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?
Verification abundance, adjudication scarcity: what happens to mathematical knowledge when proof checking becomes free
Related stories
arXiv AI
Jun 10
Evaluating Research-Level Math Proofs via Strict Step-Level Verification
arXiv:2606. 10799v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle to rigorously verify complex mathematical proofs.
arXiv AI
Oct 15
claimcheck: Measuring and Improving Specification Faithfulness
arXiv:2610.17358v1 Announce Type: cross Abstract: Formal verification proves that a program satisfies its specification, but says nothing about whether the specification captures what the author mean...
arXiv AI
Aug 24
AI with Authority, from Application to Silicon
arXiv:2608.21356v1 Announce Type: cross Abstract: For sixty years, machine verification has been a major cost overhead, affordable only for exceptional artifacts. Here we report that generative AI in...
arXiv AI
Oct 31
Beyond fabrication: Misattributed numbers in papers written by AI research agents
The paper examines how AI‑written research papers often contain misattributed numerical claims. By re‑analyzing 540 judgments from a benchmark of nine AI agents on fifteen research problems, the authors manually labeled 256 claims and found that most failures stem from misalignment between the paper and the underlying work rather than outright cheating. They propose a deterministic claim‑to‑evidence checker that achieves over 90% precision and, when combined with an LLM verifier, offers a more reliable audit of AI‑generated research.
arXiv AI
Aug 18
Does the Proof Prove It That Way? Faithful Formalization of Elements Proofs
arXiv:2608. 15432v1 Announce Type: new Abstract: In formal verification, both the autoformalization of statements and automated proof search have been studied extensively.
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 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.
arXiv AI
Aug 18
ALPS: Measuring Valid Creativity in Large Language Models with Mathematical Construction
arXiv:2608. 15979v1 Announce Type: new Abstract: Large language models produce outputs presented as discoveries - new proofs, conjectures, or molecules.
arXiv Computation and Language
Oct 15
Same Claim, Different Interpretations: How LLM Agents Resolve Ambiguity in Scientific Claims
arXiv:2610.20457v1 Announce Type: new Abstract: Co-scientists and agentic scientific claim verifiers are increasingly used to assess claims across the sciences. Natural-language scientific claims, wr...
arXiv AI
Oct 16
ClaimScope: Label-Blind Witness Verification for Mathematical Claim Auditing
ClaimScope is a 72‑task benchmark that audits mathematical claims by requiring a verdict, scope, counterexample, and repair, covering areas such as optimizer‑state transport and exact $2 imes2$ linear algebra. The study evaluates Qwen3‑8B using a witness checker that only examines the model’s submitted state, showing that verifier feedback improves counterexample acceptance and safe completions compared to generic feedback. The results demonstrate that separating claim classification from executable evidence can yield gains in witness‑structure correction without necessarily improving broader reasoning.
arXiv Computer Vision
Sep 4
VeriPhy: Agentic Physical Reasoning for World Model Evaluation and Refinement
VeriPhy is an auditable physical‑verification system that transforms a text prompt into typed physical obligations and a statically validated execution plan before any video frames are generated. During execution, it gates calls to frozen low‑level experts (segmentation, tracking, counting, depth, OCR, audio‑event detection, etc.) and records provenance‑carrying evidence for each action. The system maps these records to a three‑valued state—supported, contradicted, or unknown—providing traceable verdicts that can be used to refine generation models.