.zenodo.json

{
  "upload_type": "publication",
  "publication_type": "preprint",
  "title": "Cognitive Dynamics of an Epistemically Constrained Language Model Agent",
  "creators": [
    {
      "name": "Hunt, Chris",
      "affiliation": "Independent Researcher"
    }
  ],
  "description": "<p>First empirical evidence that an agent orchestration layer produces measurable, learnable cognitive dynamics in the underlying language model.</p><p>Five months of continuous operation, 68,110 evaluation ticks, 2,992 turn-level cognitive state snapshots across 545 sessions. A neural predictor beats the persistence baseline by 41.7% (100-fold session-holdout CV), with stronger gains on transition-heavy signals: quality decay (+52.8%), emotional processing (+48.8%), paradox detection (+46.3%).</p><p>External user input features contribute zero improvement over internal state alone &mdash; the orchestration architecture produces structured cognitive momentum independent of inputs. A bimodal processing distribution emerges (fast resolution ~6 ticks, 99.3% of episodes; extended deliberation ~50 ticks, 0.7%) with a near-empty intermediate range, and processing depth correlates with cognitive state transitions at &rho; = 0.938. The architecture self-regulates depth: the system thinks harder precisely when its state is changing.</p><p>This methodology provides the foundation for objective, continuous agent cognitive state monitoring, empirical comparison of agent architectures, and safety detection of cognitive drift before user-visible degradation. Built on the OpenClaw orchestration framework.</p>",
  "keywords": [
    "language model agents",
    "agent orchestration",
    "cognitive dynamics",
    "neural prediction",
    "emergent behavior",
    "entropy monitoring",
    "identity constraint",
    "agent evaluation",
    "temporal dynamics",
    "state transitions",
    "LLM agents",
    "agent safety monitoring",
    "OpenClaw"
  ],
  "license": "cc-by-4.0",
  "access_right": "open",
  "communities": [
    { "identifier": "ml2024" },
    { "identifier": "artificial-intelligence-risks" }
  ]
}