arXiv AI June 8, 2026 By Yuxiang Chen, Jun Wang

A Comprehensive Anatomy of Human and DeepSeek-R1 LLM Mathematical Reasoning

arXiv:2606. 07410v1 Announce Type: cross Abstract: The emergence of "Aha moments" in large language models, particularly DeepSeek-R1-0120, has raised the question of whether these systems genuinely reason or merely imitate the appearance of reasoning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

llms

arXiv AI

Jun 6

ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Traces

arXiv:2606. 05402v1 Announce Type: cross Abstract: Large reasoning models (LRMs) produce reasoning traces with non-linear structures, such as backtracking and self-correction, that complicate the evaluation and monitoring of the reasoning process.

By Jinu Lee, Shivam Agarwal, Amruta Parulekar, Siddarth Madala, Dilek Hakkani-Tur, Julia Hockenmaier

llms

arXiv AI

Jun 10

Rotate2Think: Geometric Priming via Orthogonal Rotation to Improve Language Model Reasoning

arXiv:2606. 09873v1 Announce Type: cross Abstract: Reasoning models achieve strong performance on challenging tasks by generating explicit intermediate reasoning traces before producing a final answer.

By Aditya Sharma, Christopher J. Pal, Amal Zouaq

llms rag multimodal benchmarks

arXiv AI

Jun 3

Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models

arXiv:2606. 02835v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) improve performance by generating explicit intermediate reasoning traces through increased test-time compute, yet the assumption that longer reasoning is consistently beneficial remains under-examined.

By Simone Caldarella, Davide Talon, Rahaf Aljundi, Elisa Ricci, Massimiliano Mancini

multimodal benchmarks

arXiv Computation and Language

Sep 22

STOP: Structured On-Policy Pruning of Long-Form Reasoning in Low-Data Regimes

arXiv:2605.13165v2 Announce Type: replace Abstract: Long chain-of-thought (Long CoT) reasoning improves performance on multi-step problems, but it also induces overthinking. This inefficiency is espe...

By Chenjun Xu, Zhennan Zhou, Zhan Su, Bill Howe, Lucy Lu Wang, Bingbing Wen

llms computer-vision fine-tuning efficiency

arXiv AI

Jun 8

CORE: Contrastive Reflection Enables Rapid Improvements in Reasoning

arXiv:2605. 28742v2 Announce Type: replace Abstract: Language models can use verifiable rewards to improve at a wide variety of reasoning tasks.

By Linas Nasvytis, Simon Jerome Han, Ben Prystawski, Satchel Grant, Noah D. Goodman, Judith E. Fan

llms rag

arXiv AI

2d ago

Interpreting Reasoning of Large Language Models via Partial Information Decomposition

arXiv:2610.00571v1 Announce Type: cross Abstract: Large reasoning models (LRMs) have achieved substantial improvements in solving complex mathematical problems, but often produce lengthy, repetitive,...

By Barproda Halder, Qiuyi Zhang, Sanghamitra Dutta

llms rag fine-tuning safety

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Related stories

ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Traces

Rotate2Think: Geometric Priming via Orthogonal Rotation to Improve Language Model Reasoning

Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models

STOP: Structured On-Policy Pruning of Long-Form Reasoning in Low-Data Regimes

CORE: Contrastive Reflection Enables Rapid Improvements in Reasoning

Interpreting Reasoning of Large Language Models via Partial Information Decomposition