ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning
Summary
ClinSeekAgent is an automated agentic framework that enables large language models to actively acquire and synthesize multimodal clinical evidence from raw data sources, improving decision-making accuracy in both text-only and multimodal tasks. It introduces the ClinSeek-Bench benchmark and a distilled model ClinSeek-35B-A3B that achieves strong performance on agentic clinical reasoning.
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Paper page - ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning
Source: https://huggingface.co/papers/2605.20176
Abstract
ClinSeekAgent is an automated agentic framework that enables large language models to actively acquire and synthesize multimodal clinical evidence from raw data sources, improving decision-making accuracy in both text-only and multimodal tasks.
Large language models(LLMs) andagentic systemshave shown promise forclinical decision support, but existing works largely assume that evidence has already been curated and handed to the model. Real-world clinical workflows instead require agents to actively seek, iteratively plan, and synthesize multimodal evidence from heterogeneous sources. In this paper, we introduce ClinSeekAgent, anautomated agentic frameworkfor dynamicmultimodal evidence seekingthat shifts the paradigm from passive evidence consumption to active evidence acquisition. Given only a clinical query and access to raw data sources, ClinSeekAgent gathers evidence by queryingmedical knowledge bases, navigating raw EHRs, and invokingmedical imaging tools; refines its hypotheses as new information emerges; and integrates the collected evidence intogrounded clinical decisions. ClinSeekAgent serves both as aninference-time agentfor frontier LLMs and as atraining-time pipelinefor distilling high-qualityagent trajectoriesintocompact open-source models. To validate its inference-time effectiveness, we constructClinSeek-Bench, which pairs Curated Input reasoning from fixed pre-selected evidence with Automated Evidence-Seeking over raw clinical data. On text-only EHR tasks, ClinSeekAgent improves Claude Opus 4.6 from 60.0 to 63.2 overall F1 and MiniMax M2.5 from 43.1 to 47.3, with positive risk-prediction gains in 7 out of 9 evaluated host models. On multimodal tasks, ClinSeekAgent improves Claude Opus 4.6 from 47.5 to 62.6 (+15.1); all evaluated models improve across the three CXR-related task groups. We further validate ClinSeekAgent as a training pipeline by distilling agentic evidence-seeking trajectories into ClinSeek-35B-A3B, which achieves 34.0 average F1 on existingAgentEHR-Bench, improving over its Qwen3.5-35B-A3B baseline by +11.9 points and approaching Claude Opus 4.6.
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#### UCSC-VLAA/ClinSeek-35B-A3B Text Generation• 35B• Updated1 day ago • 44
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