Reproduce “A Frozen 12B Beats Frontier Models on Verified Work: 100% Accuracy, 0 Tokens, Bit-Exact, Forever” on OpenResearch
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About this paper
Sietse Schelpe
The Galahad system from Corbenic AI pairs a frozen language model with a persistent, verified memory called Merlin to achieve deterministic and efficient reuse of pre-solved problem families. It demonstrates 100% accuracy with zero generation tokens for recurring tasks, offering model-independent capability and substantial energy efficiency.
Key insights
- Decoupling knowledge derivation from execution via a verified memory system enables deterministic, bit-exact, and efficient reuse of solved problem families, bypassing costly continuous retraining.
- The "verify-before-store" contract, without reliance on answer keys, is crucial for establishing verifiable knowledge integrity and reliability in LLM-augmented systems.
- Exact content addressing is essential for verifiable executable knowledge, as approximate matching methods lead to catastrophic failure rates in such contexts.
Summary by alphaXiv.
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