Reasoning Consistency in Language Models

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Self-Consistency via Marginal Sharpening
May 27, 2026Aleksei Arzhantsev, Otmane Sakhi, Nicolas ChopinEfficient Language Model Reasoning Reasoning Consistency in Language Models
Criteo AI Lab, CREST IP Paris · Criteo AI Lab · ENSAE, Institut Polytechnique de Paris
Do Models Know Why They Changed Their Mind? Interpretability and Faithfulness of Chain-of-Thought Under Knowledge Conflict
May 26, 2026Pruthvinath Jeripity VenkataCoT Faithfulness Faithfulness of Language Model Explanations
Independent Researcher
Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning
May 22, 2026Muhammad Usama, Dong Eui ChangRepresentational Similarity Analysis LLM Interpretability
Control Laboratory, School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141, Republic of Korea · Control Laboratory, School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST)2026 Daejeon 34141, Republic of Korea
Grounded Continuation: A Linear-Time Runtime Verifier for LLM Conversations
May 13, 2026Qisong He, Jinwei Hu, Xinmiao Huang +3Belief Revision LLM Grounding
School of Computer Science & Informatics, University of Liverpool
Controlling Logical Collapse in LLMs via Algebraic Ontology Projection over F2
May 13, 2026Hisashi Miyashita, Mgnite IncLLM Interpretability Reasoning Consistency in Language Models
Mgnite Inc.
DGPO: Beyond Pairwise Preferences with Directional Consistent Groupwise Optimization
May 11, 2026Mengyi Deng, Zhiwei Li, Xin Li +4LLM Alignment Reasoning Consistency in Language Models
1Information Hub, The Hong Kong University of Science and Technology (Guangzhou), China · 2The Hong Kong University of Science and Technology, Hong Kong SAR
Reliable Chain-of-Thought via Prefix Consistency
May 8, 2026Naoto Iwase, Yuki Ichihara, Mohammad Atif Quamar +1Self-Consistency Decoding Reasoning Consistency in Language Models
Nagoya University · Nara Institute of Science and Technology · Mohamed bin Zayed University of Artificial Intelligence +1
Towards Annotation-Free Validation of MLLMs: A Vision-Language Logical Consistency Metric
May 7, 2026Ying Gu, Mei Chee Leong, Hui Li Tan +3VLM Evaluation Large Vision-Language Models
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore
Logic-Regularized Verifier Elicits Reasoning from LLMs
May 7, 2026Xinyu Wang, Changzhi Sun, Lian Cheng +4LLM Evaluation LLM Answer Verification
Department of Computer Science and Technology, East China Normal University · Institute of Artificial Intelligence (TeleAI), China Telecom
Self-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM Reasoning
Apr 19, 2026Raman Saparkhan, Majd Hawasly, Md Rizwan Parvez +1LLM Inference Efficiency Self-Consistency Decoding
Carnegie Mellon University Qatar · Qatar Computing Research Institute
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
When Agents Disagree With Themselves: Behavioral Consistency as an Uncertainty Signal for LLM Agents
Feb 12, 2026Aman MehtaAI Agent Reliability Selective Prediction
Snowflake AI Research
Optimal Self-Consistency for Efficient Reasoning with Large Language Models
Nov 15, 2025Austin Feng, Marius Alonso, Ambroise Odonnat +2Self-Consistency Decoding Inference-Time Scaling
1Yale University, New Haven, CT, USA · 2Noah’s Ark Lab, Paris, France · 3Inria, Rennes, France +1
CLARity: Reasoning Consistency Alone Can Teach Reinforced Experts
Oct 10, 2025Jiuheng Lin, Cong Jiang, Zirui Wu +2RL for Language Models RL for Language Model Reasoning
Wangxuan Institute of Computer Technology, Peking University

May 27, 2026, cs.LGJ/K move · Enter open · S save

Self-Consistency via Marginal Sharpening

Aleksei Arzhantsev, Otmane Sakhi, Nicolas Chopin

Criteo AI Lab, CREST IP Paris · Criteo AI Lab · ENSAE, Institut Polytechnique de Paris

Efficient Language Model Reasoning Reasoning Consistency in Language Models Efficient Language Model Inference Self-Consistency in Language Models LLM Reasoning

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Inference-time sampling can elicit strong reasoning abilities from language models without additional training. Existing power-sampling methods do so by sharpening the distribution over full generated outputs, favoring completions that are individually likely under the model. We argue that this is the wrong object to target for reasoning: a completion entangles a reasoning trace with a final answer, whereas what matters is whether an answer is supported by many plausible reasoning paths. We therefore shift the target from the full-output distribution to the sharpened answer marginal, making self-consistency an inference-time objective rather than a post-hoc voting criterion. Surprisingly, this marginal target admits an efficient approximation: we propose a simple, purely autoregressive parallel sampling algorithm that approximately samples from the sharpened answer marginal, eliciting stronger performance than standard power sampling on mathematics and coding benchmarks while being orders of magnitude faster.

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