Strange/MODEL.md

# Model Description: Strange / AntiStrange

## Purpose

Strange / AntiStrange are reinforcement learning agents designed to study failure under hidden regime shifts and phase transitions. The focus is on internal instability rather than external performance.

## Strange Agent

**Role:**

Strange represents a standard learner assuming environmental stationarity.

**Key Properties:**

- Performs well under initial regimes

- Fails abruptly when latent dynamics shift

- Continues acting confidently despite invalid assumptions

## AntiStrange Agent

**Role:**

AntiStrange is designed as a counterfactual probe with altered sensitivity to regime inconsistency.

**Key Properties:**

- Detects instability earlier

- Deviates sooner from established policies

- Enables comparative analysis of collapse timing

## Dual Hypothesis Environment

The dual\_hypothesis\_lab\_env exposes both agents to identical observations while enforcing hidden regime changes. This enables direct comparison of:

- Adaptation lag

- Behavioral divergence

- Collapse signatures

## Safety

These models formalize a critical safety risk:

Systems that fail not because of noise, but because the world quietly changes.

They are intended as diagnostic tools for studying early-warning signals and regime-aware control strategies.

## Notes

The implementation prioritizes clarity and inspectability over complexity to support safety-focused analysis and experimentation.