[Submitted on 20 Oct 2026]
Title:Fig: Automated Multiagent Behavior Elicitor
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Abstract:Many social evaluations for multiagent systems study behaviors as they emerge from environments instead of systematically intervening in the environment to elicit such behaviors. We introduce Fig (Finding Interactions in Games), an agentic system that performs targeted elicitation interventions on game environments. It takes a natural language behavior description, decomposes it into sub-behaviors, generates new social games or selects existing ones, and executes single or combined interventions to the games' engine, opponents, instructions, and context. To evaluate model behavior, it combines built-in mechanical checks and synthetic rubric judgments. We validate the interventions, generated games, and evaluation mechanisms via human review. Models attempt the target behavior in 45.2% of episodes on average for valid scenarios generated by Fig for six test behaviors -- cooperation, exploitation, deception, covert coordination, betrayal, and sabotage. For game-behavior pairs with available controls, Fig's interventions increase attempt rates by 10.9% on average compared to matched control games. Fig shows that multiagent environments can serve as programmable test beds for intentional, controllable, and reproducible behavior evaluation. Code is available at this https URL.
Subjects:
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as: arXiv:2610.28891 [cs.AI]
(or arXiv:2610.28891v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.28891
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Anrui Gu [view email] [v1] Tue, 20 Oct 2026 20:16:08 UTC (997 KB)
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