arXiv Machine Learning

Route-Verify-Vote: Procedure-Conditioned Self-Consistency for Mixed-Domain Reasoning

Route-Verify-Vote (RVV) is a framework that applies procedure-conditioned self-consistency to mixed-domain reasoning without updating model parameters. It selects a domain-specific reasoning procedure, verifies each option against constraints, and aggregates answer sets, allocating extra samples to questions with close vote margins. On the SCoRE 2026 benchmark, RVV achieves 74.6% exact-set accuracy, improving to 77.3% with adaptive sampling and 79.4% when combining models on selected domain routes, earning second place among participants.

arXiv AI

Oct 2

ReSolve: Reusing Candidate Reasoning through Selective Generative Moderation

ReSolve is a training‑free inference method that reuses candidate reasoning by selectively moderating generative outputs. It examines existing derivations when candidates disagree or lack a parseable answer, then incorporates new solutions into a bounded loop. On 130 competition‑mathematics problems, ReSolve achieves 100 and 99 correct answers with significantly fewer tokens than eight‑sample self‑consistency, while a controlled ablation shows that visible derivations improve accuracy.

By Bangji Yang, Jiajun Fan, Hongba Ma, Xi Zhu, Weizhi Zhang, Minghao Guo, Ye Li, Hamid Palangi, Jiaxuan You

benchmarks

arXiv AI

Oct 1

ArgGYM: A Procedural, Engine-Verified Benchmark for Structured Defeasible Reasoning

arXiv:2609.38409v1 Announce Type: new Abstract: Recent progress in large language model reasoning has been driven by benchmarks and reinforcement learning environments with automatically verifiable r...

By \.Ibrahim Ethem Deveci, Funda Tan \c{C}al{\i}k, Bar{\i}\c{s} Deniz Sa\u{g}lam, Duygu Ataman

llms reinforcement-learning benchmarks

arXiv AI

Aug 12

VERDICT: Training-Free Step-Wise Verification of Multimodal Reasoning via Disagreement-Aware Consensus

arXiv:2608. 10665v1 Announce Type: new Abstract: Multimodal large language models often generate reasoning chains containing subtle errors that lead to incorrect answers.

By Rohit Sinha, Kunal Tilaganji, Tanuja Ganu, Nagarajan Natarajan, Amit Sharma, Vineeth Balasubramanian

llms multimodal benchmarks

arXiv Machine Learning

Aug 11

Rethinking Reasoning with MDLMs: Early Exits, Post-hoc Reasoning, and Beyond

arXiv:2510. 19990v2 Announce Type: replace Abstract: The reasoning paradigm, where language models reason before answering, has enabled breakthroughs on tasks such as mathematical problem-solving.

By Zachary Horvitz, Raghav Singhal, Hao Zou, Carles Domingo-Enrich, Zhou Yu, Rajesh Ranganath, Kathleen McKeown

llms diffusion reinforcement-learning fine-tuning

arXiv AI

Jul 1

Modality-Driven Search with Holistic Trace Judging for ARC-AGI-2

arXiv:2606. 31543v1 Announce Type: new Abstract: Large language models can produce fluent, internally coherent reasoning traces for abstract reasoning tasks while still being confidently wrong - making selection among candidates, not just generation, the central challenge.

By Johan Land

llms benchmarks

arXiv Computation and Language

Sep 15

Who Benchmarks the Benchmarks? Towards Comprehensive Evaluation of Commonsense Reasoning Benchmarks

arXiv:2504.07825v2 Announce Type: replace Abstract: Commonsense reasoning is a key language model capability, as it is purportedly a prerequisite for many basic tasks, unlike specific factual knowled...

By Pavel Chizhov, Anton Changalidis, Vishnu Prasad Vijaya Kumar, Yannick Detrois, Mattia Nee, Pierre-Carl Langlais, Ivan P. Yamshchikov

llms benchmarks

arXiv AI

Jul 14

Filtered Reasoning Score: Evaluating Reasoning Quality on a Model's Most-Confident Traces

arXiv:2604. 11996v2 Announce Type: replace-cross Abstract: Should we trust Large Language Models (LLMs) with high accuracy?

By Manas Pathak, Xingyao Chen, Shuozhe Li, Amy Zhang, Liu Leqi

llms benchmarks

arXiv AI

Oct 15

Large Decision Models: Repurposing Large Language Models as Single-Pass Decision Makers

arXiv:2610.15501v1 Announce Type: new Abstract: Large language models make decisions through parallel prefill and sequential decoding. Although effective, this approach incurs decoding costs and does...

By Ziyi Chen, Yan Zhang, Jianhui Wei, Jianing Yin, Daoan Zhang

llms efficiency

arXiv AI

Oct 15

VERGE: Formal Refinement and Guidance Engine for Verifiable LLM Reasoning

arXiv:2601.20055v3 Announce Type: replace-cross Abstract: Despite the syntactic fluency of Large Language Models (LLMs), ensuring their logical correctness in high-stakes domains remains a fundamenta...

By Vikash Singh, Darion Cassel, Nathaniel Weir, Nick Feng, Sam Bayless

llms benchmarks safety

arXiv Computation and Language

Sep 18

Schema-Anchored Latent Reasoning for Semantic Parsing-Based Knowledge Base Question Answering

The paper introduces SALR, a schema‑anchored latent reasoning approach for generating logical forms in knowledge‑base question answering. SALR delays explicit schema commitments by generating continuous thoughts in hidden states and aligns these thoughts with a codebook of KB schema elements, guided by an alignment objective derived from gold logical forms. Experiments on GrailQA and WebQSP demonstrate that SALR consistently outperforms strong baselines, notably improving compositional question performance by 2.86 F1 points over TIARA.

By Guangze Gao, Zixuan Li, Sikui Zhang, Chunfeng Yuan, Wenjuan Li, Bing Li, Xiaolong Jin, Weiming Hu

llms rag nlp safety

arXiv AI

Aug 12

TrAC: Trace-Conditioned Answer Consistency for Efficient Uncertainty Quantification in LLMs

arXiv:2608. 00422v2 Announce Type: replace Abstract: Large language models (LLMs) can generate fluent reasoning traces that nevertheless lead to incorrect answers, making response-level uncertainty estimation important for abstention, human review, and adaptive compute allocation.

By Dahai Yu, Lin Jiang, Rongchao Xu, Guang Wang

llms benchmarks

arXiv AI

Jul 15

Rethinking Reward Models for Multi-Domain Test-Time Scaling

arXiv:2510. 00492v3 Announce Type: replace Abstract: The reliability of large language models (LLMs) during test-time scaling is often assessed with \emph{external verifiers} or \emph{reward models} that distinguish correct reasoning from flawed logic.

By Dong Bok Lee, Seanie Lee, Sangwoo Park, Minki Kang, Jinheon Baek, Dongki Kim, Dominik Wagner, Jiongdao Jin, Heejun Lee, Tobias Bocklet, Jinyu Wang, Jingjing Fu, Sung Ju Hwang, Jiang Bian, Lei Song

llms reinforcement-learning

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