CARE-RL combines PA-GRM for task-adaptive rewards on open-ended tasks and DACSP for modulating RL updates using historical capability directions, reporting higher total average scores than baselines on Qwen models.
Baichuan-m3: Modeling clinical inquiry for reliable medical decision-making.arXiv preprint arXiv:2602.06570, 2026
3 Pith papers cite this work. Polarity classification is still indexing.
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MedKGTab integrates data-driven statistical priors with the SPOKE biomedical knowledge graph via dual-attention to expand cross-domain features in tabular medical data and claims to outperform existing models.
The paper describes Baichuan-M4, a coordinated medical agent system that reports leading scores across static knowledge, dynamic consultation, long-context memory, retrieval, OCR, and multimodal tasks with a 3.3% hallucination rate.
citing papers explorer
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CARE-RL: Capability-Aware Reinforcement Learning for Mitigating Cross-Domain Conflicts
CARE-RL combines PA-GRM for task-adaptive rewards on open-ended tasks and DACSP for modulating RL updates using historical capability directions, reporting higher total average scores than baselines on Qwen models.
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Cross-Domain Feature Expansion for Tabular Medical Data via Knowledge Graphs Injection
MedKGTab integrates data-driven statistical priors with the SPOKE biomedical knowledge graph via dual-attention to expand cross-domain features in tabular medical data and claims to outperform existing models.
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Baichuan-M4: A Clinical-Grade Medical Agent System for Continuous Care
The paper describes Baichuan-M4, a coordinated medical agent system that reports leading scores across static knowledge, dynamic consultation, long-context memory, retrieval, OCR, and multimodal tasks with a 3.3% hallucination rate.