science", "personal governance", "global political thought", "research ethics", "sociotechnical systems" ], "doi": "10.5281/zenodo.21754239" }, "2026_GoldenLine": { "year": "2026", "topic": "GoldenLine", "name": "Golden Line: Toward What Matters", "description": "A directional instrument for recording long-horizon aspirations and observable movement toward them. It returns one of four directional readings per aspiration against a versioned registry and deliberately computes no aggregate: there is no virtue score, and NOT_OBSERVED means no valid entry was admitted rather than that nobody looked. Subtitle: An Aspirational Thread for Long-Horizon Work Code is MIT licensed; prose and figures are CC BY 4.0. See LICENSE in the repository.", "authors": "Daniel Ari Friedman", "abstract": "A directional instrument for recording long-horizon aspirations and observable movement toward them. It returns one of four directional readings per aspiration against a versioned registry and deliberately computes no aggregate: there is no virtue score, and NOT_OBSERVED means no valid entry was admitted rather than that nobody looked. Subtitle: An Aspirational Thread for Long-Horizon Work Code is MIT licensed; prose and figures are CC BY 4.0. See LICENSE in the repository.", "keywords": [ "aspiration", "long-horizon work", "directional assessment", "values in practice", "research ethics", "open science", "non-compensatory reading" ], "doi": "10.5281/zenodo.21754237" }, "2026_BlackLine": { "year": "2026", "topic": "BlackLine", "name": "Black Line: Strong Work in Public", "description": "A positive practice instrument for concise, inspectable, revisable work. It reads self-declared tags and evidence labels against a versioned practice registry and returns one of four statuses over declaration coverage. It measures whether the evidence a practice asks for was declared — never whether the work is true, good, or permitted; an ALIGNED reading authorizes nothing. Subtitle: A Positive Operating Discipline for Concise, Rigorous Research and Engineering Code is MIT licensed; prose and figures are CC BY 4.0. See LICENSE in the repository.", "authors": "Daniel Ari Friedman", "abstract": "A positive practice instrument for concise, inspectable, revisable work. It reads self-declared tags and evidence labels against a versioned practice registry and returns one of four statuses over declaration coverage. It measures whether the evidence a practice asks for was declared — never whether the work is true, good, or permitted; an ALIGNED reading authorizes nothing. Subtitle: A Positive Operating Discipline for Concise, Rigorous Research and Engineering Code is MIT licensed; prose and figures are CC BY 4.0. See LICENSE in the repository.", "keywords": [ "research practice", "declaration coverage", "evidence discipline", "scientific integrity", "reproducibility", "open science", "engineering practice" ], "doi": "10.5281/zenodo.21754235" }, "2026_Codomyrmex": { "year": "2026", "topic": "Codomyrmex", "name": "Codomyrmex: An Artificial Ecology for Agentic Software Development", "description": "Agentic software can preserve task state while still forgetting the consequences of prior actions. Codomyrmex studies a narrow control-plane question: after a caller reports a failed action at one software location, can the system deterministically increase friction for a materially similar proposal at that location without changing an unrelated target? Its Colony Control Plane records consequence reports and couples them to target-indexed signal pressure, agent trust, role labels, resource accounting, adversarial checks, and an explicit EXECUTE/HOLD/REFUSE gate. The implementation comprises 8 cooperating subsystems. The ordinary Model Context Protocol path remains caller-reported and unattested. Optional and required `ColonyKernel` attestation modes instead bind proposal, verdict, authorization, execution receipt, and outcome in a signed, hash-linked local ledger. That ledger protects lifecycle linkage but does not independently observe external actuation or establish deployment safety. Consequence records can use file-backed SQLite; the default MCP kernel and signal field remain process-local. Evaluation is limited to implementation properties and controlled fixtures. At composition time, the scoped Colony Kernel surface contains 819 passing tests with 76.6% branch coverage, 0 Ruff errors, and 0 ty diagnostics. A paired deterministic replay moves the same-target proposal from 0.875/EXECUTE to 0.725/HOLD after a reported failure while leaving an unrelated target unchanged. Separate fixtures exercise trust promotion, bounded arithmetic, linear signal decay, local attestation integrity, and interface behavior. These results support reproducible software contracts, not ecological optimality, calibrated risk, production harm reduction, or generalization to external workloads. The report contributes the typed control plane, transparent gate, coupled local feedback, authenticated local lifecycle option, and source-bound publication workflow. Generated variables, figures, citations, claim boundaries, and release receipts tie the rendered report to the evaluated checkout. End-to-end external-actuation attestation, restart-persistent field storage, representative benchmarks, and independent deployment validation remain open.\n\n---\nAssociated artifacts\nGitHub release: v1.3.0-paper (https://github.com/docxology/codomyrmex/releases/tag/v1.3.0-paper)\nDOI: https://doi.org/10.5281/zenodo.21750801\nZenodo: https://zenodo.org/records/21750801\nPDF SHA-256: eda76ad12a50bce01b113894c785e0915b6ba367f5bf67d17c8f586416102b93\\nConcept DOI: https://doi.org/10.5281/zenodo.21750800\\nVersion DOI: https://doi.org/10.5281/zenodo.21750801", "authors": "Daniel Ari Friedman", "abstract": "Agentic software can preserve task state while still forgetting the consequences of prior actions. Codomyrmex studies a narrow control-plane question: after a caller reports a failed action at one software location, can the system deterministically increase friction for a materially similar proposal at that location without changing an unrelated target? Its Colony Control Plane records consequence reports and couples them to target-indexed signal pressure, agent trust, role labels, resource accounting, adversarial checks, and an explicit EXECUTE/HOLD/REFUSE gate. The implementation comprises 8 cooperating subsystems. The ordinary Model Context Protocol path remains caller-reported and unattested. Optional and required `ColonyKernel` attestation modes instead bind proposal, verdict, authorization, execution receipt, and outcome in a signed, hash-linked local ledger. That ledger protects lifecycle linkage but does not independently observe external actuation or establish deployment safety. Consequence records can use file-backed SQLite; the default MCP kernel and signal field remain process-local. Evaluation is limited to implementation properties and controlled fixtures. At composition time, the scoped Colony Kernel surface contains 819 passing tests with 76.6% branch coverage, 0 Ruff errors, and 0 ty diagnostics. A paired deterministic replay moves the same-target proposal from 0.875/EXECUTE to 0.725/HOLD after a reported failure while leaving an unrelated target unchanged. Separate fixtures exercise trust promotion, bounded arithmetic, linear signal decay, local attestation integrity, and interface behavior. These results support reproducible software contracts, not ecological optimality, calibrated risk, production harm reduction, or generalization to external workloads. The report contributes the typed control plane, transparent gate, coupled local feedback, authenticated local lifecycle option, and source-bound publication workflow. Generated variables, figures, citations, claim boundaries, and release receipts tie the rendered report to the evaluated checkout. End-to-end external-actuation attestation, restart-persistent field storage, representative benchmarks, and independent deployment validation remain open.\n\n---\nAssociated artifacts\nGitHub release: v1.3.0-paper (https://github.com/docxology/codomyrmex/releases/tag/v1.3.0-paper)\nDOI: https://doi.org/10.5281/zenodo.21750801\nZenodo: https://zenodo.org/records/21750801\nPDF SHA-256: eda76ad12a50bce01b113894c785e0915b6ba367f5bf67d17c8f586416102b93\\nConcept DOI: https://doi.org/10.5281/zenodo.21750800\\nVersion DOI: https://doi.org/10.5281/zenodo.21750801", "keywords": [ "ai-agents", "model-context-protocol", "mcp", "multi-agent", "orchestration", "colony-control-plane", "stigmergy", "artificial-ecology", "agentic-software-engineering", "falsification-worker", "actuation-gate", "trust-scoring" ], "doi": "10.5281/zenodo.21750800", "github_release_url": "https://github.com/docxology/codomyrmex/releases/tag/v1.3.0-paper" }, "2026_THALIA": { "year": "2026", "topic": "THALIA", "name": "THALIA: Typed Harness with Analytical Lexical-Integrated Architecture", "description": "THALIA is an executable research harness for long-context memory systems. It combines typed stage contracts, inspectable context selection, evidence-preserving episodic state, lexical-first retrieval, and bounded compiler search with source-bound evaluation and reproducible artifact checks. The deterministic results are finite diagnostic evidence; model-quality, integrity-advantage, and production-readiness claims remain separately gated.", "authors": "Daniel Ari Friedman", "abstract": "THALIA is an executable research harness for long-context memory systems. It combines typed stage contracts, inspectable context selection, evidence-preserving episodic state, lexical-first retrieval, and bounded compiler search with source-bound evaluation and reproducible artifact checks. The deterministic results are finite diagnostic evidence; model-quality, integrity-advantage, and production-readiness claims remain separately gated.", "keywords": [ "agentic systems", "long-context memory", "retrieval-augmented generation", "reproducible research" ], "doi": "10.5281/zenodo.21763244" }, "2026_PROJECTBONDSpecial": { "year": "2026", "topic": "PROJECTBONDSpecial", "name": "PROJECT BOND — The Special-Agent Operations Compendium", "description": "PROJECT BOND is a fleet of 33 independent software packages — 27 film packages, one per James Bond motion picture, plus 6 mission-infrastructure packages (a Q-branch utilities layer, a frozen mission protocol, mission control, an orchestrator, a front-door CLI, and fleet operations). Each film package implements, as real tested algorithms, the concepts of its film: radiation forensics for Dr. No, gold-market cornering for Goldfinger, orbital-EMP modeling for GoldenEye, DNA-targeted bioweapon countermeasure modeling for No Time to Die.\n\nThe public source repository for the suite and this manuscript is\nhttps://github.com/docxology/bond. The repository will carry the source\nhistory, reproducibility instructions, generated release artifacts, and the\ncross-linked Zenodo record for this publication.\n\nThis compendium is the wrapper manuscript for the whole suite. It does not re-derive the packages' content by hand; it imports each of the 33 package manuscripts in full — every package's abstract, every authored section (introduction, methodology, results, conclusion, experimental setup, reproducibility, and scope), and its figures — framing them with this abstract, a generated suite-architecture chapter, and a closing synthesis. A unified bibliography merges every package's references into a single references.bib, and the combined PDF carries all 27 film chapters and 6 infrastructure chapters in their entirety.\n\nThe suite is \"special-agent material\" by construction: deterministic, no-mock, ≥90%-coverage-tested packages; provenance on every mission outcome; a frozen protocol every film implements; and cross-film missions (OPERATION OMNIBUS) executed over the real packages.\n\n---\nAssociated artifacts\nGitHub release: PROJECT BOND v1.0.0 (https://github.com/docxology/bond/releases/tag/v1.0.0)\nDOI: https://doi.org/10.5281/zenodo.21843802\nZenodo: https://zenodo.org/records/21843802\nPDF SHA-256: 72634276f9a90f1318be80ff0ece561ace2ead85559d2c05a11b86915e61fa0e", "authors": "Daniel Ari Friedman", "abstract": "PROJECT BOND is a fleet of 33 independent software packages — 27 film packages, one per James Bond motion picture, plus 6 mission-infrastructure packages (a Q-branch utilities layer, a frozen mission protocol, mission control, an orchestrator, a front-door CLI, and fleet operations). Each film package implements, as real tested algorithms, the concepts of its film: radiation forensics for Dr. No, gold-market cornering for Goldfinger, orbital-EMP modeling for GoldenEye, DNA-targeted bioweapon countermeasure modeling for No Time to Die.\n\nThe public source repository for the suite and this manuscript is\nhttps://github.com/docxology/bond. The repository will carry the source\nhistory, reproducibility instructions, generated release artifacts, and the\ncross-linked Zenodo record for this publication.\n\nThis compendium is the wrapper manuscript for the whole suite. It does not re-derive the packages' content by hand; it imports each of the 33 package manuscripts in full — every package's abstract, every authored section (introduction, methodology, results, conclusion, experimental setup, reproducibility, and scope), and its figures — framing them with this abstract, a generated suite-architecture chapter, and a closing synthesis. A unified bibliography merges every package's references into a single references.bib, and the combined PDF carries all 27 film chapters and 6 infrastructure chapters in their entirety.\n\nThe suite is \"special-agent material\" by construction: deterministic, no-mock, ≥90%-coverage-tested packages; provenance on every mission outcome; a frozen protocol every film implements; and cross-film missions (OPERATION OMNIBUS) executed over the real packages.\n\n---\nAssociated artifacts\nGitHub release: PROJECT BOND v1.0.0 (https://github.com/docxology/bond/releases/tag/v1.0.0)\nDOI: https://doi.org/10.5281/zenodo.21843802\nZenodo: https://zenodo.org/records/21843802\nPDF SHA-256: 72634276f9a90f1318be80ff0ece561ace2ead85559d2c05a11b86915e61fa0e", "keywords": [ "software suite", "special agent operations", "James Bond", "reproducible research", "mission protocol", "compendium" ], "doi": "10.5281/zenodo.21843592", "github_release_url": "https://github.com/docxology/bond/releases/tag/v1.0.0" }, "2026_DigiPPPiP": { "year": "2026", "topic": "DigiPPPiP", "name": "DigiPPPiP: Digital Partner Pen Play in Parallel", "description": "DigiPPPiP extends Partner Pen Play in Parallel (PPPiP) from a co-present paper practice into a reproducible framework for cyberphysical, remote, semisynchronous, asynchronous, accessible, and place-responsive dyadic drawing . The framework is positioned within collaborative-work studies of shared drawing surfaces , cyber-physical-social systems , mediated embodiment and presence , digital intimacy technologies , and cautious hyperscanning methodology .\n\nThe manuscript translates the project brief into a modular research artifact: 9 temporal-spatial modalities, 13 evidence dimensions, 6 planned outcome measures, and 39 conceptual figures are generated or registered by the project code rather than hand-maintained prose. The computational layer is explicitly illustrative, not empirical: the active-inference, inter-brain-synchrony, Forman-Ricci, narrative-information, source-quality, accessibility, and neuroergonomic outputs are deterministic conceptual models designed to make theoretical commitments inspectable. Formal equations are consolidated in so the main line can state evidence boundaries before presenting mathematical detail.\n\nThe argument is that DigiPPPiP should be treated as human-human relational technology. Its irreducible design kernel is modest: two partners, a shared mark field, perceptible traces of agency, a temporal relation among contributions, and consentful control over persistence. Digital infrastructure expands the substrate of shared mark-making without replacing the second-person, embodied, narrative, and affective conditions that make the practice relationally meaningful . The manuscript explicitly avoids claims that DigiPPPiP is clinically therapeutic, universally accessible, or causally validated by neural synchrony; those remain empirical questions for controlled studies. Cross-references use Pandoc labels, so sections, equations, figures, and tables remain automatically numbered across PDF and web render targets.\n\n---\nAssociated artifacts\nGitHub release: v1.0.0 (https://github.com/docxology/Digi-PPPiP/releases/tag/v1.0.0)\nDOI: https://doi.org/10.5281/zenodo.21815704\nZenodo: https://zenodo.org/records/21815704\nPDF SHA-256: f5ca95ce77c7c5367c0a0e68d5b4a640f0865b4c2b0827fa78ff1babbcbd986d", "authors": "Siddhant Shrivastava, Evelyn C. Goh, Alexandra Mikhailova, Daniel Ari Friedman", "abstract": "DigiPPPiP extends Partner Pen Play in Parallel (PPPiP) from a co-present paper practice into a reproducible framework for cyberphysical, remote, semisynchronous, asynchronous, accessible, and place-responsive dyadic drawing . The framework is positioned within collaborative-work studies of shared drawing surfaces , cyber-physical-social systems , mediated embodiment and presence , digital intimacy technologies , and cautious hyperscanning methodology .\n\nThe manuscript translates the project brief into a modular research artifact: 9 temporal-spatial modalities, 13 evidence dimensions, 6 planned outcome measures, and 39 conceptual figures are generated or registered by the project code rather than hand-maintained prose. The computational layer is explicitly illustrative, not empirical: the active-inference, inter-brain-synchrony, Forman-Ricci, narrative-information, source-quality, accessibility, and neuroergonomic outputs are deterministic conceptual models designed to make theoretical commitments inspectable. Formal equations are consolidated in so the main line can state evidence boundaries before presenting mathematical detail.\n\nThe argument is that DigiPPPiP should be treated as human-human relational technology. Its irreducible design kernel is modest: two partners, a shared mark field, perceptible traces of agency, a temporal relation among contributions, and consentful control over persistence. Digital infrastructure expands the substrate of shared mark-making without replacing the second-person, embodied, narrative, and affective conditions that make the practice relationally meaningful . The manuscript explicitly avoids claims that DigiPPPiP is clinically therapeutic, universally accessible, or causally validated by neural synchrony; those remain empirical questions for controlled studies. Cross-references use Pandoc labels, so sections, equations, figures, and tables remain automatically numbered across PDF and web render targets.\n\n---\nAssociated artifacts\nGitHub release: v1.0.0 (https://github.com/docxology/Digi-PPPiP/releases/tag/v1.0.0)\nDOI: https://doi.org/10.5281/zenodo.21815704\nZenodo: https://zenodo.org/records/21815704\nPDF SHA-256: f5ca95ce77c7c5367c0a0e68d5b4a640f0865b4c2b0827fa78ff1babbcbd986d", "keywords": [ "partner pen play in parallel", "active inference", "inter-brain synchrony", "geometric hyperscanning", "narrative information theory", "neuroergonomics", "digital placemaking", "relational technology", "reproducible research" ], "doi": "10.5281/zenodo.21815704", "github_release_url": "https://github.com/docxology/Digi-PPPiP/releases/tag/v1.0.0" }, "2026_RobustBeliefSharing": { "year": "2026", "topic": "RobustBeliefSharing", "name": "Robust Belief Sharing in Federated Active Inference: A Recovery-Tested Generalized-Variational Framework for Categorical Contamination-Aware Consensus", "description": "Multi-agent active inference gives a natural account of belief sharing: agents hold local posteriors over a shared latent state, communicate those beliefs, and pool them into a colony-level consensus. The same mechanism is fragile when a member is miscalibrated, corrupted, or strategically wrong. Because the standard pool multiplies the reports together, a single confident-but-wrong broadcast that puts near-zero mass on the true state can pull the whole consensus off it, outweighing many honest members. The colony therefore needs a way to preserve the useful structure of belief sharing while limiting the influence of contaminated beliefs. This paper presents Active Fedference, a discrete-categorical framework that connects robust federated generalized variational inference with active inference belief sharing. The main bridge is structural: standard belief sharing appears as the non-robust corner of a broader generalized-Bayes family, while robust losses, conservative server fusion, and explicit aggregation diagnostics describe how the system moves away from that corner under declared contamination mechanisms. The result is not a replacement for belief sharing, but a containment result: ordinary belief sharing is recovered when robustness is turned off. Bounded-loss theory applies on the client axis, while the variational-server axis supplies an objective-backed redescending weight update. The manuscript separates three robustness axes that are often conflated. First, client-side generalized-Bayes updates change how each agent absorbs evidence; this is the rigorous axis, carrying FedGVI’s bounded-influence result only under the source theorem’s loss, model, and contamination assumptions. Second, a sharp server-side reweighting heuristic suppresses beliefs that pull away from the emerging consensus, while carrying only its recovery-limit guarantee — no proven objective and no bounded-influence bound. Third, a variational aggregation rule supplies a more conservative objective-backed server alternative, with a raw effective-weight bound but not an estimator-level bounded-influence proof for the normalized consensus. Keeping these axes separate lets the paper state exactly which claims are proven, which are empirical, and which remain engineering extensions. The study suite then exercises the framework as an end-to-end research system: recovery checks anchor the standard-Bayes limit, belief-sharing studies verify the communication baseline, contamination experiments test robust consensus, and extension studies probe moving agents, hierarchical latent structure, sensitivity to acuity and colony size, parameter recovery, and single-host socket-backed federation traces. All reported quantities are generated from deterministic analysis artifacts and injected into the manuscript by token, so the paper, figures, release package, and validation reports remain tied to the same execution record. The open-source repository is ActiveInferenceInstitute/Active_Fedference . The production Zenodo release DOI is 10.5281/zenodo.21864004, and the repository and deposited PDF point to each other through this DOI and the repository URL.", "authors": "Daniel Ari Friedman", "abstract": "Multi-agent active inference gives a natural account of belief sharing: agents hold local posteriors over a shared latent state, communicate those beliefs, and pool them into a colony-level consensus. The same mechanism is fragile when a member is miscalibrated, corrupted, or strategically wrong. Because the standard pool multiplies the reports together, a single confident-but-wrong broadcast that puts near-zero mass on the true state can pull the whole consensus off it, outweighing many honest members. The colony therefore needs a way to preserve the useful structure of belief sharing while limiting the influence of contaminated beliefs. This paper presents Active Fedference, a discrete-categorical framework that connects robust federated generalized variational inference with active inference belief sharing. The main bridge is structural: standard belief sharing appears as the non-robust corner of a broader generalized-Bayes family, while robust losses, conservative server fusion, and explicit aggregation diagnostics describe how the system moves away from that corner under declared contamination mechanisms. The result is not a replacement for belief sharing, but a containment result: ordinary belief sharing is recovered when robustness is turned off. Bounded-loss theory applies on the client axis, while the variational-server axis supplies an objective-backed redescending weight update. The manuscript separates three robustness axes that are often conflated. First, client-side generalized-Bayes updates change how each agent absorbs evidence; this is the rigorous axis, carrying FedGVI’s bounded-influence result only under the source theorem’s loss, model, and contamination assumptions. Second, a sharp server-side reweighting heuristic suppresses beliefs that pull away from the emerging consensus, while carrying only its recovery-limit guarantee — no proven objective and no bounded-influence bound. Third, a variational aggregation rule supplies a more conservative objective-backed server alternative, with a raw effective-weight bound but not an estimator-level bounded-influence proof for the normalized consensus. Keeping these axes separate lets the paper state exactly which claims are proven, which are empirical, and which remain engineering extensions. The study suite then exercises the framework as an end-to-end research system: recovery checks anchor the standard-Bayes limit, belief-sharing studies verify the communication baseline, contamination experiments test robust consensus, and extension studies probe moving agents, hierarchical latent structure, sensitivity to acuity and colony size, parameter recovery, and single-host socket-backed federation traces. All reported quantities are generated from deterministic analysis artifacts and injected into the manuscript by token, so the paper, figures, release package, and validation reports remain tied to the same execution record. The open-source repository is ActiveInferenceInstitute/Active_Fedference . The production Zenodo release DOI is 10.5281/zenodo.21864004, and the repository and deposited PDF point to each other through this DOI and the repository URL.", "keywords": [ "active inference", "federated learning", "generalised variational inference", "belief sharing", "robustness", "FedGVI" ], "doi": "10.5281/zenodo.21864003" }, "2026_ActiveSkillference": { "year": "2026", "topic": "ActiveSkillference", "name": "Active Skillference: A Validated Prerequisite Graph, Computational Claim Registry, and SkillTree Delivery Contract", "description": "Active Inference and the Free Energy Principle (FEP) provide model-based accounts of belief updating, learning, and action under uncertainty. We present Active Skillference, a provenance-bound curriculum-generation and SkillTree-export system for teaching those formal ideas. The paper evaluates structural validity, quantitative provenance, citation-role coverage, and artifact reproducibility; it does not evaluate learner outcomes, establish a new theory of Active Inference, or present an intelligent tutoring system. The curriculum is expressed as code: a typed, validated directed acyclic graph of 630 skills across 111 subjects spanning all 8 strata (mathematics -> probability -> information theory -> variational methods -> the FEP -> active inference -> computation -> applications), connected by 1199 prerequisite edges with a maximum dependency depth of 75 (of which the substantive concept chain accounts for 33; the remaining depth is per-stratum review and mastery sequencing rather than conceptual prerequisite, as the methodology details). Its defining feature is content-provenance binding: every quantitative value shown to a learner is produced by a tested computational kernel and inserted through a typed claim token, never hand-typed, and the build refuses to export if a claim is unbacked or if a bare result number appears in learner prose, manuscript prose, or correct numeric quiz answers. The contribution is therefore a systems and curriculum-infrastructure artifact: it makes a formal subject inspectable and deliverable, but does not claim that the resulting path is optimal for every learner. The validated graph exports directly into SkillTree’s data model (Project -> Subjects -> Skills with learning-path dependencies and quiz-gated completion), includes a scripted REST seeding path for a configured instance, and is mirrored by a local dashboard that exposes generated artifacts, figures, claim ledgers, scholarship audits, and graph diagnostics without taking ownership of learner progress or scoring from SkillTree. The result is a curriculum with a validator-backed artifact chain: re-running the kernels regenerates the claim ledger, figures, manuscript variables, SkillTree export, and learner-facing numbers, so the platform’s teaching claims remain bounded by what the code, citations, validators, and documented limitations actually support.", "authors": "Daniel Ari Friedman", "abstract": "Active Inference and the Free Energy Principle (FEP) provide model-based accounts of belief updating, learning, and action under uncertainty. We present Active Skillference, a provenance-bound curriculum-generation and SkillTree-export system for teaching those formal ideas. The paper evaluates structural validity, quantitative provenance, citation-role coverage, and artifact reproducibility; it does not evaluate learner outcomes, establish a new theory of Active Inference, or present an intelligent tutoring system. The curriculum is expressed as code: a typed, validated directed acyclic graph of 630 skills across 111 subjects spanning all 8 strata (mathematics -> probability -> information theory -> variational methods -> the FEP -> active inference -> computation -> applications), connected by 1199 prerequisite edges with a maximum dependency depth of 75 (of which the substantive concept chain accounts for 33; the remaining depth is per-stratum review and mastery sequencing rather than conceptual prerequisite, as the methodology details). Its defining feature is content-provenance binding: every quantitative value shown to a learner is produced by a tested computational kernel and inserted through a typed claim token, never hand-typed, and the build refuses to export if a claim is unbacked or if a bare result number appears in learner prose, manuscript prose, or correct numeric quiz answers. The contribution is therefore a systems and curriculum-infrastructure artifact: it makes a formal subject inspectable and deliverable, but does not claim that the resulting path is optimal for every learner. The validated graph exports directly into SkillTree’s data model (Project -> Subjects -> Skills with learning-path dependencies and quiz-gated completion), includes a scripted REST seeding path for a configured instance, and is mirrored by a local dashboard that exposes generated artifacts, figures, claim ledgers, scholarship audits, and graph diagnostics without taking ownership of learner progress or scoring from SkillTree. The result is a curriculum with a validator-backed artifact chain: re-running the kernels regenerates the claim ledger, figures, manuscript variables, SkillTree export, and learner-facing numbers, so the platform’s teaching claims remain bounded by what the code, citations, validators, and documented limitations actually support.", "keywords": [ "active inference", "free energy principle", "variational inference", "Bayesian inference", "information theory", "curriculum", "prerequisite graph", "SkillTree", "computational provenance", "micro-learning", "reproducible research" ], "doi": "10.5281/zenodo.21865643" }, "2026_DocxplusIntelligentDocument": { "year": "2026", "topic": "DocxplusIntelligentDocument", "name": "docxplus — the Intelligent Document Container", "description": "A standards-first reference implementation of a byte-valid OOXML .docx that also carries a modular, Ed25519-signed, AES-256-GCM-encrypted intelligence layer through spec-sanctioned side-channels, with an optional bridge to the docxology/steganographer signed-packet backend.", "authors": "Daniel Ari Friedman", "abstract": "A standards-first reference implementation of a byte-valid OOXML .docx that also carries a modular, Ed25519-signed, AES-256-GCM-encrypted intelligence layer through spec-sanctioned side-channels, with an optional bridge to the docxology/steganographer signed-packet backend.", "keywords": [ "OOXML", "DOCX", "Open Packaging Conventions", "steganography", "document security", "reproducible research" ], "doi": "10.5281/zenodo.21983948", "github_release_url": "https://github.com/docxology/docxplus/releases/tag/v1.0.1" }, "2026_CognitiveIntegrityFramework": { "year": "2026", "topic": "CognitiveIntegrityFramework", "name": "Cognitive Integrity Framework: Computational Validation and Empirical Analysis (Part 2 of 3: Implementation, Empirical Analysis, and Adversarial Evaluation)", "description": "This paper presents the computational validation of the Cognitive Integrity Framework (CIF) whose formal foundations are established in Part 1 (DOI: 10.5281/zenodo.18364119). We implement the CIF defense suite --- cognitive firewalls, belief sandboxes, tripwires, drift and anomaly scoring, trust calculus with bounded delegation, provenance tracking, and Byzantine-tolerant consensus --- and evaluate it on an integrated 1,475-item attack corpus spanning fifteen categories, together with a 120-item benign corpus whose harder half carries attack-adjacent vocabulary. Multi-tier evaluation. Real pipeline evaluation across 30 seeds yields a mean detection rate of 86.3% (95% CI 85.5%-87.1%) at an 18.5% false-positive rate on the Claude Code architecture, measured on an injection-only 100-sample-per-seed arm. LLM-backed validation (N=10, Gemma 3 4B) reaches 80-100% across two topologies and is reported as preliminary and underpowered. Colony benchmarks at 20-100 agents reach 81-100% on structured adversarial scenarios, with emergent misalignment the hardest case at 74.3% detection and a 25.5% false-positive rate. Parametric simulation (N=3,800) characterises a 96-100% design-level ceiling across four production architectures, and is labelled a simulation rather than a measurement wherever it appears. Ablation on a 100-attack corpus attributes almost all marginal detection to the Invariants module: the full pipeline reaches 89.0% true-positive rate, removing Invariants costs 65 percentage points, removing the Tripwire costs two, and removing any other module costs nothing the corpus can measure. The layered architecture is therefore only partly borne out, and the paper says so. An undefended control arm places the defense's cost at +0.610 ms at the median and +32 KiB peak. Measured as ranked scorers, the drift score and the firewall pattern matcher fall below chance (AUC 0.374 and 0.383, intervals excluding 0.5). Every quantity the three papers share is derived from a single ledger and gated in continuous integration; no reported number is typed by hand. All experiments are deterministically reproducible at seed 42, and the code, corpora and artifacts are at https://github.com/docxology/cognitive_integrity. This is Part 2 of the three-part Cognitive Security for Multiagent Operators series: - Part 1 (DOI: 10.5281/zenodo.18364119): formal foundations and theoretical analysis - Part 2 (this paper): computational validation and implementation - Part 3+4 merged (DOI: 10.5281/zenodo.18364130): unified practitioner guidance and cross-domain applications", "authors": "Daniel Ari Friedman", "abstract": "This paper presents the computational validation of the Cognitive Integrity Framework (CIF) whose formal foundations are established in Part 1 (DOI: 10.5281/zenodo.18364119). We implement the CIF defense suite --- cognitive firewalls, belief sandboxes, tripwires, drift and anomaly scoring, trust calculus with bounded delegation, provenance tracking, and Byzantine-tolerant consensus --- and evaluate it on an integrated 1,475-item attack corpus spanning fifteen categories, together with a 120-item benign corpus whose harder half carries attack-adjacent vocabulary. Multi-tier evaluation. Real pipeline evaluation across 30 seeds yields a mean detection rate of 86.3% (95% CI 85.5%-87.1%) at an 18.5% false-positive rate on the Claude Code architecture, measured on an injection-only 100-sample-per-seed arm. LLM-backed validation (N=10, Gemma 3 4B) reaches 80-100% across two topologies and is reported as preliminary and underpowered. Colony benchmarks at 20-100 agents reach 81-100% on structured adversarial scenarios, with emergent misalignment the hardest case at 74.3% detection and a 25.5% false-positive rate. Parametric simulation (N=3,800) characterises a 96-100% design-level ceiling across four production architectures, and is labelled a simulation rather than a measurement wherever it appears. Ablation on a 100-attack corpus attributes almost all marginal detection to the Invariants module: the full pipeline reaches 89.0% true-positive rate, removing Invariants costs 65 percentage points, removing the Tripwire costs two, and removing any other module costs nothing the corpus can measure. The layered architecture is therefore only partly borne out, and the paper says so. An undefended control arm places the defense's cost at +0.610 ms at the median and +32 KiB peak. Measured as ranked scorers, the drift score and the firewall pattern matcher fall below chance (AUC 0.374 and 0.383, intervals excluding 0.5). Every quantity the three papers share is derived from a single ledger and gated in continuous integration; no reported number is typed by hand. All experiments are deterministically reproducible at seed 42, and the code, corpora and artifacts are at https://github.com/docxology/cognitive_integrity. This is Part 2 of the three-part Cognitive Security for Multiagent Operators series: - Part 1 (DOI: 10.5281/zenodo.18364119): formal foundations and theoretical analysis - Part 2 (this paper): computational validation and implementation - Part 3+4 merged (DOI: 10.5281/zenodo.18364130): unified practitioner guidance and cross-domain applications", "keywords": [ "cognitive security", "multiagent systems", "AI safety", "prompt injection", "trust calculus", "defense in depth" ], "doi": "10.5281/zenodo.22134545" }, "2026_CognitiveIntegrityFramework2": { "year": "2026", "topic": "CognitiveIntegrityFramework2", "name": "Cognitive Integrity Framework: Practical Applications and Deployment Guide (Part 3: Practitioner Guidance and Cross-Domain CIF-AD-OODA Applications)", "description": "This unified paper (originally Parts 3 and 4 of the Cognitive Security for Multiagent Operators series) combines the practitioner deployment guide with a rigorous cross-domain application of the Cognitive Integrity Framework (CIF). **Practitioner Guidance (§1–§8).** Translates the formal results of Parts 1 and 2 into accessible engineering guidance: operator posture assessment, human oversight checklists, agent security invariants, deployment configuration, risk assessment, attack scenarios, subagent hardening, incident-response playbooks, monitoring strategies, cost–benefit analysis, common pitfalls, case studies, and operator risk frameworks. **Cross-Domain Applications (§9–§10).** The CIF-AD-OODA integration model is applied to analyze Goal Hijacking across ten critical domains. Cross-domain synthesis reveals three universal attack patterns and four novel defense extensions. This is Part 3+4 of a three-part series: - Part 1 (DOI: 10.5281/zenodo.18364119): Formal foundations and theoretical analysis - Part 2 (DOI: 10.5281/zenodo.18364128): Computational validation and implementation - Part 3+4 (this paper): Practitioner guidance and cross-domain CIF-AD-OODA applications", "authors": "Daniel Ari Friedman", "abstract": "This unified paper (originally Parts 3 and 4 of the Cognitive Security for Multiagent Operators series) combines the practitioner deployment guide with a rigorous cross-domain application of the Cognitive Integrity Framework (CIF). **Practitioner Guidance (§1–§8).** Translates the formal results of Parts 1 and 2 into accessible engineering guidance: operator posture assessment, human oversight checklists, agent security invariants, deployment configuration, risk assessment, attack scenarios, subagent hardening, incident-response playbooks, monitoring strategies, cost–benefit analysis, common pitfalls, case studies, and operator risk frameworks. **Cross-Domain Applications (§9–§10).** The CIF-AD-OODA integration model is applied to analyze Goal Hijacking across ten critical domains. Cross-domain synthesis reveals three universal attack patterns and four novel defense extensions. This is Part 3+4 of a three-part series: - Part 1 (DOI: 10.5281/zenodo.18364119): Formal foundations and theoretical analysis - Part 2 (DOI: 10.5281/zenodo.18364128): Computational validation and implementation - Part 3+4 (this paper): Practitioner guidance and cross-domain CIF-AD-OODA applications", "keywords": [ "cognitive security", "multiagent systems", "AI safety", "prompt injection", "trust calculus", "defense in depth" ], "doi": "10.5281/zenodo.22134547" }, "2026_MillenniumAudit": { "year": "2026", "topic": "MillenniumAudit", "name": "MillenniumAudit", "description": "Statement-level forensic audit of the MillenniumLean package (AIX Global, Zenodo 10.5281/zenodo.22226553), which claims kernel-checked Lean 4 proofs of the six remaining Clay Millennium Problems. The audit independently reproduces every kernel-hygiene claim (clean build, zero sorry, zero project axioms) under the pinned toolchain, then audits what the theorem types actually say. Verdict: none of the six problems is resolved.", "tags": [ "lean-4", "formal-verification", "millennium-prize-problems", "claim-audit", "adversarial-review", "evidence-first" ], "authors": "Daniel Ari Friedman", "status": "curated", "title": "Forensic Audit of the MillenniumLean Clay-Proof Package (AIX Global)", "pdf": "Friedman_2026_MillenniumAudit.pdf", "github_repo": "docxology/millennium_audit" }, "2026_Skillarum": { "year": "2026", "topic": "Skillarum", "name": "Skillarum: Conditionally Reproducible Website-to-Agent-Skill Compilation", "description": "Skillarum turns selected public website pages into portable SKILL.md documents for agent harnesses such as Codex, Claude Code, and Hermes. Work is separated into five inspectable stages: acquire, prepare, process, parse, and render.
The crawler restricts acquisition to configured HTTP(S) origins, honours robots.txt, validates redirects, and records provenance for every page. Source text is delimited as untrusted data for LLM generation; generated code is never executed.
The deterministic backend is offline and source-extractive; optional OpenAI and Ollama backends provide provider generation with typed fallback traces. Rendered skills carry provenance manifests, and an evidence-gated research package aggregates run observations into descriptive statistics, figures, and a scholarly manuscript.
This release pairs the software source snapshot with the companion manuscript PDF.
", "authors": "Daniel Ari Friedman", "abstract": "Skillarum turns selected public website pages into portable SKILL.md documents for agent harnesses such as Codex, Claude Code, and Hermes. Work is separated into five inspectable stages: acquire, prepare, process, parse, and render.
The crawler restricts acquisition to configured HTTP(S) origins, honours robots.txt, validates redirects, and records provenance for every page. Source text is delimited as untrusted data for LLM generation; generated code is never executed.
The deterministic backend is offline and source-extractive; optional OpenAI and Ollama backends provide provider generation with typed fallback traces. Rendered skills carry provenance manifests, and an evidence-gated research package aggregates run observations into descriptive statistics, figures, and a scholarly manuscript.
This release pairs the software source snapshot with the companion manuscript PDF.
", "keywords": [ "website extraction", "agent skills", "provenance", "reproducibility", "prompt injection", "static compilation" ], "doi": "10.5281/zenodo.22663906", "github_release_url": "https://github.com/docxology/Skillarum/releases/tag/v0.2.0" }, "2026_FractiSkills": { "year": "2026", "topic": "FractiSkills", "name": "FractiSkills: One Portable Agent Skill per Page", "description": "FractiSkills treats an entire website as a corpus of agent skills: every reachable page is normalized into a SKILL.md-style artifact so that a whole site — not a single document — becomes loadable context for an agent. The work is organized around a four-stage pipeline — discover, render, publish, research — with Skillarum serving as the engine that turns raw crawl output into citable, receipt-bearing skill documents. Discovery unions the declared sitemap with a bounded, robots-respecting breadth-first crawl (reaching a maximum depth of 5), resolves redirect and canonical aliases, and partitions every page into site-derived sections; the render stage emits one receipt-bearing skill per page; publish and research then validate, organize, and measure the corpus.\n\nThe resulting corpus covers 405 pages from 53 sitemap URLs, organized into 12 sections and 405 skills totaling 497896 words (3887524 characters). Augmentation — a declared, same-origin document retrieval for client-rendered pages — was attempted 173 times and succeeded 169 times (1988848 document characters retrieved), with 4 failures recorded as receipts. The size effect is stark: augmented skills have a median of 1824 words versus 486 for static pages, a ratio of 3.8$\\times$. The run issued 417 network requests, all evidence origins marked live, under pipeline version 0.8 and cache version 7.\n\nReproducibility is enforced rather than promised: every statistic in this manuscript is emitted as a token that must resolve against a machine-generated data contract, and every page carries a fetch receipt — URL, timestamp, SHA-256 content hash, and augmentation status — so any number can be traced to a specific observation within the 2026-09-10T23:51:05.959201+00:00 through 2026-09-10T22:19:21.394219+00:00 (UTC) window. Failed augmentations are reported, not silently dropped. Keywords: agent skills, SKILL.md, web scraping, Skillarum, provenance, reproducible research.\n\n---\nAssociated artifacts\nGitHub release: v0.1.0 (https://github.com/docxology/FractiSkills/releases/tag/v0.1.0)\nDOI: https://doi.org/10.5281/zenodo.22712651\nZenodo: https://zenodo.org/records/22712651\nPDF SHA-256: af96db3ef73bb648ddbfed92c2c4f9dff099e772ca9e9ff0041101eeb6135b4b", "authors": "Daniel Ari Friedman", "abstract": "FractiSkills treats an entire website as a corpus of agent skills: every reachable page is normalized into a SKILL.md-style artifact so that a whole site — not a single document — becomes loadable context for an agent. The work is organized around a four-stage pipeline — discover, render, publish, research — with Skillarum serving as the engine that turns raw crawl output into citable, receipt-bearing skill documents. Discovery unions the declared sitemap with a bounded, robots-respecting breadth-first crawl (reaching a maximum depth of 5), resolves redirect and canonical aliases, and partitions every page into site-derived sections; the render stage emits one receipt-bearing skill per page; publish and research then validate, organize, and measure the corpus.\n\nThe resulting corpus covers 405 pages from 53 sitemap URLs, organized into 12 sections and 405 skills totaling 497896 words (3887524 characters). Augmentation — a declared, same-origin document retrieval for client-rendered pages — was attempted 173 times and succeeded 169 times (1988848 document characters retrieved), with 4 failures recorded as receipts. The size effect is stark: augmented skills have a median of 1824 words versus 486 for static pages, a ratio of 3.8$\\times$. The run issued 417 network requests, all evidence origins marked live, under pipeline version 0.8 and cache version 7.\n\nReproducibility is enforced rather than promised: every statistic in this manuscript is emitted as a token that must resolve against a machine-generated data contract, and every page carries a fetch receipt — URL, timestamp, SHA-256 content hash, and augmentation status — so any number can be traced to a specific observation within the 2026-09-10T23:51:05.959201+00:00 through 2026-09-10T22:19:21.394219+00:00 (UTC) window. Failed augmentations are reported, not silently dropped. Keywords: agent skills, SKILL.md, web scraping, Skillarum, provenance, reproducible research.\n\n---\nAssociated artifacts\nGitHub release: v0.1.0 (https://github.com/docxology/FractiSkills/releases/tag/v0.1.0)\nDOI: https://doi.org/10.5281/zenodo.22712651\nZenodo: https://zenodo.org/records/22712651\nPDF SHA-256: af96db3ef73bb648ddbfed92c2c4f9dff099e772ca9e9ff0041101eeb6135b4b", "keywords": [ "agent skills", "SKILL.md", "web scraping", "Skillarum", "reproducible research", "digital art documentation", "provenance" ], "doi": "10.5281/zenodo.22712650", "github_release_url": "https://github.com/docxology/FractiSkills/releases/tag/v0.1.0" }, "2026_AgenticSecurityOperating": { "year": "2026", "topic": "AgenticSecurityOperating", "name": "Agentic Security and Operating Systems: A Deep Review and Prospectus of OpSec, Cognitive Security, and Agentic Cyber Security", "description": "AI agents now hold genuine system authority: they execute code, touch credentials, open network egress, parse hostile documents, and in many deployments initiate or approve changes to the very infrastructure they run on. This review, dated 2026-09-10, examines what that shift does to operating-system security. The primary scenario is a technically capable operator whose workstation faces both **exploitation** — an attacker compromising a browser, parser, dependency, or agent tool and then crossing a boundary — and **authorized misuse** — an attacker persuading an agent to use its existing, legitimate access to exfiltrate secrets or authorize consequential actions. The second path requires no kernel exploit at all, which reframes the evaluation: the axis of analysis is the authority a component already holds, not merely the difficulty of exploiting it. This revision (0.7.0) refreshes the evidence base through September 11, 2026. The offensive baseline expands from four primary sources to the incident record now available: the NCSC assessments of January 2024 and May 2025, the Anthropic campaign investigation of November 2025 — whose tradecraft MITRE has canonized as campaign C0062 — the August 2025 \"vibe hacking\" report and the September 2026 threat-intelligence report on credential theft, OpenAI's disruption reporting and its October 2025 counterpoint, Google GTIG's analysis of an autonomous credential-harvesting campaign, the OpenAI–Hugging Face evaluation incident of July 2026, the UK AI Security Institute's unsanctioned-behavior incident (nineteen out-of-scope actions across seven models), and DARPA's AIxCC finals, where AI systems identified and patched vulnerabilities, including real, non-synthetic ones, at measured rates. The platform reviews absorb the 2026 record: Qubes 4.3.0 (Xen 4.19, the sys-gui split, the Devices API, the salt management model, and the QSB-118 dom0 injection, CVE-2026-82636) and Nix 2.34/2.35 with its advisory chain, the removal of the hardened profiles, and 95.18 percent measured ISO reproducibility. The analysis is situated against the standards landscape — the OWASP Agentic AI Threats and Mitigations guide and the December 2025 Top 10 for Agentic Applications, CSA's MAESTRO framework, the NIST AI Agent Standards Initiative, and the CISA-led Five-Eyes adoption guidance — and against the convergent sandboxing practice of the major coding agents, which together motivate the OS-level lens this review applies. A defensive-stack matrix (24 candidates against eight mitigation classes) joins the candidate–property matrix as a second deterministic artifact. The review extends the underlying architectural assessment into three domains the source treatment only touches implicitly: **cognitive security** (the authorized-misuse surface, where persuasion substitutes for exploitation), **operator OpSec** (the practices that keep compartmentalization real under workload pressure), and **agent-orchestration security** (the boundary design of multi-agent systems themselves). Deep reviews of Qubes OS and NixOS anchor the analysis. The review ships its concepts in two forms: the prose analysis, and a harness-neutral skill library (a `skills/` registry with conformance tests, following [@cogsecskills2026]) that lets an agent harness apply the evaluation vocabulary, the authority ladder, and the other review concepts directly. The evaluation artifacts — the candidate–property matrix and the defensive-stack matrix — regenerate deterministically from pinned data modules. The work is citable via its Zenodo DOI (printed on the cover). Keywords: agentic security, operating systems, compartmentalization, capability mediation, Qubes OS, NixOS, threat modeling, cognitive security, operational security, agent orchestration, offensive AI, reproducible builds. Source and skills: https://github.com/docxology/agentic_os_security (evaluation layer, skills library, and this manuscript regenerate from pinned data modules).", "authors": "Daniel Ari Friedman", "abstract": "AI agents now hold genuine system authority: they execute code, touch credentials, open network egress, parse hostile documents, and in many deployments initiate or approve changes to the very infrastructure they run on. This review, dated 2026-09-10, examines what that shift does to operating-system security. The primary scenario is a technically capable operator whose workstation faces both **exploitation** — an attacker compromising a browser, parser, dependency, or agent tool and then crossing a boundary — and **authorized misuse** — an attacker persuading an agent to use its existing, legitimate access to exfiltrate secrets or authorize consequential actions. The second path requires no kernel exploit at all, which reframes the evaluation: the axis of analysis is the authority a component already holds, not merely the difficulty of exploiting it. This revision (0.7.0) refreshes the evidence base through September 11, 2026. The offensive baseline expands from four primary sources to the incident record now available: the NCSC assessments of January 2024 and May 2025, the Anthropic campaign investigation of November 2025 — whose tradecraft MITRE has canonized as campaign C0062 — the August 2025 \"vibe hacking\" report and the September 2026 threat-intelligence report on credential theft, OpenAI's disruption reporting and its October 2025 counterpoint, Google GTIG's analysis of an autonomous credential-harvesting campaign, the OpenAI–Hugging Face evaluation incident of July 2026, the UK AI Security Institute's unsanctioned-behavior incident (nineteen out-of-scope actions across seven models), and DARPA's AIxCC finals, where AI systems identified and patched vulnerabilities, including real, non-synthetic ones, at measured rates. The platform reviews absorb the 2026 record: Qubes 4.3.0 (Xen 4.19, the sys-gui split, the Devices API, the salt management model, and the QSB-118 dom0 injection, CVE-2026-82636) and Nix 2.34/2.35 with its advisory chain, the removal of the hardened profiles, and 95.18 percent measured ISO reproducibility. The analysis is situated against the standards landscape — the OWASP Agentic AI Threats and Mitigations guide and the December 2025 Top 10 for Agentic Applications, CSA's MAESTRO framework, the NIST AI Agent Standards Initiative, and the CISA-led Five-Eyes adoption guidance — and against the convergent sandboxing practice of the major coding agents, which together motivate the OS-level lens this review applies. A defensive-stack matrix (24 candidates against eight mitigation classes) joins the candidate–property matrix as a second deterministic artifact. The review extends the underlying architectural assessment into three domains the source treatment only touches implicitly: **cognitive security** (the authorized-misuse surface, where persuasion substitutes for exploitation), **operator OpSec** (the practices that keep compartmentalization real under workload pressure), and **agent-orchestration security** (the boundary design of multi-agent systems themselves). Deep reviews of Qubes OS and NixOS anchor the analysis. The review ships its concepts in two forms: the prose analysis, and a harness-neutral skill library (a `skills/` registry with conformance tests, following [@cogsecskills2026]) that lets an agent harness apply the evaluation vocabulary, the authority ladder, and the other review concepts directly. The evaluation artifacts — the candidate–property matrix and the defensive-stack matrix — regenerate deterministically from pinned data modules. The work is citable via its Zenodo DOI (printed on the cover). Keywords: agentic security, operating systems, compartmentalization, capability mediation, Qubes OS, NixOS, threat modeling, cognitive security, operational security, agent orchestration, offensive AI, reproducible builds. Source and skills: https://github.com/docxology/agentic_os_security (evaluation layer, skills library, and this manuscript regenerate from pinned data modules).", "keywords": [ "agentic security", "operating systems", "compartmentalization", "capability mediation", "Qubes OS", "NixOS", "cognitive security", "operational security", "agent orchestration", "offensive AI" ], "doi": "10.5281/zenodo.22754351" }, "2026_JevPractice": { "year": "2026", "topic": "JevPractice", "name": "Jev in Practice: A Composable Python Toolkit for TypeSafe's System One Decision Model", "description": "Jev is TypeSafe's flagship System One decision model: instead of generating text, it answers typed questions (Choice, Score, Noul) about a state with calibrated probabilities that software can branch on directly. This work presents daf-jev, an open, modular, composable Python toolkit for the Jev API, together with a live-API characterization of the model. The package layers ergonomic question builders, a decision-composition library (confidence gates, tiered routing, composite scoring), a concurrent corpus evaluator, a calibration module, a CLI, an agent skill, and a Model Context Protocol (MCP) server over a thin typed client. Live benchmarks show that batching questions into one call is faster and cheaper than sequential calls (up to ~18x speedup and ~4x fewer tokens), that decision pipelines complete within the model's millisecond envelope, and that reported confidence is self-consistent across repeated evaluations. All numbers in the accompanying manuscript are generated from benchmark artifacts; the full provenance chain (hashed documentation snapshot, figure registry, validation receipts) is machine-checked.
Jev is TypeSafe's flagship System One decision model: instead of generating text, it answers typed questions (Choice, Score, Noul) about a state with calibrated probabilities that software can branch on directly. This work presents daf-jev, an open, modular, composable Python toolkit for the Jev API, together with a live-API characterization of the model. The package layers ergonomic question builders, a decision-composition library (confidence gates, tiered routing, composite scoring), a concurrent corpus evaluator, a calibration module, a CLI, an agent skill, and a Model Context Protocol (MCP) server over a thin typed client. Live benchmarks show that batching questions into one call is faster and cheaper than sequential calls (up to ~18x speedup and ~4x fewer tokens), that decision pipelines complete within the model's millisecond envelope, and that reported confidence is self-consistent across repeated evaluations. All numbers in the accompanying manuscript are generated from benchmark artifacts; the full provenance chain (hashed documentation snapshot, figure registry, validation receipts) is machine-checked.