Papers by Yunsheng Zeng

2 papers
Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning (2026.findings-acl)

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Challenge: Clinical Decision Support Systems (CDSSs) provide reasoning and inquiry guidance for physicians, yet they face high maintenance costs and low generalization capability.
Approach: They propose a clinical diagnostic model with clinical reasoning and inquiry skills, the Dr. Assistant, and a pipeline to capture abstract reasoning logic.
Outcome: The proposed model outperforms open-source models and achieves competitive performance to closed-source model.
Frozen LLMs are Native Decoders for High-Norm Semantic Vectors (2026.acl-long)

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Challenge: Existing compression methods selectively prune tokens based on information-theoretic metrics, resulting in interpretability but risking the loss of fine-grained information.
Approach: They propose a landmark-based compression framework for long contexts that captures global dependencies over landmark tokens.
Outcome: The proposed framework outperforms soft compression baselines on four QA benchmarks.

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