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.

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benchmarks

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

Sep 16

State of Thought Enables Endogenous Reasoning

arXiv:2609.16055v1 Announce Type: cross Abstract: Test-time compute has emerged as a major approach to improving the capabilities of Large Language Models (LLMs). However, existing test-time reasonin...

By Zhiren Gong, Yikun Hou, Zihao Zeng, Ming Xiao, Chau Yuen, Wei Yang Bryan Lim

llms rag multimodal

arXiv AI

Sep 1

Drop the Act: Probe-Filtered RL for Faithful Chain-of-Thought Reasoning

arXiv:2605.11467v2 Announce Type: replace-cross Abstract: Reasoning models post-hoc rationalize answers they have already committed to internally, producing chains of *reasoning theater*: deliberativ...

By Swapnil Parekh, Naman Goyal

llms reinforcement-learning benchmarks safety

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 Computation and Language

Aug 28

TRACES: Tagging Reasoning Steps for Adaptive Cost-Efficient Early-Stopping

TRACES (Tagging Reasoning Steps for Adaptive Cost‑Efficient Early‑Stopping) is a lightweight framework that tags reasoning steps of large‑language models in real time, enabling adaptive, cost‑efficient early stopping during inference. By monitoring the types of steps generated, the method identifies when models shift their reasoning after arriving at a correct answer, allowing for interpretable stopping criteria. Experiments on mathematical reasoning benchmarks (MATH500, GSM8K, AIME) and knowledge benchmarks (MMLU, GPQA) show token reductions of 20–50% while preserving accuracy, with more conservative thresholds needed for harder tasks such as BeyondAIME and IMO AnswerBench.

By Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo, John D. Kelleher

benchmarks

arXiv Machine Learning

Aug 17

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

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

State of Thought Enables Endogenous Reasoning

Drop the Act: Probe-Filtered RL for Faithful Chain-of-Thought Reasoning

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

TRACES: Tagging Reasoning Steps for Adaptive Cost-Efficient Early-Stopping

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