STETHO: Synchronized Time-series and Evidence Tracking via Heterogeneous Orchestration in Multi-Agent Medical Reasoning

Multimodal medical reasoning requires the seamless integration of heterogeneous clinical data, yet monolithic Multimodal Large Language Models (MLLMs) fre- quently suffer from modality dominance and cognitive overload, failing to simulate the rigorous, cross-examined diagnostic workflows of human experts. We propose STETHO (Synchronized Time-series and Evidence Tracking via Heterogeneous Orchestration), a novel training-free multi-agent framework designed to explicitly decouple the reasoning processes across diverse modalities. STETHO introduces an Independent Blind Review mechanism to prevent early anchoring bias, fol- lowed by a Cross-Modal Examination Protocol where a Chief Agent acts as an active adjudicator to detect logical conflicts and trigger Selective Replay of raw data for targeted reflection. Finally, a Meta-Cognitive Reasoning Refiner distills multi-agent interactions into a standardized Four-Stage Clinical Paradigm, ensuring logical transparency and grounding. Extensive evaluations on challeng- ing benchmarks, including MedFrameQA and OmniMedVQA, demonstrate that STETHO, utilizing only lightweight 7B/8B backbones, significantly outperforms massive monolithic behemoths (e.g., GPT-4o) and reinforcement, learning-based models (e.g., Med-R1). Results based on M3CoTBench metrics further confirm that STETHO achieves superior logical correctness and consistency, providing an auditable diagnostic trail that effectively bridges the gap between automated reasoning and expert-level clinical consultation.