arXiv Machine Learning June 19, 2026 By Xinghao Chen, Chak Tou Leong, Wenjin Guo, Jian Wang, Wenjie Li, Xiaoyu Shen

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

Read the original on arXiv Machine Learning →

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.

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

Jun 2

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

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).

By Shuochen Chang, Tong Bai, Xiaofeng Zhang, Qianli Ma, Qingyang Liu, Zhaohe Liao, Yibo Miao, Li Niu

llms safety

arXiv Machine Learning

Sep 10

Think Wider: Mitigating Latent Rank Collapse in Implicit Chain-of-Thought Reasoning

arXiv:2609.07406v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning improves the reasoning ability of large language models by introducing intermediate computation, but explicit rational...

By Yuwen Hao, Menglin Yang

llms

arXiv AI

Jun 30

Invariant Reasoning Directions in Latent Trajectories of Language Models

arXiv:2606. 29164v1 Announce Type: cross Abstract: Latent reasoning models perform multi-step inference directly in hidden-state space, yet the structure of these latent reasoning trajectories remains poorly understood.

By Arun Vignesh Malarkkan, Manan Roy Choudhury, Utkarsh Byahut, Yash Ravindra Charde, Vivek Gupta, Yanjie Fu

llms diffusion benchmarks safety

arXiv AI

Sep 10

A*-Thought-V2: Efficient Latent Reasoning via Geometric Dynamics of LLM

A*-Thought-V2 is a framework that models Chain-of-Thought reasoning as a geometric trajectory in a 3D PCA space, using explicit-implicit latent tokens to compress steps that deviate from the main question-to-solution direction. The method measures alignment angles to decide which steps remain text and which become latent, and introduces stepwise embedding forcing and label forcing to train the architecture. Experiments on Qwen models show up to 2.6% accuracy gains, halved response length, and significant reductions in computation and training time.

By Xiaoang Xu, Siyuan Liu, Shuo Wang, Junlan Feng, Fanyu Meng, Zhu Zhang, Jixun Wang, Xiaorong Wang, Zihan Zhou, Xin Li, Chaojun Xiao, Yiming Zhang, Huijia Wu, Liuyu Xiang, Peipei Li, Zhaofeng He

llms rag efficiency multimodal benchmarks safety

arXiv AI

Jul 21

Training Continuous Chain of Thought Models: A Tale of Two Regimes

arXiv:2607. 16972v1 Announce Type: new Abstract: Continuous Chain-of-Thought methods replace verbose reasoning traces with a short sequence of dense latent representations.

By Varun Yerram, He He, Eunsol Choi

rag

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

One email a morning, machine-written

Related stories

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

Think Wider: Mitigating Latent Rank Collapse in Implicit Chain-of-Thought Reasoning

Invariant Reasoning Directions in Latent Trajectories of Language Models

A*-Thought-V2: Efficient Latent Reasoning via Geometric Dynamics of LLM

Training Continuous Chain of Thought Models: A Tale of Two Regimes

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