Papers by Jinghao Shi

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.
Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation (2025.acl-industry)

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Challenge: Effective content moderation is essential for video platforms to safeguard user experience and uphold community standards.
Approach: They propose a method to transform a generative MLLM into a multimodal classifier using minimal discriminative training data.
Outcome: The proposed method improves F1 score by 66.50% over traditional classifiers while requiring only 2% of the fine-tuning data.

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