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StepBench: 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

Portuguese

A avaliação da capacidade de planejamento de um agente permanece desafiadora para tarefas de domínio aberto, onde a verificação de procedimentos através da execução é muitas vezes impraticável. As avaliações existentes geralmente avaliam apenas o plano final e não distinguem erros processuais, como erros perdidos, incorretos ou mal-ordenados. Introduzimos o STEPBENCH, um benchmark de 6.885 procedimentos verificados rastreados da wikiHow em 81 categorias de domínio aberto. De cada procedimento, derivamos 5 tarefas de diagnóstico: quatro com respostas determinísticas envolvendo passos retidos, incorretos ou mal-ordenados, e um que requer a próxima geração de etapas. Nós também avaliamos a geração de plano completo para examinar como o desempenho de diagnóstico se correlaciona com a

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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