arXiv Machine Learning June 2, 2026 By Shuochen Chang, Tong Bai, Xiaofeng Zhang, Qianli Ma, Qingyang Liu, Zhaohe Liao, Yibo Miao, Li Niu

Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention

Read the original on arXiv Machine Learning →

arXiv:2606. 01243v1 Announce Type: cross Abstract: Latent reasoning enables Large Language Models (LLMs) to perform multi-step inference within continuous hidden states, offering efficiency gains over explicit Chain-of-Thought (CoT).

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 Machine Learning.

arXiv Machine Learning

Sep 22

Assessing Adversarial Robustness of Latent Reasoning Models

arXiv:2609.22228v1 Announce Type: cross Abstract: Large language models increasingly rely on long chain-of-thought (CoT) trajectories for complex reasoning, but autoregressive generation brings subst...

By Shaolong Chen, Ang Li, Mingjie Li, Yisen Wang

llms multimodal benchmarks safety

arXiv Machine Learning

Jun 19

What Makes Effective Supervision in Latent Chain-of-Thought: An Information-Theoretic Analysis

arXiv:2606. 20075v1 Announce Type: new Abstract: Latent Chain-of-Thought (CoT) internalizes reasoning within continuous hidden states, offering a promising alternative to verbose discrete reasoning traces.

By Xinghao Chen, Chak Tou Leong, Wenjin Guo, Jian Wang, Wenjie Li, Xiaoyu Shen

diffusion nlp

arXiv AI

Sep 1

Triggering Chain-of-Thought via Latent Feature Interventions in Large Language Models

arXiv:2601.08058v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting often improves the reasoning performance of large language models (LLMs), but the internal signal that trigg...

By Zhenghao He, Guangzhi Xiong, Bohan Liu, Sanchit Sinha, Aidong Zhang

llms benchmarks

arXiv Computation and Language

Sep 1

Decompose, Look, and Reason: Reinforced Latent Reasoning for VLMs

arXiv:2604.07518v2 Announce Type: replace Abstract: Vision-Language Models often struggle with complex visual reasoning due to the visual information loss in textual CoT. Existing methods either add...

By Mengdan Zhu, Senhao Cheng, Liang Zhao

llms rag diffusion multimodal benchmarks safety

arXiv Computation and Language

Sep 1

Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning

arXiv:2505.16782v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT...

By Xinghao Chen, Anhao Zhao, Heming Xia, Xuan Lu, Hanlin Wang, Yanjun Chen, Wei Zhang, Jian Wang, Wenjie Li, Xiaoyu Shen

llms nlp

arXiv AI

Aug 17

Think in Latent, Explain in Language: Self-Explainable Latent Reasoning

arXiv:2608. 13570v1 Announce Type: cross Abstract: Latent reasoning has emerged as a powerful alternative to text-based Chain-of-Thought (CoT), offering significant gains in computational efficiency by compressing verbose reasoning into compact embeddings.

By Dayuan Zhao, Shengcao Cao, Yu-Xiong Wang, Liang-Yan Gui

llms rag diffusion multimodal safety

One email a morning, machine-written

Related stories

Assessing Adversarial Robustness of Latent Reasoning Models

What Makes Effective Supervision in Latent Chain-of-Thought: An Information-Theoretic Analysis

Triggering Chain-of-Thought via Latent Feature Interventions in Large Language Models

Decompose, Look, and Reason: Reinforced Latent Reasoning for VLMs

Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning

Think in Latent, Explain in Language: Self-Explainable Latent Reasoning