# Theory-of-Mind Reasoning

Theory-of-mind benchmarks usually ask story questions about what others believe, and a correct answer does not show the model can use that judgment in a task. This evaluation extends household collaboration and calendar scheduling with asymmetric information and separates world understanding, information access, beliefs, decisions and execution into independent interfaces. On matched scheduling cases Claude recovers all agent beliefs yet produces subjectively optimal plans in only 67.9% of cases, while supplying beliefs raises Qwen-27B household task success from 55.0% to 75.0%.

Theory-of-mind benchmarks usually ask story questions about what others believe, and a correct answer does not show the model can use that judgment in a task. This evaluation extends household collaboration and calendar scheduling with asymmetric information and separates world understanding, information access, beliefs, decisions and execution into independent interfaces. On matched scheduling cases Claude recovers all agent beliefs yet produces subjectively optimal plans in only 67.9% of cases, while supplying beliefs raises Qwen-27B household task success from 55.0% to 75.0%.

The work introduces EchoChat, which reformulates empathetic spoken dialogue as a structured cognitive reasoning process spanning perception, mental-state reasoning, and response generation, supported by the EchoDialogue-400K dataset of 400K single-turn samples plus 4K multi-turn sessions, Acoustic-Anchored Attention, and a stage-aware RL objective with Step-Decomposed Credit Assignment, together with the 1K-instance expert-annotated EchoEval benchmark; experiments report gains over 12 compared models with perception 8.23, reasoning 8.43, and response 9.26, and an average Spearman correlation of 0.83 between LLM-based scores and human judgments.

The work introduces EchoChat, which reformulates empathetic spoken dialogue as a structured cognitive reasoning process spanning perception, mental-state reasoning, and response generation, supported by the EchoDialogue-400K dataset of 400K single-turn samples plus 4K multi-turn sessions, Acoustic-Anchored Attention, and a stage-aware RL objective with Step-Decomposed Credit Assignment, together with the 1K-instance expert-annotated EchoEval benchmark; experiments report gains over 12 compared models with perception 8.23, reasoning 8.43, and response 9.26, and an average Spearman correlation of 0.83 between LLM-based scores and human judgments.