SEMA-RAG: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning

SEMA-RAG introduces a multi-agent framework that decouples medical reasoning tasks into three specialized agents to improve retrieval-augmented generation for clinical question answering. The approach achieves 6.46 percentage point accuracy improvements over existing baselines by addressing hallucinations and knowledge obsolescence through iterative, evidence-driven retrieval rather than single-round static lookups.

SEMA-RAG addresses a fundamental limitation in current medical AI systems: the tendency to compress complex clinical reasoning into linear, single-stage retrieval processes. Traditional RAG systems struggle with semantic interpretation and lack mechanisms for iterative verification, creating brittleness in high-stakes medical contexts where accuracy directly impacts patient care decisions. This research demonstrates that task specialization—separating interpretation, exploration, and adjudication into distinct agents—substantially improves performance across multiple benchmarks and model architectures.

The advancement reflects broader recognition that medical reasoning differs structurally from general-purpose question answering. Clinical decisions require layered evidence evaluation, contextual interpretation of patient data, and confidence assessment across competing diagnostic or treatment pathways. Previous approaches attempted to handle these requirements through prompt engineering and single-pass retrieval, an approach that inherently conflates different cognitive tasks and produces suboptimal outcomes.

For the AI and healthcare sectors, this work signals that specialized multi-agent architectures outperform monolithic approaches for domain-specific problems. The 6.46-point accuracy improvement, measured consistently across five LLM backbones, suggests the framework's robustness independent of underlying model choice—a critical property for clinical deployment where model stability matters. This has implications for healthcare organizations evaluating AI systems for clinical decision support, automated diagnosis assistance, and medical research.

Looking forward, the key development is whether SEMA-RAG's architecture scales to real-world clinical workflows with live patient data, incomplete information, and time constraints. Validation on proprietary medical datasets and comparison with human clinician performance remain necessary before broader adoption.

• →SEMA-RAG's multi-agent design separates interpretation, exploration, and adjudication tasks, achieving 6.46% accuracy gains over baseline systems.
• →The framework addresses hallucinations and knowledge staleness by implementing iterative, sufficiency-driven retrieval instead of single-round lookups.
• →Performance improvements hold consistently across five different large language model backbones, indicating architecture robustness.
• →Task decoupling represents a broader shift toward specialized agents for domain-specific reasoning rather than monolithic general-purpose models.
• →The approach has potential clinical applications in diagnostic support and medical knowledge synthesis, pending real-world validation.