AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation

Hugging Face Daily Papers Papers
agent-as-judge benchmark environment-aware llm-evaluation reinforcement-learning

Summary

AJ-Bench introduces a benchmark to evaluate Agent-as-a-Judge systems that interact with environments to verify agent behaviors across 155 tasks in search, data systems, and GUI domains.

As reinforcement learning continues to scale the training of large language model-based agents, reliably verifying agent behaviors in complex environments has become increasingly challenging. Existing approaches rely on rule-based verifiers or LLM-as-a-Judge models, which struggle to generalize beyond narrow domains. Agent-as-a-Judge addresses this limitation by actively interacting with environments and tools to acquire verifiable evidence, yet its capabilities remain underexplored. We introduce a benchmark AJ-Bench to systematically evaluate Agent-as-a-Judge across three domains-search, data systems, and graphical user interfaces-comprising 155 tasks and 516 annotated trajectories. The benchmark comprehensively assesses judge agents' abilities in information acquisition, state verification, and process verification. Experiments demonstrate consistent performance gains over LLM-as-a-Judge baselines, while also revealing substantial open challenges in agent-based verification. Our data and code are available at https://aj-bench.github.io/.
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Abstract

Agent-as-a-Judge benchmark evaluates automated verification capabilities across multiple domains with comprehensive task assessment.

Asreinforcement learningcontinues to scale the training of large language model-based agents, reliably verifying agent behaviors in complex environments has become increasingly challenging. Existing approaches rely on rule-based verifiers orLLM-as-a-Judgemodels, which struggle to generalize beyond narrow domains.Agent-as-a-Judgeaddresses this limitation by actively interacting with environments and tools to acquire verifiable evidence, yet its capabilities remain underexplored. We introduce a benchmarkAJ-Benchto systematically evaluateAgent-as-a-Judgeacross three domains-search, data systems, and graphical user interfaces-comprising 155 tasks and 516 annotated trajectories. The benchmark comprehensively assesses judge agents’ abilities ininformation acquisition,state verification, andprocess verification. Experiments demonstrate consistent performance gains overLLM-as-a-Judgebaselines, while also revealing substantial open challenges inagent-based verification. Our data and code are available at https://aj-bench.github.io/.

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