SentinelBench: A Benchmark for Long-Running Monitoring Agents

본문 미리보기

arXiv:2606.05342v1 Announce Type: new Abstract: AI agents are increasingly asked to carry out work that spans minutes, hours, or longer. Yet the default model of agent behavior is continuous action: issuing tool calls, refreshing pages, searching for alternatives, or otherwise trying to force progress. This is the wrong approach for many long-running tasks, which are better served by a strategy of sustained attention. Instead, agents should monitor an environment, notice when an external event

SentinelBench: A Benchmark for Long-Running Monitoring Agents

본문 미리보기

관련 글

Thousand Token Wood: shipping a multi-agent economy on a 3B model

Stability vs. Manipulability: Evaluating Robustness Under Post-Decision Interaction in LLM Judges

An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)

LeanMarathon: Toward Reliable AI Co-Mathematicians through Long-Horizon Lean Autoformalization