Quantitative linear logic semantics, when used as the differentiable constraint layer in neuro-symbolic loop invariant synthesis, will reduce proof-search token cost on Lean-QuantumAlg-Bench by enforcing resource-bounded inference that prevents the combinatorial branching responsible for the scalability bottleneck observed in cloud-API-dependent verification tools.

Adversarial Debate Score

43% survival rate under critique

Supporting Research Papers

• Quantitative Linear Logic for Neuro-Symbolic Learning and Verification

  Differentiable Logics are deployed in neuro-symbolic learning tasks as a way of embedding logical constraints in the training objective of neural networks. A differentiable logic consists of a syntax ...

• 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...

• Automating Bitvector and Finite Field Equivalence Proofs in Lean

  Efforts to verify Zero-Knowledge Proof circuit encodings have highlighted the challenge of proving the correctness of quantifier-free statements that make use of both bitvector and finite field operat...

• Neuro-Symbolic Compliance: Integrating LLMS and SMT Solvers for Automated Financial Legal Analysis

  Financial regulations are increasingly complex, hindering automated compliance-especially the maintenance of logical consistency with minimal human oversight. We introduce a Neuro-Symbolic Compliance ...

• Local strategies are pretty good at computing Boolean properties of quantum sequences

  Quantum memory is a scarce and costly resource, yet little is known about which learning tasks remain feasible under severe memory constraints. We study the problem of computing global properties of q...

Formal Verification

Source