Papers by Sneheel Sarangi
Can Small LLMs Learn a Robust Theory of Mind via RLVR? Investigating Generalization through the False-Belief Task (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have demonstrated emergent capabilities in complex reasoning, largely spurred by rule-based Reinforcement Learning (RL) techniques applied during post-training. |
| Approach: | They evaluate whether small-scale LLMs can acquire a robust and generalizable Theory of Mind (ToM) capability through RL with verifiable rewards. |
| Outcome: | The proposed model performs well on in-distribution tasks but fails to transfer to unseen ToM tasks with different characteristics. |
Agentic-ToM: Cognition-Inspired Agentic Processing For Enhancing Theory of Mind Reasoning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Current models struggle with reasoning about others’ perspectives, limiting their ability to attribute mental states to oneself and others. |
| Approach: | They propose to embed psychologically-grounded functions into LLMs to enable them to attribute mental states to oneself and others, known as Theory of Mind. |
| Outcome: | The proposed approach outperforms baselines on three ToM datasets without task-specific modifications. |
Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition (2025.coling-main)
Copied to clipboard
| Challenge: | Theory of Mind (ToM) is the ability to attribute and infer the mental states of others. |
| Approach: | They propose an LLM-based inference algorithm that improves model performance on complex ToM tasks by simulating user perspectives. |
| Outcome: | The proposed algorithm improves model performance on complex ToM tasks while requiring minimal prompt tuning across tasks and no additional model training. |