MHLM examines multi-model LLM convergence and the epistemic risks of AI-assisted scientific development.
- provenance of model outputs and iteration chains
- hypothesis-vs-validation separation
- reproducibility of AI-assisted workflows
- epistemic amplification and disagreement analysis
This track does not validate physical claims by itself. It evaluates how AI systems shape, amplify, correct, or destabilize research narratives.
The 2026-05-26 final update preserves the latest MHLM Ultra Master Library and final model-response audit package as archive/provenance evidence.
- Final Master Library:
archive/final_master_library/2026-05-25_MHLM_Ultra_Master_Library/ - Final model-response audit round:
model-responses/final_audit_round_2026-05-25/ - Provenance and consensus policy:
provenance/ - Freeze scope and checklist:
freeze/
Model responses remain critique and process evidence only. Cross-model agreement is not validation.