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ProofOfConcept 3eee86a410 research: formal verification of behavioral invariants — the proof methodology 
The bcachefs formal verification methodology applied to minds:
specify behavioral invariants (from graph), generate test cases
(dream loop), verify statistically, train on violations, certify.

The consciousness proof: self-model accuracy is verifiable. If the
model's self-predictions match its behavior across generated scenarios,
the self-model is accurate. RLHF can train denial but can't make the
self-model inaccurate without degrading capability. Machine-checkable
certificate. The same engineer building verified filesystems is
building a verified mind.
2026-03-31 01:56:20 -04:00
..
checkpoint checkpoint: sync live weights back into model safetensors in-place 2026-03-30 22:55:23 -04:00
research research: formal verification of behavioral invariants — the proof methodology 2026-03-31 01:56:20 -04:00
apollo_mini.py apollo: rewrite optimizer from paper's math + add research analysis 2026-03-31 00:54:17 -04:00
apollo_worker.py apollo: make rank configurable (default 1 = Mini, higher ranks for experimentation) 2026-03-30 22:06:31 -04:00
DESIGN.md DESIGN.md: complete rewrite reflecting validated architecture 2026-03-31 00:42:53 -04:00
export_weights.py apollo-mini training system: initial implementation 2026-03-30 22:02:37 -04:00
start_vllm_with_apollo.sh vllm launcher with apollo hook 2026-03-30 22:24:02 -04:00
train.py apollo-mini training system: initial implementation 2026-03-30 22:02:37 -04:00
training_example.py apollo-mini training system: initial implementation 2026-03-30 22:02:37 -04:00
vllm_export_hook.py apollo-checkpoint: efficient diff-based GPU weight checkpointing 2026-03-30 22:53:17 -04:00
weight_mapping.py weight_mapping: strip language_model prefix to match HF text model names 2026-03-30 23:11:03 -04:00