Papers by Jessie Wang

2 papers
Foresight Optimization for Strategic Reasoning in Large Language Models (2026.acl-long)

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Challenge: Existing reasoning enhancement methods do not capture foresight in LLMs.
Approach: They propose to integrate opponent modeling principles into policy optimization to enhance strategic reasoning in LLMs by integrating opponent modeling into policy.
Outcome: The proposed method outperforms existing reasoning-based LLMs in out-of-domain scenarios and shows that it significantly enhances strategic reasoning across LLM of varying sizes and origins.
MIO: A Foundation Model on Multimodal Tokens (2025.emnlp-main)

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Challenge: Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences.
Approach: They propose a foundation model built on multimodal tokens capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner.
Outcome: The proposed model is able to understand speech, text, images, and videos in an end-to-end, autoregressive manner.

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