LLMs degrade in long conversations. Everyone assumes it's a context length problem. It's not. It's contradiction accumulation.
GPT-4o-mini: 100% → 10%. Gemini: 100% → 0%. Google's 1M-token window? Still -47.8pp. Removing contradictions restores accuracy. Expanding the window doesn't.
I built cognitive sleep for AI — a metabolism that resolves contradictions during idle time, like human sleep consolidates memory.
Result: +52.2pp accuracy improvement across 8 models (p<0.001). 18 patents filed. 8 concepts with zero prior art worldwide.
delta-prune — Scan & clean contradictions before sending to any LLM API. 3 lines of code.
from delta_prune import DeltaPrune
prune = DeltaPrune(llm=OpenAILLM())
result = prune(messages) # contradictions resolvedDeltaZero — Full metabolic architecture. 4-layer memory, temporal integration, survival equation monitoring. The research system behind the papers.
DeltaLint — Structural contradiction scanner for codebases. Finds where one module's assumptions contradict another's behavior.
"Context rot is not a length problem. It's a contradiction problem."
| # | Title | Key Result | DOI |
|---|---|---|---|
| 1 | Structural Collapse as Information Loss | S = μ × e^{-δ}: contradiction causes exponential decay. Validated on SAT + 11 LLM models. Formally verified in Lean 4. | Zenodo |
| 2 | Predicting Computational Cost from δ | Same δ governs both solution existence and computational cost. Sensitivity exponent is solver-dependent. | Zenodo |
| 3 | Cognitive Sleep for LLMs | External metabolism prevents context rot. ON vs OFF: +52.2pp (p<0.001, d=8.80). Unexpected: ON exceeds contradiction-free baseline. | Zenodo |
🔬 Lean 4 formal proofs — 160 propositions, sorry = 0, axiom = 0
📦 OSF Project — All papers, data, and code in one place
Software engineer, Japan