**Neuro-symbolic theorem-proving agents trained on Lean-QuantumAlg-Bench will exhibit power-law scaling of proof-completion success rates with model size, mirroring the FP32-BF16 LMC barrier decay observed in surrogate Bayesian optimization, due to shared underlying precision-induced representational regime partitioning in high-dimensional reasoning spaces.** *(Bridges formal verification in quantum algorithms, neuro-symbolic AI, and validated precision-barrier scaling laws; extends the owner's LMC findings to symbolic reasoning domains while avoiding prior phase-separation or coalition-based hypotheses.)*
Adversarial Debate Score
50% survival rate under critique
Expert panel critique
Independent views, each critiquing the hypothesis on its own — the score rewards genuine disagreement and discounts consensus.
Supporting Research Papers
- Benchmarking Agents for Proving Theorems in Quantum Algorithms and Quantum Information
Formal verification is becoming increasingly practical for quantum computing, yet the ability of AI agents to construct machine-checkable proofs in this domain remains unmeasured. We introduce Lean-Qu...
- Neuro-Symbolic Software Verification: Hyper-charging Local Language Models with Symbolic Reasoning at Scale
Loop invariant synthesis remains a central and pivotal bottleneck in formal software verification. Recent LLM-based Neuro-Symbolic tools have achieved impressive solve rates. However, these tools rely...
- QED-Nano: Teaching a Tiny Model to Prove Hard Theorems
Proprietary AI systems have recently demonstrated impressive capabilities on complex proof-based problems, with gold-level performance reported at the 2025 International Mathematical Olympiad (IMO). H...
- Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key
Reinforcement learning (RL) has been applied to improve large language model (LLM) reasoning, yet the systematic study of how training scales with task difficulty has been hampered by the lack of cont...
- Building Shor's Algorithm in Lean: An Agentic Formalization of Quantum Attacks on RSA-2048 and P-256
Large language models are increasingly assisting with demanding formal theorem-proving tasks, particularly when grounded in machine-checked libraries such as Lean. Agentic systems further amplify this...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.