Papers by Guoren Wang

2 papers
DORA: A Dual-Objective Reinforcement Learning Framework for Effective and Efficient Multimodal Agentic Search (2026.acl-long)

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Challenge: Existing methods to train large language models overlook quality of intermediate search results . existing methods often invoke search calls during reasoning, making inference inefficient .
Approach: They propose a dual-objective reinforcement learning framework to improve search strategies of MLLMs . DORA outperforms state-of-the-art methods, achieving up to 8.4% higher accuracy .
Outcome: The proposed model outperforms state-of-the-art methods while reducing search calls by 9.7%.
Demystifying Uncertainty in LLMs: Active Calibration between Concepts and Human Evaluations (2026.acl-long)

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Challenge: Existing static strategies for mitigating hallucinations do not explicitly model the information gain from interacting with the external environment.
Approach: They propose a calibration-driven interactive learning strategy that selects clarification queries by optimizing calibration error.
Outcome: The proposed method provides theoretical guarantees and empirical gains for reliability.

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