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This research introduces an active evaluation framework where an in-world agent deliberately generates specific social scenarios to test interactive LLMs, addressing the limitations of passive observation methods that miss context-dependent behaviors like conflict resolution. By actively eliciting relevant situations rather than scoring free-form trajectories, the approach significantly improves criteria coverage and alignment with human judgments in complex life-simulation environments. This methodology offers a more robust standard for assessing the reliability of social agents that depend on prior interactions and downstream actions.
Online Agent-as-a-Judge: Situation-Generating Evaluation for Interactive Agents
arXiv:2606.08200v1 Announce Type: cross Abstract: Evaluating LLM-powered interactive social agents is challenging because socially relevant behaviors depend not only on isolated outputs, but also on prior interactions, social roles, and downstream actions. Existing methods typically allow a target agent to act freely in an environment and then score the resulting trajectory. However, this passive setup can miss capabilities that only become observable under specific social circumstances; for example, conflict handling may remain untested if no disagreement arises. We propose Online Agent-as-a-Judge, a situation-generating evaluation framework for interactive social agents. Online Agent-as-a-Judge deploys an in-world evaluator agent that interacts with the target agent through the environment's native dialogue and action protocol, actively eliciting situations relevant to the evaluation criteria. The resulting trajectories provide evidence for assessing both immediate responses and subsequent behavior. In a life-simulation environment with $32$ designer-authored social criteria, Online Agent-as-a-Judge improves criteria coverage and agreement with human labels, yielding more reliable evidence-grounded evaluations of behaviors that passive methods can leave unobserved.
Abstract:Evaluating LLM-powered interactive social agents is challenging because socially relevant behaviors depend not only on isolated outputs, but also on prior interactions, social roles, and downstream actions. Existing methods typically allow a target agent to act freely in an environment and then score the resulting trajectory. However, this passive setup can miss capabilities that only become observable under specific social circumstances; for example, conflict handling may remain untested if no disagreement arises. We propose Online Agent-as-a-Judge, a situation-generating evaluation framework for interactive social agents. Online Agent-as-a-Judge deploys an in-world evaluator agent that interacts with the target agent through the environment's native dialogue and action protocol, actively eliciting situations relevant to the evaluation criteria. The resulting trajectories provide evidence for assessing both immediate responses and subsequent behavior. In a life-simulation environment with $32$ designer-authored social criteria, Online Agent-as-a-Judge improves criteria coverage and agreement with human labels, yielding more reliable evidence-grounded evaluations of behaviors that passive methods can leave unobserved.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2606.08200 [cs.AI]
(or arXiv:2606.08200v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.08200 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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