{ "title": "A Deterministic Testbed for Self-Organizing Agent-Team Coordination", "version": "1.0.0", "doi": "10.5281/zenodo.20533670", "doi_url": "https://doi.org/10.5281/zenodo.20533670", "zenodo_record": "https://zenodo.org/records/20533670", "record_id": "20533670", "publication_date": "2026", "resource_type": { "title": "Journal article", "type": "publication", "subtype": "article" }, "creators": [ { "name": "Daniel Ari Friedman", "affiliation": "Active Inference Institute", "orcid": "0000-0001-6232-9096" } ], "description": "Abstract\n\nRecent 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: v1.0.0 (https://github.com/docxology/template_autoscientists/releases/tag/v1.0.0)\nPDF SHA-256: a7f202bb1aa3c13803508e033c7561e6afe8b74148b60e9a3f91341ebfc21e65", "keywords": [ "agent coordination", "scientific discovery", "noise-band confirmation", "ablation study", "reproducible research", "language-model agents" ], "files": [ { "name": "Friedman_2026_Deterministic_a7f202bb.pdf", "size_bytes": 270230, "checksum": "md5:d32f9117e4aa04717612c93d89ae7828", "download_url": "https://zenodo.org/api/records/20533670/files/Friedman_2026_Deterministic_a7f202bb.pdf/content" } ], "related_resources": [ { "type": "repository", "url": 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