@inproceedings{sun-etal-2026-agent,
title = "Agent Planning Benchmark: A Diagnostic Framework for Planning Capabilities in {LLM} Agents",
author = "Sun, Haoyu and
Wang, Wenxuan and
Song, Mingyang and
He, Jujie and
Zhang, Weinan and
Liu, Yang and
Yang, Yang and
Cheng, Yu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1041/",
pages = "22008--22070",
ISBN = "979-8-89176-462-0",
abstract = "Planning is central to LLM agents: before acting, an agent must decompose goals, select tools, reason over constraints, and decide when a task is infeasible. Yet existing agent evaluations often report only end-to-end success, making it difficult to determine whether failures stem from planning or execution. We introduce \textbf{Agent Planning Benchmark (APB)}, a planning-specific diagnostic benchmark with 4,209 multimodal cases across 22 domains and five settings, covering holistic planning, feedback-conditioned step-wise planning, and robustness under extraneous tools, broken tools, and unsolvable tasks. Across 12 MLLMs, APB reveals systematic weaknesses in long-horizon planning, tool-noise robustness, calibrated refusal, and inference-time refinement. We further validate APB on 200 ToolSandbox tasks and 200 $\tau^2$-bench tasks, where APB-guided refinement consistently improves plan correctness, plan grade, and downstream execution metrics across three representative models. APB thus serves as an upstream diagnostic complement to execution benchmarks. The APB benchmark and code are available in \href{https://github.com/Mikivishy/AgentPlanningBenchmark}{this URL}."
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<abstract>Planning is central to LLM agents: before acting, an agent must decompose goals, select tools, reason over constraints, and decide when a task is infeasible. Yet existing agent evaluations often report only end-to-end success, making it difficult to determine whether failures stem from planning or execution. We introduce Agent Planning Benchmark (APB), a planning-specific diagnostic benchmark with 4,209 multimodal cases across 22 domains and five settings, covering holistic planning, feedback-conditioned step-wise planning, and robustness under extraneous tools, broken tools, and unsolvable tasks. Across 12 MLLMs, APB reveals systematic weaknesses in long-horizon planning, tool-noise robustness, calibrated refusal, and inference-time refinement. We further validate APB on 200 ToolSandbox tasks and 200 τ²-bench tasks, where APB-guided refinement consistently improves plan correctness, plan grade, and downstream execution metrics across three representative models. APB thus serves as an upstream diagnostic complement to execution benchmarks. The APB benchmark and code are available in \hrefhttps://github.com/Mikivishy/AgentPlanningBenchmarkthis URL.</abstract>
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%0 Conference Proceedings
%T Agent Planning Benchmark: A Diagnostic Framework for Planning Capabilities in LLM Agents
%A Sun, Haoyu
%A Wang, Wenxuan
%A Song, Mingyang
%A He, Jujie
%A Zhang, Weinan
%A Liu, Yang
%A Yang, Yang
%A Cheng, Yu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F sun-etal-2026-agent
%X Planning is central to LLM agents: before acting, an agent must decompose goals, select tools, reason over constraints, and decide when a task is infeasible. Yet existing agent evaluations often report only end-to-end success, making it difficult to determine whether failures stem from planning or execution. We introduce Agent Planning Benchmark (APB), a planning-specific diagnostic benchmark with 4,209 multimodal cases across 22 domains and five settings, covering holistic planning, feedback-conditioned step-wise planning, and robustness under extraneous tools, broken tools, and unsolvable tasks. Across 12 MLLMs, APB reveals systematic weaknesses in long-horizon planning, tool-noise robustness, calibrated refusal, and inference-time refinement. We further validate APB on 200 ToolSandbox tasks and 200 τ²-bench tasks, where APB-guided refinement consistently improves plan correctness, plan grade, and downstream execution metrics across three representative models. APB thus serves as an upstream diagnostic complement to execution benchmarks. The APB benchmark and code are available in \hrefhttps://github.com/Mikivishy/AgentPlanningBenchmarkthis URL.
%U https://aclanthology.org/2026.findings-emnlp.1041/
%P 22008-22070
Markdown (Informal)
[Agent Planning Benchmark: A Diagnostic Framework for Planning Capabilities in LLM Agents](https://aclanthology.org/2026.findings-emnlp.1041/) (Sun et al., Findings 2026)
ACL
- Haoyu Sun, Wenxuan Wang, Mingyang Song, Jujie He, Weinan Zhang, Yang Liu, Yang Yang, and Yu Cheng. 2026. Agent Planning Benchmark: A Diagnostic Framework for Planning Capabilities in LLM Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 22008–22070, Budapest, Hungary. Association for Computational Linguistics.