arXiv AI August 11, 2026 By Dahai Yu, Lin Jiang, Rongchao Xu, Guang Wang

SymboUQ: Symbolic Uncertainty Quantification for Spatial Reasoning in LLMs

arXiv:2608. 00417v2 Announce Type: replace Abstract: Although large language models (LLMs) can produce fluent spatial reasoning traces, their intermediate relations may fail to support the final conclusion, making token-level confidence insufficient for final-answer reliability estimation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Aug 11

SymDiag: Explainable Diagnosis for LLM Reasoning via Neuro-Symbolic Verification

arXiv:2608. 08786v1 Announce Type: new Abstract: Large language models (LLMs) increasingly serve as data-driven reasoners, yet their chains-of-thought (CoT) can be unfaithful even when final answers are correct.

By Wenyao Cui, Huaping Zhang, Yongyi Huang, Qiuchi Li, Jian Xu, Cheng-Lin Liu, Chunxiao Gao, Juan Wang, Baohua Zhang

llms benchmarks

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 1

Measuring Reasoning Quality in LLMs: A Multi-Dimensional Behavioral Framework

arXiv:2605. 24661v2 Announce Type: replace Abstract: LLMs have achieved remarkable success in complex reasoning tasks, yet current evaluation approaches predominantly rely on final-answer correctness, offering limited insight into the underlying reasoning processes that produce those answers.

By Ali \c{S}enol, Garima Agrawal, Huan Liu

llms benchmarks

arXiv AI

Aug 5

The Tell-Tale Trace: Detecting Reasoning Failures in LLMs Using Chain-of-Thought Dynamics

arXiv:2608. 03291v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning improves large language model (LLM) performance while also providing an observable interface to the model's reasoning process.

By Shashwat Sourav, Aishwarya Balwani

llms

arXiv AI

Jun 3

Quantifying Faithful Confidence Expression in Large Reasoning Models

arXiv:2606. 03969v1 Announce Type: cross Abstract: Reliable uncertainty communication is critical to the trustworthiness of LLMs, yet faithful calibration (FC)--the alignment between models' intrinsic and (linguistically) expressed confidence--is a persistent failure mode.

By Areeb Gani, Asal Meskin, Gabrielle Kaili-May Liu, Arman Cohan

llms safety

arXiv AI

Jul 31

A Reference-Free Score for Detecting Silent Reasoning Failures in Large Language Models

arXiv:2607. 26102v1 Announce Type: cross Abstract: Mathematical chain of thought (CoT) evaluation is commonly reduced to whether the final answer matches a reference.

By Vivek Shukla, Varun Shukla, Atul, Divya Mishra, Mehul Kumar Das

llms

Related stories

SymDiag: Explainable Diagnosis for LLM Reasoning via Neuro-Symbolic Verification

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

Measuring Reasoning Quality in LLMs: A Multi-Dimensional Behavioral Framework

The Tell-Tale Trace: Detecting Reasoning Failures in LLMs Using Chain-of-Thought Dynamics

Quantifying Faithful Confidence Expression in Large Reasoning Models

A Reference-Free Score for Detecting Silent Reasoning Failures in Large Language Models