CRC-Router: Risk-Constrained Routing for Medical Agentic AI Systems

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:2608.26191v1 Announce Type: new Abstract: Incident risk prediction from longitudinal electronic health records (EHRs) is challenging because relevant signals are multimodal,

arXiv:2608.27847v1 Announce Type: new Abstract: Interactive medical diagnosis dynamically acquires patient information through multiple rounds of questioning, supporting accurate,

arXiv:2605.05657v1 Announce Type: new Abstract: Multi-agent LLM systems for code generation face a fundamental routing problem: the optimal orchestration topology depends on the

arXiv:2605.11603v1 Announce Type: new Abstract: The growing deployment of large language models (LLMs) makes per-request routing essential for balancing response quality and

arXiv:2603.22331v1 Announce Type: cross Abstract: Every wildfire prediction model deployed today shares a dangerous property: none of these methods provides formal guarantees on

arXiv:2604.05348v1 Announce Type: new Abstract: Hallucinations in medical large language models (LLMs) remain a safety-critical issue, particularly when available evidence 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,