English(EN) Collaborative Theory of Mind: Learning to Self-Revise by Anticipating User Responses

新的CToM框架训练LLM预测用户响应以实现更好的协作

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

研究人员开发了一个名为协作心智理论（CToM）的新框架，旨在改进大型语言模型（LLM）与用户协作的方式。CToM训练LLM预测用户响应，并利用这些预测来优化自身行为，从而提高任务性能和效率。在数学推理、编码和写作任务上的实验表明，CToM的表现优于基线模型，尤其是在协作数学推理方面提高了准确性，并减少了编码和写作任务所需的与用户交互的次数。 AI

影响通过使模型能够预测和适应用户输入来增强LLM协作，有可能改善用户体验和任务完成率。

排序理由该集群包含一篇详细介绍LLM协作新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

Signal score: 11 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍LLM协作新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Fanzhi Zeng, Anubrata Das, Maytal Saar-Tsechansky, Liu Leqi · 2026-10-12 04:00

协作式心智理论：通过预测用户响应来学习自我修正

arXiv:2610.12872v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong individual task performance, yet still struggle to collaborate with users over extended, multi-turn interactions, for example by attempting solutions before understanding the user's ne…

本文如何被排名

报道来源 [1]

协作式心智理论：通过预测用户响应来学习自我修正

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