HAS-Bench: Evaluating LLM-Based Human-Agent Systems under Configurable Human Participation
Large language models increasingly operate in settings where humans are active collaborators rather than passive task providers. We introduce HAS-Framework, a graph-based framework that represents humans and LLM-powered agents as first-class participants with explicit roles, permissions, communication paths, and action authority. Building on this framework, HAS-Bench evaluates Human-Agent Systems under configurable human participation across agency levels, interaction channels, and persona policies. The benchmark measures both task outcomes and process-level collaboration behavior, including clarification quality, feedback utilization, control calibration, safety, initiative, and interact...
| Authors | Yaozu Wu, Wei-Chieh Huang, Jizhou Guo et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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