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On the Structural Non-Preservation of Epistemic Behaviour under Policy Transformation

Alexander Galozy · Feb 24, 2026 · Citations: 0

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Feb 24, 2026, 10:55 PM

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Apr 13, 2026, 6:42 AM

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Extraction source

Persisted extraction

Confidence 0.15

Abstract

Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent context. We formalise such information-conditioned interaction patterns as behavioural dependency: variation in action selection with respect to internal information under fixed observations. This induces a probe-relative notion of $ε$-behavioural equivalence and a within-policy behavioural distance that quantifies probe sensitivity. We establish three structural results. First, the set of policies exhibiting non-trivial behavioural dependency is not closed under convex aggregation. Second, behavioural distance contracts under convex combination. Third, we prove a sufficient local condition under which gradient ascent on a skewed mixture objective decreases behavioural distance when a dominant-mode gradient aligns with the direction of steepest contraction. Minimal bandit and partially observable gridworld experiments provide controlled witnesses of these mechanisms. In the examined settings, behavioural distance decreases under convex aggregation and under continued optimisation with skewed latent priors, and in these experiments it precedes degradation under latent prior shift. These results identify structural conditions under which probe-conditioned behavioural separation is not preserved under common policy transformations.

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HFEPX Relevance Assessment

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Main weakness

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Trust level

Low

Eval-Fit Score

0/100 • Low

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Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

HFEPX Fit

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Field Provenance & Confidence

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Human Feedback Types

missing

None explicit

Confidence: Low Source: Persisted extraction missing

No explicit feedback protocol extracted.

Evidence snippet: Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent context.

Evaluation Modes

missing

None explicit

Confidence: Low Source: Persisted extraction missing

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Evidence snippet: Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent context.

Quality Controls

missing

Not reported

Confidence: Low Source: Persisted extraction missing

No explicit QC controls found.

Evidence snippet: Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent context.

Benchmarks / Datasets

missing

Not extracted

Confidence: Low Source: Persisted extraction missing

No benchmark anchors detected.

Evidence snippet: Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent context.

Reported Metrics

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Confidence: Low Source: Persisted extraction missing

No metric anchors detected.

Evidence snippet: Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent context.

Rater Population

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Unknown

Confidence: Low Source: Persisted extraction missing

Rater source not explicitly reported.

Evidence snippet: Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent context.

Human Data Lens

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Unknown
  • Unit of annotation: Unknown
  • Expertise required: General
  • Extraction source: Persisted extraction

Evaluation Lens

  • Evaluation modes:
  • Agentic eval: None
  • Quality controls: Not reported
  • Confidence: 0.15
  • Flags: low_signal, possible_false_positive

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Deterministic synthesis

Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent context. HFEPX protocol signal is limited in abstract-level metadata, so treat it as adjacent context. Updated from current HFEPX corpus.

Generated Apr 13, 2026, 6:42 AM · Grounded in abstract + metadata only

Key Takeaways

  • Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent context.

Researcher Actions

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  • Identify benchmark choices from full text before operationalizing conclusions.
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Caveats

  • Generated from title, abstract, and extracted metadata only; full-paper implementation details are not parsed.
  • Low-signal flag detected: protocol relevance may be indirect.

Research Summary

Contribution Summary

  • Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent context.

Why It Matters For Eval

  • Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent context.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Gap: Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

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