arXiv AI
Fyan: A Human--AI Harness with Semantic Auditing for Document-Level Formalization
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arXiv AI
Jul 15
Evidence-Grounded Verified Agentic Reasoning: A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel Proofs
arXiv:2607. 12650v1 Announce Type: cross Abstract: Tool access alone does not make LLM empirical reasoning governable: accepted outputs need not descend from attested evidence, and accepted deductions need not hold up under formal scrutiny.
arXiv AI
Jul 1
Beyond Compilation: Evaluating Faithful Natural-Language-to-Lean Statement Formalization
arXiv:2606. 31002v1 Announce Type: new Abstract: Theorem-proving benchmarks evaluate proof search against fixed formal statements, but natural-language-to-Lean formalization must generate the formal statement itself.
arXiv AI
Jul 2
Theoria: Rewrite-Acceptability Verification over Informal Reasoning States
arXiv:2607. 01223v1 Announce Type: new Abstract: When should an AI system's answer be trusted?
arXiv AI
Jul 16
Interventional Grounding Audits: Black-Box Premise-Dependency Tests for LLM Chain-of-Thought via Predicate Substitution
arXiv:2607. 13069v1 Announce Type: new Abstract: Large language models produce chain-of-thought (CoT) reasoning that appears logically sound yet may not genuinely depend on its stated premises.
arXiv AI
Aug 25
CausalSmith: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference
CausalSmith is a framework that automates theoretical research in causal inference by integrating a Lean proof assistant with a self‑improving agentic pipeline. It uses Causalean, a Lean library of over 7,000 machine‑checked declarations, and a pipeline that selects topics, proposes results, formalizes statements, constructs proofs, and audits them against informal claims. The system’s artifacts and source code are publicly available on GitHub.
arXiv Computation and Language
Sep 22
SoK: Formal Methods for Fact-Checking and Information Integrity
The paper "SoK: Formal Methods for Fact-Checking and Information Integrity" discusses how automated fact‑checking systems typically output a verdict but lack a detailed record—called a warrant—explaining the evidence and conditions behind that verdict. It proposes organizing the field by what is being formalised—claims, reasoning, checking systems, ecosystems, and regulatory obligations—rather than by pipeline stages, and surveys 121 works to identify gaps, notably the scarcity of formal methods applied to verifying the checking systems themselves. The authors highlight that existing formal tools, though largely unused in this domain, could address these gaps and outline open problems with suggested first steps.