SEMA-RAG: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning
Researchers propose SEMA-RAG, a multi-agent framework that restructures how retrieval-augmented generation handles medical reasoning by decoupling interpretation, exploration, and evidence synthesis into separate task streams rather than forcing them through a single pipeline. The work addresses a fundamental architectural mismatch: static, single-round RAG cannot replicate the iterative, multi-stage diagnostic process clinicians follow, leading to weak semantic grounding and incomplete evidence chains. This signals growing recognition that naive RAG scaling fails in high-stakes domains where reasoning transparency and evidence reliability matter more than raw retrieval speed, potentially reshaping how enterprises deploy LLMs in regulated verticals.
Modelwire context
ExplainerThe paper's core insight isn't just that medical RAG needs iteration, but that the bottleneck is architectural, not computational. By separating interpretation, exploration, and synthesis into parallel task streams, SEMA-RAG makes the reasoning process itself auditable at each stage, not just the final answer.
This directly addresses the provenance problem raised in the May 16 arXiv paper on agentic AI accountability. When a multi-agent system like SEMA-RAG decomposes medical reasoning into discrete streams, each stream becomes independently traceable. That transparency is what the provenance paper identified as missing from current agentic systems. The same tension appears in the clinical bias paper from May 17: if stigmatizing language skews LLM outputs, a decomposed architecture lets you isolate which stage introduced the distortion, rather than treating the entire pipeline as a black box.
If SEMA-RAG's evaluation on the UCSF brain tumor MRI dataset (released May 16) shows that clinicians can identify which agent stream introduced an error, that validates the auditability claim. If the paper only reports aggregate accuracy without per-stream error attribution, the provenance advantage collapses.
Coverage we drew on
- Responsible Agentic AI Requires Explicit Provenance · arXiv cs.CL
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MentionsSEMA-RAG · Retrieval-Augmented Generation · Multi-Agent Systems
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