arXiv AI June 3, 2026 By Yu Xia, Zhouhang Xie, Xin Xu, Byungkyu Kang, Prarit Lamba, Xiang Gao, Julian McAuley

Agentic Chain-of-Thought Steering for Efficient and Controllable LLM Reasoning

arXiv:2606. 03965v1 Announce Type: cross Abstract: Large language models improve final-answer accuracy through extended chain-of-thought reasoning, but often spend tokens inefficiently and offer little inference-time control.

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.

arXiv AI

Jul 21

Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering

arXiv:2607. 18100v1 Announce Type: new Abstract: Extended reasoning has become standard for frontier Large Language Models (LLMs), yet the trajectories these models produce remain largely uncontrollable.

By Sheldon Yu, Tong Yu, Xunyi Jiang, Rohan Surana, Gagan Mundada, Sungchul Kim, Lina Yao, Julian McAuley, Junda Wu

llms

arXiv AI

4d ago

MetaCtrl: Your Large Language Models Can Reason Better and More Concisely with a Metacognitive Controller

arXiv:2609.37304v1 Announce Type: new Abstract: Large reasoning models improve performance on challenging problems by allocating additional computation before answering, but longer reasoning does not...

By Zhibin Wen, Tao Han, Lei Bai, Can Li, Yang Xu

llms reinforcement-learning benchmarks

arXiv AI

Aug 11

Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning in Agents

arXiv:2604. 05164v3 Announce Type: replace-cross Abstract: As LLM reasoning performance plateaus, improving inference-time compute efficiency is crucial to mitigate overthinking and long thinking traces even for simple queries.

By Neharika Jali, Anupam Nayak, Gauri Joshi

llms agents reinforcement-learning benchmarks

arXiv AI

Sep 1

AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning

arXiv:2608.29988v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong reasoning performance, which depends critically on inference-time decisions. Yet these decisions are common...

By Hanjun Luo, Qiushi Liu, Jingya Zhang, Haihong Pang, Jiaheng Wen, Yifei Ma, Yu Yao, Chengxi Zhang, Hanrong Zhang, Yankai Chen, Hanan Salam

llms benchmarks

arXiv Machine Learning

Aug 27

$R^3$: Training Robots to Reason in Natural Language via Reinforcement Learning

The paper introduces $R^3$, a post‑training method that converts vision‑language models into robotic reasoners by first mid‑training on expert reasoning traces and then refining them with single‑step rubric‑based reinforcement learning. $R^3$ enables free‑form language reasoning to guide low‑level manipulation policies, improving exploration, generalization, and performance on long‑horizon tasks in Language Table and simulated bimanual grocery packing benchmarks. The approach outperforms instruction‑only imitation learning baselines and demonstrates that natural language reasoning can serve as a test‑time compute mechanism for steering robotic actions.

By Lehong Wu, Yuxiao Qu, Zheyuan Hu, Ivan Zhang, Limin Wei, Zackory Erickson, Aviral Kumar

reinforcement-learning robotics multimodal benchmarks

arXiv AI

Jul 14

OS-Pruner: Pruning Chains-of-Thought of Reasoning Models via Optimal Stopping

arXiv:2607. 11089v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks through Chain-of-Thought (CoT) prompting.

By Mohammed Ehab, Aymane El Gadarri, Vivek F. Farias, Adam Jozefiak, Ciamac C. Moallemi

llms efficiency benchmarks

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Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering

MetaCtrl: Your Large Language Models Can Reason Better and More Concisely with a Metacognitive Controller

Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning in Agents

AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning

$R^3$: Training Robots to Reason in Natural Language via Reinforcement Learning

OS-Pruner: Pruning Chains-of-Thought of Reasoning Models via Optimal Stopping