Rethinking Failure Attribution in Multi-Agent Systems: A Multi-Perspective Bench

arXiv:2607.09996v1 Announce Type: new Abstract: Automated failure attribution uses LLMs to identify where and why agentic systems fail. As agents become more capable, their

arXiv:2604.11978v1 Announce Type: new Abstract: Large language model (LLM) agents perform strongly on short- and mid-horizon tasks, but often break down on long-horizon tasks that

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment

arXiv:2607.18754v1 Announce Type: new Abstract: LLM agent failures are difficult to debug because the step where an error surfaces is often not the one that caused it. Existing

arXiv:2603.18743v1 Announce Type: new Abstract: We introduce \emph{Memento-Skills}, a generalist, continually-learnable LLM agent system that functions as an \emph{agent-designing

arXiv:2605.01604v1 Announce Type: new Abstract: Existing evaluation frameworks for large language models -- including HELM, MT-Bench, AgentBench, and BIG-bench -- are designed for

arXiv:2604.27891v1 Announce Type: new Abstract: Agent orchestration frameworks -- LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, and others -- place an external orchestrator

arXiv:2603.29848v1 Announce Type: new Abstract: We introduce a comprehensive validation framework for LLM-based agentic systems that provides systematic diagnosis and improvement