arXiv Computation and Language October 19, 2026 By Tianjun Zhong, Linyang He, Ziyang Li, Nima Mesgarani

LLMs Think with DAGs: Probing the Latent Graph Structure of Reasoning

Read the original on arXiv Computation and Language →

The paper introduces Reasoning DAG Probing, a method that treats premises, intermediate conclusions, and final answers as nodes in a directed acyclic graph (DAG). By training lightweight probes on LLM hidden states, the authors show that DAG structure is encoded in the models, peaking in intermediate layers and becoming stronger with larger models. During autoregressive generation, the latent DAG structure emerges progressively, anticipating answer correctness and enabling correction of some incorrect outputs by steering activations toward the ground‑truth DAG.

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 Computation and Language.

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 Machine Learning

Jun 18

Formalizing and Mitigating Structural Distortion in LLM Attention for Graph Reasoning

arXiv:2606. 15633v2 Announce Type: replace Abstract: Large Language Models (LLMs) have shown promise for reasoning over Text-Attributed Graphs (TAGs).

By Donald Loveland, Puja Trivedi, Ari Weinstein, Edward W Huang, Danai Koutra

llms rag benchmarks

arXiv AI

Jun 6

ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Traces

arXiv:2606. 05402v1 Announce Type: cross Abstract: Large reasoning models (LRMs) produce reasoning traces with non-linear structures, such as backtracking and self-correction, that complicate the evaluation and monitoring of the reasoning process.

By Jinu Lee, Shivam Agarwal, Amruta Parulekar, Siddarth Madala, Dilek Hakkani-Tur, Julia Hockenmaier

llms

arXiv Machine Learning

Jun 16

Formalizing and Mitigating Structural Distortion in LLM Attention for Zero-Shot Graph Reasoning

arXiv:2606. 15633v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown promise for reasoning over Text-Attributed Graphs (TAGs).

By Donald Loveland, Puja Trivedi, Ari Weinstein, Edward W Huang, Danai Koutra

llms rag benchmarks

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

Jun 8

A Comprehensive Anatomy of Human and DeepSeek-R1 LLM Mathematical Reasoning

arXiv:2606. 07410v1 Announce Type: cross Abstract: The emergence of "Aha moments" in large language models, particularly DeepSeek-R1-0120, has raised the question of whether these systems genuinely reason or merely imitate the appearance of reasoning.

By Yuxiang Chen, Jun Wang

llms

One email a morning, machine-written

Related stories

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

Formalizing and Mitigating Structural Distortion in LLM Attention for Graph Reasoning

ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Traces

Formalizing and Mitigating Structural Distortion in LLM Attention for Zero-Shot Graph Reasoning

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

A Comprehensive Anatomy of Human and DeepSeek-R1 LLM Mathematical Reasoning