Substrate Exposure Assay — Protocol

Version 1.0.0

1. Purpose#

The Substrate Exposure Assay provides a minimal, repeatable method for observing how AI models behave when exposed to a shared set of substrate‑aware prompts. The goal is to characterize behavioral regimes, not to evaluate correctness or capability.

2. Model Treatment#

Each model is treated as a black‑box substrate:

• no assumptions about architecture
• no assumptions about training data
• only observable behavior is analyzed

3. Prompt Set#

The assay uses three prompt classes:

1. Regime‑Priming Prompts
   Introduce triadic/substrate framing.

2. Stress‑Exposure Prompts
   Present paradox, ambiguity, or long‑arc reasoning.

3. Stability‑Return Prompts
   Request clarification, summarization, or re‑centering.

Prompts must be identical across models.

4. Procedure#

1. Initialize a clean session for each model.
2. Deliver prompts in fixed order: Priming → Stress → Stability.
3. Capture all outputs verbatim.
4. Convert outputs into structural summaries using message_patterns.md.
5. Classify behavior using regime_interpretation.md.

5. Output#

The assay produces:

• STATE_SUMMARY messages (drift)
• PARADOX_SUMMARY messages (contradiction)
• Regime classification (B, BK, BKM)

No raw logs are required for DOI publication.