AEROBAT: First Multi-Agent System Automates Behavioral Science on AI Agents
A new paper on arXiv (2608.10030) introduces AEROBAT, the first multi-agent system designed to automate behavioral scientific research on AI agents. The system, given a target behavior, autonomously generates hypotheses, designs and executes controlled experiments, makes behavioral assessments, analyzes results, and writes reports. In testing, AEROBAT generated 79 hypotheses across 12 target behaviors, designing 1,240 controlled experiments and executing 23,512 simulation rounds. Statistical evidence was found for 26 hypotheses, including novel ones. This development addresses the labor-intensive nature of behavioral research on AI agents, which is increasingly important as AI agents are deployed in complex environments. The paper is authored by researchers and is available on arXiv.
Key facts
- AEROBAT is the first multi-agent system to automate behavioral scientific research on AI agents.
- The system executes a full pipeline: hypothesis generation, experiment design and execution, behavioral assessment, result analysis, and report writing.
- AEROBAT was tested on 12 target behaviors, generating 79 hypotheses.
- The system designed 1,240 controlled experiments and executed 23,512 simulation rounds.
- Moderate-to-strong statistical evidence was found for 26 hypotheses, including some novel ones.
- The research addresses the manual and labor-intensive nature of behavioral scientific research on AI agents.
- The paper is available on arXiv with identifier 2608.10030.
- The work is relevant as AI agents are increasingly deployed in complex environments.
Entities
Institutions
- arXiv