Computational · Paper · 2026

Cognitive Integrity Framework: Computational Validation and Empirical Analysis (Part 2 of 3: Implementation, Empirical Analysis, and Adversarial Evaluation)

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Catalog Row213
Citation KeyFriedman2026CognitiveIntegrityFrameworkComputational213
Paper FolderAvailable

Overview

Extracted from the local paper documentation when available.

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 re

cognitive securitymultiagent systemsAI safetyprompt injectiontrust calculusdefense in depth

Use Notes

Concise findings and methods pulled from README/SKILL documentation.

Findings / Concepts
  • cognitive security
  • multiagent systems
  • AI safety
  • prompt injection
  • trust calculus
Methods / Techniques
  • Not yet summarized.

Citation

Plain-text citation for quick reuse.

Friedman, Daniel Ari. 2026. Cognitive Integrity Framework: Computational Validation and Empirical Analysis (Part 2 of 3: Implementation, Empirical Analysis, and Adversarial Evaluation). Zenodo. DOI: 10.5281/zenodo.22134545. URL: https://doi.org/10.5281/zenodo.22134545.

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