Unfortunately I couldn't make it to #EMNLP2026 but do check out the following work at the conference, covering: - pragmatics as a self-taught reasoning task - faithful CoT via multi-agent speaker-listener training - grounding CUAs to history/vision - early exiting for report generation - tracing errors in deep research - trade-offs between debugging and regenerating

Theory-of-mind lets agents coordinate by modeling others’ thoughts/beliefs, but requires the agent to hold many hypotheses at once. ToM2Vec teaches agents to produce continuous ToM thought vectors which capture multiple hypotheses, improving across 5 multi-agent tasks and transferring to unseen partners, including frontier-model partners. LLM agents often focus on their own plans without accounting for their partners’ next steps, leading to redundant or conflicting actions. ToM2Vec addresses this by improving agents’ theories-of-mind, training agents to predict a continuous, abstract representation of a partner’s reasoning. These continuous ToM tokens then guide the agent’s own reasoning and actions. At test-time, the main agent generates a series of latent thought vectors representing its estimate of another agent’s reasoning/thoughts. ToM2Vec improves coordination without any task rewards, across cooperative, competitive, and mixed-motive settings, including Overcooked, negotiation, Decrypto, Hanabi, and Collab Escape/Capture. Crucially, agents trained with ToM2Vec generalize to unseen partner model families, including GPT-5.4 and Gemma-4. 🧵👇