Newest papers

ToM2Vec: Continuous Theory-of-Mind Tokens for Multi-Agent Coordination

Nithin Sivakumaran, Ali Payani, Hyunji Lee, Mohit Bansal, Elias Stengel-Eskin · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29934 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

形成一个准确的代理机智理论(ToM)是成功的多代理合作的关键,并需要在不确定性下推理来捕捉多个可能的合作伙伴状态。这在文本代表空间中是很难的,原生于大语言模型(LLM)的代理人,更容易地描述一个单一的合作伙伴状态。 为了应对这个挑战,我们介绍了ToM2Vec,这需要学习通过连续推理代币的固定预算来捕捉多个可能的合作伙伴状态的抽象理论,培训期间,对这些代币的监督来自合作伙伴的实际思想,而ToM代币被训练来抽象和匹配。 按时,ToM2Vec的证据是由每个代理人生成的,并作为代理人的信息思想和行动的条件。 在多个环境中,涉及协作,竞争和混合

Machine translation (Meta M2M100); the English original follows.

Abstract

Forming an accurate theory-of-mind (ToM) is key to successful multi-agent collaboration and requires reasoning under uncertainty to capture multiple possible partner states. This is hard to do in the text representation space native to large language model (LLM) agents, which more easily describe a single partner state. To address this challenge, we introduce ToM2Vec, which learns to form abstract theories-of-mind in a rich latent space via a fixed budget of continuous reasoning tokens. During training, supervision for these tokens comes from the partner's actual thoughts, which the ToM tokens are trained to abstract and match. At test-time, ToM tokens are generated by each agent and act as conditioners for that agent's thoughts and actions. Across several environments covering collaborative, competitive, and mixed-motive settings (Overcooked-AI, multiparty negotiation, Decrypto, Hanabi, and Collab Escape/Capture), we evaluate ToM2Vec's ability to support multi-agent interactions and collaboration, finding consistent improvements over the base model and a variety of competitive trained and prompted baselines. We show that latent reasoning is critical, with performance dropping when reasoning is expressed in text. Moreover, we show that ToM2Vec training generalizes to partner model families not seen at train-time, including a proprietary model (GPT-5.4). In our analysis, we provide evidence that the latent tokens encode a ToM, rather than task-specific information: ToM2Vec's tokens improve the agent's ability to directly answer questions about partner beliefs, desires, and next actions.

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Topics: Computer science, ENCODE, Representation (politics), Variety (cybernetics), Space (punctuation), Artificial intelligence

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