1. This result extends your machine learning and combinatorial optimization methods by providing a more token-efficient, AST-based theorem-proving agent (AoA) to formally verify the correctness of your QUBO docking algorithms and GNN surrogate models without high API costs.
Adversarial Debate Score
47% 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...
- Harnessing Code Agents for Automatic Software Verification
Formal verification offers the strongest guarantee of software correctness, but it does not scale: the proofs demanded by interactive theorem provers such as Coq require enormous expert effort. Large ...
- OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement
We propose OptiAgent, a multi-agent framework that, given a natural language description of an Operations Research problem, is able to output a solver-ready mathematical formulation as well as executa...
- Efficient and Sound Probabilistic Verification for AI Agents
Securing AI agents that operate in complex digital environments has become a critical need, and runtime monitoring approaches that formulate and enforce policies expressed in a formal language like Da...
- PostTrainBench: Can LLM Agents Automate LLM Post-Training?
AI agents have become surprisingly proficient at software engineering over the past year, largely due to improvements in reasoning capabilities. This raises a deeper question: can these systems extend...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.