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StebBench: Diagnosing Real-World Procedural Understanding in AI Agents

Hossein A. Rahmani, Bin Wu, Chaeeun Lee, Fanghua Ye, Sahan Bulathwela, Sujay Kumar Jauhar, Marzieh Fadaee, Nick Craswell, Emine Yılmaz · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26679 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

エージェントの計画能力を評価することは、オープンドメインタスクのための挑戦的であり、実行を通じて手順を検証することはしばしば非実践的である。既存の評価は、通常、最終計画のみを評価し、欠落した、間違った、または誤った行動などの手順のエラーを区別しません。我々は、オープンドメインカテゴリ81カテゴリを通じてウィキホーから検証された6885の手順のベンチマークであるSTEPBENCHを導入します。それぞれの手順から、我々は5つの診断タスクを生み出します:4つは、抑制された、間違った、または混乱したステップを含む決定的な答えと1つは、次のステップの生成を必要とする

Machine translation (Meta M2M100); the English original follows.

Abstract

Evaluating an agent's planning ability remains challenging for open-domain tasks, where verifying procedures through execution is often impractical. Existing evaluations typically assess only the final plan and do not distinguish procedural errors such as missing, incorrect, or misordered actions. We introduce STEPBENCH, a benchmark of 6,885 verified procedures crawled from wikiHow across 81 open-domain categories. From each procedure, we derive 5 diagnostic tasks: four with deterministic answers involving withheld, incorrect, or shuffled steps, and one requiring next-step generation. We additionally evaluate full-plan generation to examine how diagnostic performance correlates with end-to-end plan validity. Using the same procedures across tasks lets us directly compare different planning capabilities. For generation, STEPEVAL identifies critical failures that prevent goal completion. Across 14 configurations of 12 models, we find substantial differences across planning capabilities, with step ordering proving the most difficult: the strongest model correctly orders only 63.0% of procedures, dropping to 33.1% for those with nine or more steps. Incorrect-step detection also predicts judged plan validity across models, suggesting a lower-cost alternative for evaluating generation quality. On STEPBENCH-Val, comprising 214 procedures published in 2026, comprehension scores and rankings remain stable, suggesting limited influence from memorisation.

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Topics: Computer science, Plan (archaeology), Artificial intelligence, Machine learning, Benchmark (surveying), Comprehension

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