Language-Informed Synthesis of Rational Agent Models for Grounded Theory-of-Mind Reasoning On-the-fly (2025.findings-emnlp)
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Lance Ying, Ryan Truong, Katherine M. Collins, Cedegao E. Zhang, Megan Wei, Tyler BrookeWilson, Tan Zhi-Xuan, Lionel Wong, Joshua B. Tenenbaum
| Challenge: | Language is a powerful source of information in social settings, especially in novel situations where language can provide both abstract information about the environment dynamics and concrete specifics about an agent that cannot be easily visually observed. |
| Approach: | They propose a language-informed rational agent synthesis framework that integrates linguistic and visual inputs to draw context-specific social inferences. |
| Outcome: | The proposed framework outperforms ablations and state-of-the-art models on a range of social reasoning tasks derived from cognitive science experiments. |
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