arXiv AI August 11, 2026 By Xinyan Wang, Xiaogeng Liu, Ming Pei, Chaowei Xiao

ROM: Real-time Overthinking Mitigation via Streaming Detection and Intervention

arXiv:2603. 22016v3 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) often reach a correct solution before their long Chain-of-Thought trace ends, yet continue with redundant verification, repeated attempts, or unnecessary exploration that wastes computation and can even overturn the correct answer.

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

benchmarks

arXiv AI

Jun 9

Why Limit the Residual Stream to Layers and Not Tokens? Persistent Memory for Continuous Latent Reasoning

arXiv:2606. 07720v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable reasoning abilities on mathematical and multi-hop planning tasks.

By Mujtaba Farhan, Maheep Chaudhary

llms diffusion

arXiv Machine Learning

2d ago

You Only Pass Once: Answering and Abstaining Together in a Single Forward Pass of a Frozen Language Model

arXiv:2608. 14465v1 Announce Type: cross Abstract: A frozen language model on reasoning tasks has two coupled weaknesses: it under-uses evidence its own residual stream already encodes, and it fails to detect when the input is insufficient to answer, so it confabulates.

By Ziyang Luo, Zhongyao Chu, Xinjie He, Youting Wang, Xukui Qin, Runxiong Wu, Yan-Syuan Chen

llms benchmarks

arXiv Machine Learning

Jun 17

Continual Self-Improvement with Lightweight Experiential Latent Memories

arXiv:2606. 17803v1 Announce Type: new Abstract: Large language models achieve strong reasoning performance by scaling inference-time compute, yet remain fundamentally stateless, discarding the rich, self-produced reasoning traces generated during this process.

By Vaggelis Dorovatas, Nancy Kalaj, Rahaf Aljundi

llms reinforcement-learning benchmarks

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 Machine Learning

Jun 18

EfficientRollout: System-Aware Self-Speculative Decoding for RL Rollouts

arXiv:2606. 18967v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a representative post-training paradigm for LLMs, enabling strong reasoning and agentic capabilities.

By Minseo Kim, Minjae Lee, Seunghyuk Oh, Kevin Galim, Donghoon Kim, Coleman Hooper, Harman Singh, Amir Gholami, Hyung Il Koo, Wonjun Kang

llms agents reinforcement-learning efficiency

arXiv Machine Learning

Jun 26

What Survives When You Compress a Recursive Reasoner for the Edge?

arXiv:2606. 26488v1 Announce Type: new Abstract: Recursive reasoning models can solve complex structured tasks with only a few million parameters by repeatedly updating a latent state.

By Pearse Jim, Steven Kolawole, Opegbemi Matthias Busoye, Glory Bagai, Virginia Smith

rag efficiency

Related stories

Why Limit the Residual Stream to Layers and Not Tokens? Persistent Memory for Continuous Latent Reasoning

You Only Pass Once: Answering and Abstaining Together in a Single Forward Pass of a Frozen Language Model

Continual Self-Improvement with Lightweight Experiential Latent Memories

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

EfficientRollout: System-Aware Self-Speculative Decoding for RL Rollouts

What Survives When You Compress a Recursive Reasoner for the Edge?