Mind the Tool Failures: Achieving Synergistic Tool Gains for Medical Agents
arXiv:2605.13046v1 Announce Type: new Abstract: Mental health disorders affect millions worldwide, and healthcare systems are increasingly overwhelmed by the volume of clinical
arXiv:2605.06890v1 Announce Type: new Abstract: AI agents are promising for high-stakes enterprise workflows, but dependable deployment remains limited because tool-use failures
arXiv:2609.02191v1 Announce Type: new Abstract: Human interventions at fault points can alter the diagnostic accuracy of multi-agent medical systems. We defined fault points as
arXiv:2610.21449v1 Announce Type: new Abstract: Large language models (LLMs) can reason over both structured measurements and free-text records, which makes them attractive for
arXiv:2605.13848v1 Announce Type: new Abstract: Agentic LLM frameworks that rely on prompted orchestration, where the model itself determines workflow transitions, often suffer
arXiv:2607.01084v1 Announce Type: new Abstract: While Large Language Model (LLM) agents demonstrate proficiency in static benchmarks, their deployment in real-world scenarios is
arXiv:2604.27309v1 Announce Type: new Abstract: Clinical AI systems require not just point-in-time evaluation but continuous governance: the ongoing practice of monitoring,
arXiv:2604.06375v1 Announce Type: new Abstract: AI-driven symptom analysis systems face persistent challenges in reliability, interpretability, and hallucination. End-to-end
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