The paper investigates how verifier feedback influences self‑repair in formal theorem proving. Using controlled one‑step experiments on Lean with frozen Qwen3.6‑27B and DeepSeek‑Prover‑V2‑7B, the authors find that showing a model its failed predecessor can hurt success and increase copying, while providing the corresponding compiler error can partially recover performance. In sequential trials, Qwen benefits from correct diagnostics within failed‑proof histories, but no feedback condition outperforms simple instruction‑only resampling, indicating that verifier feedback can aid repair yet cannot fully offset the cost of conditioning on a failed attempt.
By Jack Li, Sophia Li
arXiv:2610.19298v1 Announce Type: new
Abstract: Reinforcement learning for code usually rewards passing tests, which a model can do while remaining wrong on the inputs the tests do not cover. A progr...
By George Ma, Yuhao Zhang, Bing He, Zeren Shui, Ferhat Erata, Murali Krishna Ramanathan, Somayeh Sojoudi, Hao Zhou
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.
By Mushir Akhtar
The paper introduces SymCE, a dataset of 4,707 false undergraduate‑algebra and real‑analysis conjectures each paired with a deterministic Python verifier that can be executed to check truth. It studies counterexample generation by training a 4B‑parameter language model (Qwen3‑4B) first with supervised fine‑tuning and then with reinforcement learning (GRPO) using the verifier as a reward. The results show that counterexample‑only fine‑tuning can destroy true‑theorem recognition, while reinforcement learning with a sparse outcome‑only reward restores and surpasses baseline performance, achieving higher success rates than larger open‑weight models and competitive performance on other math benchmarks.
"whyItMatters":"The study demonstrates that reinforcement learning guided by a per‑theorem verifier can effectively repair the shortcomings of supervised fine‑tuning in theorem proving, offering a practical approach to improve language models’ mathematical reasoning capabilities."
By Omar Farouk Zouak, Houssam Eddine Boukhalfa, Soumaya Lakehal, Shiv Katiyar, Samia Nefti-Meziani
The paper introduces TrustSwap, a counterfactual test that swaps or removes source reliability labels while keeping evidence text constant, to evaluate how retrieval‑augmented fact‑checking models respond across verdict, confidence, and search decisions. Experiments on untrained and RL‑trained models show that confidence and search largely follow labels, yet label changes can flip a significant portion of verdicts, especially in larger models. The authors propose trust‑swap augmentation (TSA) to mitigate this shortcut, demonstrating reduced verdict flip rates and maintained accuracy in several settings, though its effectiveness diminishes at larger model scales.
By Jianchang Su, Yiwei Yang, Wei Zhang
arXiv:2607. 23386v1 Announce Type: new Abstract: We document a failure class in frontier large language models -- exception chain collapse -- observed in eligibility evaluation under nested conditional rules of the form "A is required UNLESS B applies, UNLESS C overrides B".
By Paul Simpson, John Kozak, Lisa Doake
The study investigates how language models equipped with tools can still produce unsupported final claims, even when a single tool call could resolve the uncertainty. It defines two metrics—occurrence (how often unsupported claims arise) and conditional repair (how often they are fixed when evidence is provided). Experiments on Qwen3-32B and Gemma 4 show that providing the missing evidence consistently repairs all unsupported claims in the Qwen3-32B setup, while the Gemma 4 model never produced unsupported claims under the tested conditions.
By Justin Bronder
The paper investigates whether an agent’s own verifier remains reliable when the agent is optimized against it. It introduces a stress‑testing framework that measures the pressure ratio ρ, the influence of the verifier on the agent’s search, and four falsifiable probes targeting common failure modes. Applying this framework to the Rationale Consistency Score (RCS) in an LLM agent that designs machine‑learning pipelines, the authors find that the verifier’s impact is minimal in their baseline setup, that agents can shorten claim lists without affecting search, and that pre‑commit probing can hide declines in faithfulness. The study concludes with a proposed reporting standard to audit verifiers before trusting them.
By Jonghyun Lee, Moonsu Kim, Sunghwan Park
VERITYGATE is a four‑gate framework that checks whether LLM‑generated narrations adhere to a fixed schema of declared evidence IDs, entities, numbers, and claim types, rather than verifying every fact in the prose. In experiments with GPT‑4o‑mini, Llama‑3.3‑70B, and Claude Sonnet 4.6 on 900 grounded‑ungrounded pairs, the framework identified high failure rates (up to 80.3% for mini claims) and demonstrated that a single repair pass can improve claim survival rates. The authors also provide code, data, and preliminary human studies to validate the rules and highlight gaps between schema compliance and correct prose.
"whyItMatters":"The framework offers a systematic way to evaluate and improve the faithfulness of LLM explanations to structured evidence, revealing significant failure rates and the impact of repair strategies."
By Sachin Gupta
arXiv:2609.26035v1 Announce Type: new
Abstract: Conversational agents often express answers in a uniformly confident register. We test whether expressed uncertainty, provenance-aware assertion, and e...
By Sebastian Cochinescu
arXiv:2608. 07813v1 Announce Type: new Abstract: An LLM judge deployed inside a reasoning pipeline does not merely measure quality, it decides which answer ships.
By Yiyao Zhang, Diksha Goel, Hussain Ahmad, Shixun Huang, Jun Shen
The paper shows that providing correctness feedback in the Verus/Rust verification workflow actually removes the evidence needed for completeness auditing, leading to a significant drop in certified witness yield compared to no‑feedback approaches. Experiments on 12 tasks across four model families demonstrate that independent discovery without feedback more than doubles the yield (59.0% vs. 27.1%) and that a phase‑separated execution can recover this yield without reusing responses. Preserving these witnesses also improves specification repair, with Phase‑Revised contracts achieving 81.3% implementation success versus 62.5% for IS‑Revised, while the effect does not appear in Dafny or Lean evaluations, indicating a specific limitation of the Verus/Rust workflow.
By Adarsh Agrawal (Independent Researcher)