CausalCoT is an inference framework that enhances causal transparency in chain-of-thought prompting by structuring problems as a directed acyclic graph of atomic sub-questions. Each node is evaluated in an isolated context that includes only its direct causal parents, thereby preventing attention leakage to unrelated content. This approach maintains competitive predictive performance while significantly improving explanation faithfulness, raising causal sensitivity of intermediate reasoning steps by an average of 12 percentage points and up to 21 percentage points over the strongest baseline, and enables predictable test-time interventions through the structured dependency graph.
By Gabriele Dominici, Teague McMillan, Fazl Barez, Marc Langheinrich, Martin Gjoreski
arXiv:2607. 15281v1 Announce Type: new Abstract: Causal and intervention-based question answering is fundamental to advancing large language models (LLMs) toward reasoning beyond surface-level correlations and understanding underlying causal mechanisms.
By Su Lan, Xuefei Yin, Yanming Zhu, Alan Wee-Chung Liew
arXiv:2508. 21010v3 Announce Type: replace-cross Abstract: Existing Causal-Why Video Question Answering (VideoQA) models often struggle with higher-order reasoning, relying on opaque, monolithic pipelines that entangle video understanding, causal inference, and answer generation.
By Paritosh Parmar, Eric Peh, Basura Fernando
The paper investigates whether latent chain-of-thought (latent CoT) models truly perform reasoning by examining twenty-one model-task configurations across different training recipes, tasks, and backbones. Using chain deletion, content replacement, probes, and trajectory analysis, the authors find that on logical tasks the latent chain often contains an answer before the chain starts and can be deleted with little effect, while on arithmetic tasks the chain’s content is essential. The study introduces a taxonomy that distinguishes what a chain represents from how a model uses it, clarifying that readable latent states alone do not prove reasoning.
By Jingjie Qiu, Sheldon Yu, Xunyi Jiang, Jiajin Liu, Zihan Huang, Zhendong Chu, Tong Yu, Julian McAuley, Junda Wu
arXiv:2606. 13603v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning is the dominant paradigm for inference-time scaling in language models, yet the causal influence of individual steps on the final answer poorly understood.
By Daniel Scalena, Sara Candussio, Luca Bortolussi, Elisabetta Fersini, Malvina Nissim, Gabriele Sarti
arXiv:2602. 20094v2 Announce Type: replace Abstract: As large language models (LLMs) witness increasing deployment in complex, high-stakes decision-making scenarios, it becomes imperative to ground their reasoning in causality rather than spurious correlations.
By Yuzhe Wang, Yaochen Zhu, Jundong Li