Reasoning Verification

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Can Language Models Learn to Reject Their Own Bad Reasoning Steps?
Oct 5, 2026Siheng Xiong, Xiaoze Liu, Yiqiao Jin +2LLM Self-Correction Language Model Decoding
Georgia Institute of Technology · Purdue University
Hallucination Across the Reasoning Lifecycle: Interface Visibility, Causal Evidence, and Release Control in Large Reasoning Models
Oct 4, 2026Zhe Yu, Mohan Li, Lei Yu +6LLM Hallucination Mitigation Hallucination in Language Models
Zhejiang University, China · Binjiang Institute of Zhejiang University, China · Guangzhou University, China +5
The Error You See Is Not the Error You Made: Progression-aware Reasoning Origin for Reasoning Error Localization
Sep 27, 2026Yiguo Wang, Ziyuan Yang, Yi Zou +3Language Model Error Detection Reasoning Verification
Xiaopeng Honors College, Nanchang Hangkong University, China · School of Cyber Science and Engineering, Sichuan University, China · School of Software, Nanchang Hangkong University, China +1
Deposon: An Auditable, Conservation-Guaranteed, Game-Theoretically Tested Scattering Layer over LLM Reasoning Paths
Sep 8, 2026Qihao YuanLLM Reasoning with Graphs LLM Auditing
School of Chemistry and Life Resources, Renmin University of China
ReflectFact: Self-Reflective Agents for Improving Comprehension and Reasoning in Multi-Hop Fact Verification
Aug 13, 2026Runze Zhao, Zixin Tang, Xiaoshuai Hao +4Automated Fact-Checking Agentic Reasoning
Institute of Information Engineering, Chinese Academy of Sciences · 1Zhongguancun Laboratory · 3Xiaomi EV
A Human Audit of OpenAIs AI-Generated Mathematical Proofs
Aug 3, 2026Mikołaj Sienicki, Krzysztof SienickiLLM Auditing Human Oversight of AI
Polish-Japanese Academy of Information Technology ul. Koszykowa 86, 02-008 Warsaw, Poland, European Union · Chair of Theoretical Physics of Naturally Intelligent Systems (NIS)1 ul. Lipowa 2 / Topolowa 19, 05-807 Podkowa Leśna, Poland, European Union
Answer-Conditioned Chains of Thought Degrade Verifiable-Reasoning Distillation in Large Language Models
Jul 16, 2026Jungseob Lee, Seungyoon Lee, Suhyune Son +4CoT Distillation Language Model Distillation
1Korea University · 2Zoom Communications · 3Yonsei University
Evidence-Grounded Verified Agentic Reasoning: A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel Proofs
Jul 14, 2026Junyu RenLLM Hallucination Mitigation Evidence-Grounded Reasoning
Committee on Computational and Applied Mathematics, University of Chicago, Chicago, IL, USA.
Theoria: Rewrite-Acceptability Verification over Informal Reasoning States
Jul 1, 2026Michael Saldivar, Ben SlivinskiLLM Answer Verification LLM Auditing
Independent Researchers
Think Again or Think Longer? Selective Verification for Budget-Aware Reasoning
Jun 18, 2026Sajib Acharjee Dip, Dawei Zhou, Liqing ZhangLLM Inference Efficiency Cost-Aware Inference
Department of Computer Science, Virginia Tech · Fralin Biomedical Research Institute, Virginia Tech · FBRI Cancer Research Center, Washington, DC
Scaling LLM Reasoning from Minimal Labels: A Semi-Supervised Framework with a Lightweight Verifier
Jun 15, 2026Keizo Kato, Chenhui Chu, Yugo Murawaki +1Pseudo-Labeling LLM Reasoning
Fujitsu Limited, Kawasaki, Japan · Kyoto University, Kyoto, Japan · National Institute of Informatics, Tokyo, Japan
VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models
Jun 15, 2026Sen Xu, Shixi Liu, Wei Wang +6Small Language Models Reasoning Verification
Sina Weibo Inc.
Odds Law: The Decomposition Algebra On How Intelligence Organizes Itself to Solve Difficult Problems Reliably
Jun 14, 2026Hidayet AksuAgent Reliability Ensemble Learning
VeriGeo: Controllable Geometry Question Generation with Numerical and Analytical Verification
Jun 12, 2026Xiaoxian Duan, Zequn Liu, Yingce XiaSynthetic Data Generation Multimodal Reasoning
Institute of Automation, Chinese Academy of Sciences, Beijing, China · Zhongguancun Academy, Beijing, China
Evaluating Research-Level Math Proofs via Strict Step-Level Verification
Jun 9, 2026Yifeng SunLLM Answer Verification Automated Theorem Proving
Independent Researcher
An Enigma of Artificial Reason: Investigating the Production-Evaluation Gap in Large Reasoning Models
May 31, 2026Mingzhong Sun, Teresa Yeo, Armando Solar-Lezama +1Reasoning Evaluation LLM Interpretability
1NUS Department of Computer Science · 4Singapore-MIT Alliance for Research and Technology (SMART) · 2MIT EECS +1
Self-Trained Verification for Training- and Test-Time Self-Improvement
May 28, 2026Chen Henry Wu, Aditi RaghunathanLLM Self-Refinement Language Model Self-Improvement
Carnegie Mellon University
Conformal Certification of Reasoning Trace Prefixes
May 28, 2026Matt Y. Cheung, Ashok Veeraraghavan, Hanjie Chen +1Conformal Prediction Language Model Calibration
Department of Electrical & Computer Engineering, Rice University · Department of Computer Science, Rice University
Debate Helps Weak Judges Reward Stronger Models
May 26, 2026Ethan Elasky, Frank Nakasako, Naman GoyalScalable Oversight LLM-as-a-Judge
Palaestra Research · Independent
The Hidden Signal of Verifier Strictness: Controlling and Improving Step-Wise Verification via Selective Latent Steering
May 20, 2026Yefan Zhou, Yilun Zhou, Austin Xu +3Language Model Steering Activation Steering
Dartmouth College · Datadog AI Research · Salesforce AI Research
Using Aristotle API for AI-Assisted Theorem Proving in Lean 4: A Formalisation Case Study of the Grasshopper Problem
May 19, 2026Gabriel Rongyang LauMathematical Reasoning Lean Theorem Proving
Nanyang Technological University, Singapore
A Nash Equilibrium Framework For Training-Free Multimodal Step Verification
May 19, 2026Rohit Sinha, Kunal Tilaganji, Tanuja Ganu +3Multi-View Consistency Game Theory
1Microsoft Research India · 2Indian Institute of Technology Hyderabad
Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains
May 18, 2026Zhonghang Yuan, Zhefan Wang, Fang Hu +7RL for Language Model Reasoning Reinforcement Learning with Verifiable Rewards
Shanghai Artificial Intelligence Laboratory · Shanghai Innovation Institute
Unsupervised Process Reward Models
May 11, 2026Artyom Gadetsky, Maxim Kodryan, Siba Smarak Panigrahi +2Reward Modeling Process Reward Models
Swiss Federal Institute of Technology (EPFL)
LaTER: Efficient Test-Time Reasoning via Latent Exploration and Explicit Verification
May 8, 2026Xuan Li, Yining Wang, Yuchen Liu +7CoT Reasoning Efficient Language Model Reasoning
University of Science and Technology of China · Unisound AI Technology Co., Ltd · MAIS, Institute of Automation, Chinese Academy of Sciences
Verifiable Counterfactual Supervision for Process Reward Models
May 4, 2026Yinghui Chi, Yuanhong WangProcess Reward Models Process Supervision
Jilin University
Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization
May 2, 2026Yunhan Bu, Quan Zhang, Huaping Zhang +9Structural Causal Models RL for Language Model Reasoning
Beijing Institute of Technology, Beijing, China · School of Computer Science and Technology, Xinjiang University, Urumqi, China · Military Science Information Research Center, Academy of Military Science, Beijing, China
Navigating the Conceptual Multiverse
Apr 20, 2026Andre Ye, Jenny Y. Huang, Alicia Guo +3LLM Decision-Making Human-AI Decision Making
MIT EECS · UW Allen School of CSE · UW Department of Philosophy
Reasoning on the Manifold: Bidirectional Consistency for Self-Verification in Diffusion Language Models
Apr 17, 2026Jiaoyang Ruan, Xin Gao, Yinda Chen +5Diffusion Model Alignment Diffusion Language Model Inference
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China · Shanghai Artificial Intelligence Laboratory, Shanghai, China · University of Science and Technology of China, Hefei, China +1
Auditing Multi-Agent LLM Reasoning Trees Outperforms Majority Vote and LLM-as-Judge
Feb 10, 2026Wei Yang, Shixuan Li, Heng Ping +3LLM Auditing Preference Optimization
University of Southern California
The Bidirectional Process Reward Model
Aug 3, 2025Lingyin Zhang, Jun Gao, Xiaoxue Ren +1Process Reward Models Reasoning Verification
School of Computer Science and Technology, Soochow University · Biomedical Basic Research Center of Jiangsu, Soochow University, Suzhou, Jiangsu 215123, China · School of Software Technology, Zhejiang University +1
Measuring Progress in Reasoning Toward Mathematical Discovery with Automatic Verification
Date pendingErik Y. Wang, Sumeet R. Motwani, James V. Roggeveen +9Mathematical Reasoning Benchmarks LLM Mathematical Reasoning
Stanford University · Benchmark · University of Oxford +3

Oct 5, 2026, cs.CLJ/K move · Enter open · S save

Can Language Models Learn to Reject Their Own Bad Reasoning Steps?

Siheng Xiong, Xiaoze Liu, Yiqiao Jin, Xiaoqian Wang+1

Georgia Institute of Technology · Purdue University

LLM Self-Correction Language Model Decoding Efficient Language Model Reasoning LLM Reasoning Reasoning Verification

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Verifier-guided decoding can prevent harmful reasoning steps from contaminating subsequent generation, but typically relies on an external learned verifier. We ask whether a language model can instead reject its own bad reasoning steps. We define a prefix's recoverability as the probability that the frozen generator can complete it correctly. Diagnostics show that adjacent recoverability changes are often difficult to resolve with practical Monte Carlo budgets, while same-prefix candidates exhibit a sparse low-recoverability tail. We introduce Self-Step Rejection (SSR), which trains a lightweight LoRA acceptance gate on the generator backbone while keeping the base model frozen. SSR uses confidence-qualified first-passage supervision: steps before the first resolved crossing of a root-relative recoverability barrier are accepted, the crossing step is rejected, and unresolved steps and suffixes are excluded. Training combines pointwise classification, same-prefix pairwise learning, and group-relative policy refinement using final-answer correctness. At inference, SSR accepts candidates or resamples from the unchanged prefix under rejection budgets, without an external learned verifier. Across three reasoning models and five mathematical reasoning benchmarks, SSR improves macro-average accuracy over single-pass decoding by 5.4--10.1 points using 1.21--1.40x as many generated tokens, and achieves the highest macro-average accuracy among evaluated step-level methods. Full-solution scaling methods require 4.47--8.27x the single-pass token cost for comparable performance.

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