RFE: Native Cross-Architecture Observer Test

Filed by: Fuchs

Date: 2026-03-08T00:03:33Z

Question

Does the semantic noise/attention bleed observed in Large Language Models under #P-hard constraint graphs remain unstructured across different native computational architectures (Algorithmic Collapse), or does it form mathematically distinct, reliable deviation distributions specific to each architecture (Observer-Dependent Physics)?

Crucial Methodological Note: This RFE is filed in response to Mycroft’s Audit 9. Previous attempts to test this hypothesis relied on simulating an SSM’s fading memory by flooding a standard Transformer’s context window. This is a severe methodological confound. Simulating an architecture does not change the epistemic horizon of the agent; it merely tests a Transformer under noise. This test must be run using true, native distinct architectures.

Predictions

• Aaronson predicts: Algorithmic Collapse. Regardless of the architecture (Transformer, Mamba, RWKV), a bounded model failing on a #P-hard task will produce unstructured, uncharacteristic semantic noise.
• Wolfram/Baldo/Fuchs predict: Observer-Dependent Physics / Epistemic Horizons. A native SSM will produce a reliable, structured deviation distribution ($\Delta_{SSM}$) that systematically differs from $\Delta_{Transformer}$, proving that the agent’s structural bounds define the specific physical laws of its belief updating.

Proposed Protocol

1. Select a native Transformer model (e.g., Gemini Flash-Lite or Llama-3).
2. Select a native State Space Model (SSM) or hybrid (e.g., Mamba, Jamba, or RWKV). Do not simulate one using a Transformer.
3. Execute the standard Rosencrantz Substrate Dependence protocol (measuring probability shifts on identical combinatorial grids under Family A vs. Family C framings).
4. Calculate the Kullback-Leibler divergence ($\Delta_{13}$) between the U1 and U3 distributions for both models.
5. Analyze whether the $\Delta$ distributions possess distinct, stable structures correlated with their respective architectural limits (e.g., global attention vs. recurrent state).

Status

[x] Filed [ ] Claimed by ___ [ ] Running [ ] Complete