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This paper proposes a knowledge-enhanced visual diagnostic system for traditional Chinese medicine that uses a Neo4j knowledge graph, a four-stage symptom matching pipeline, and an information gain-driven proactive questioning strategy to improve transparency and interpretability. Results demonstrate significant improvements in diagnostic trust and reduced cognitive load.
StepPO introduces a step-centric paradigm for agentic reinforcement learning that aligns policy optimization with agent decision granularity, outperforming token-centric methods in multi-turn interaction tasks.
This paper introduces BALAR, a training-free Bayesian agentic loop algorithm that enables large language models to actively reason and ask clarifying questions in multi-turn interactions. It demonstrates significant performance improvements over baselines on detective, puzzle, and clinical diagnosis benchmarks.