feat: Anomaly Learning Agent (ALA) implementation

[google-labs-jules]

Translates the formal ALA and statistical anomaly score specification into a functional prototype module (AnomalyLearningAgent).

Features implemented:

• Neural Sequence Modeling Mock: Evaluates $S_{\text{neural}}$ against action histories.
• Toolchain Entropy Gradient Tracking: Dynamically calculates Shannon entropy over consecutive API/tool calls.
• KL Divergence: Computes Relative Entropy for task-context alignment.
• Run-Time Verification Loop (The ALA Guard): A gatekeeper that triages actions via an affordance watchlist and entropy-spike detection, calculating a composite Risk Score across four symbolic/sub-symbolic metrics.
• Threshold Dynamics ODE Simulation: Models the evolution of defensive posture to prevent "Sycophantic Blindness" and "Semantic Ossification."

Includes full Pytest coverage across stability scenarios (Over-Damped, Under-Damped), ADR 39 for design recording, integration into the LEXICON.md, and 3 strategic research prompts regarding Differentiable Logic Manifolds and asynchronous KV-Cache auditing.

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PR created automatically by Jules for task 9680624320776698695 started by @projectedanx

[google-labs-jules]

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