Ο^2-bench
Emerging6papers using it
2026first seen
The 'Ο^2-Bench' is a benchmark dataset used to evaluate the performance of online warning monitors for large language model agents by assessing their ability to predict risks based on traces of agent actions.
Papers using Ο^2-bench (6)
- Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM AgentsPrefixGuard: From LLM-Agent Traces to Online Failure-Warning MonitorsA Matter of TASTE: Improving Coverage and Difficulty of Agent BenchmarksTRACE: Capability-Targeted Agentic TrainingEnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RLSkillX: Automatically Constructing Skill Knowledge Bases for Agents