Self-Healing Agentic Orchestrators for Reliable Tool-Augmented Large Language Model Systems 事件

Self-Healing Agentic Orchestrators for Reliable Tool-Augmented Large Language Model Systems arXiv:2606.01416v1 Announce Type: new Abstract: Tool-augmented large language model (LLM) agents rely on orchestration layers that coordinate planning, retrieval, tool invocation, validation, memory, and recovery. In these systems, failures arise not only from model errors, but also from orchestration-level issues such as tool timeouts, malformed arguments, stale context, contradictory evidence, retry lo