Papers by Haihua Yang

2 papers
DeepMed: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training & Inference (2026.findings-acl)

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Challenge: Medical reasoning models are constrained by parametric knowledge and can induce hallucinations and spurious attributions.
Approach: They propose a model that uses a multi-hop med-search QA synthesis method to apply the DR paradigm in medical contexts.
Outcome: The proposed model outperforms larger medical reasoning models on medical benchmarks.
ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following (2026.acl-long)

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Challenge: Experiments show that enhancing implicit reasoning capabilities can significantly improve complex instruction following in large language models.
Approach: They propose a method to enhance LLMs’ understanding of implicit reasoning instructions by formalizing such instructions as verifiable reasoning graphs and fine-tuning with graph reasoning.
Outcome: The proposed method outperforms existing models on five complex instruction following benchmarks and will be open-sourced in the near future.

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