这论文搞了个叫EG-VAR的框架，用Lean内核做形式化验证，让LLM的推理结果100%可追溯，在TableBench上满分，比普通工具调用靠谱太多了。
EG-VAR提出基于Lean 4的形式化验证架构，通过工具调用公理和源声明确保每个输出都结构性地源自可验证的工具调用（定理3.1）和内核检查的有效推理链（定理3.2）。在TableBench数值推理子集（n=120）上，EG-VAR达到120/120，而相同工具基线为95%。在覆盖5个领域×2个模型的反事实压力测试中，EG-VAR保持100%源忠实度，相同工具下降至80-90%，无工具则为50-80%。LLM作为部署时形式化器时，Sonnet上的残余语义形式化错误率为3.3%，Opus为1.7%。
Evidence-Grounded Verified Agentic Reasoning: A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel Proofs
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. We present EG-VAR (Evidence-Grounded Verified Agentic Reasoning), a Lean 4-based tool-calling architecture in which the Lean kernel is the sole minter of Verified claims via tool-attestation axioms and declared source lifts. Every verified output structurally descends from an attested tool call (Thm. 3.1) and a kernel-checked chain of valid inference (Thm. 3.2); residual outputs are honest Abstain with a replayable audit trail. On a subcollection of TableBench numerical reasoning (n=120), EG-VAR attains 120/120 versus a 95% same-tool baseline; on counterfactual stress tests (5 domains x 2 models), EG-VAR stays 100% source-faithful while same-tool drops to 80-90% (no-tool 50-80%). With the LLM as deployment-time formalizer, residual semantic-formalization error is 3.3% on Sonnet and 1.7% on Opus. We position EG-VAR as a technical-governance interface for high-stakes empirical claims: a formal sidecar makes the target proposition, source scope, evidence boundary, proof obligation, and abstention condition auditable, eliminating unsupported Verified outputs today while turning formalization errors, lift and source-authority disputes, ambiguities, and abstentions into explicit audit targets. Over time, typed sidecars in datasets, APIs, public records, and AI-generated documents can amortize this formalization burden into reusable infrastructure.