What We Talk About When We Talk About LLM Planning Evidence for Two Distinct Planning Abilities

Jul 16, 2026

Uneven performance of large language models on planning tasks often gets explained by task difficulty, but this view is incomplete. The study argues that task-level variation can reflect distinct latent planning competencies rather than differences along a single ability axis. Using ACPBench-Hard, the work evaluates multiple LLM families under different test-time reasoning budgets and fits a multidimensional IRT model. Results identify two key dimensions: operational reasoning and structural enumeration, with the latter remaining relatively insensitive.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

Abstract

Introduction

Planning as a benchmark and symbolic planning background

Research question: single ability vs multiple latent competencies

Method: IRT/MIRT evaluation design and models

Findings: two interpretable planning dimensions

What We Talk About When We Talk About LLM Planning: Evidence for Two Distinct Planning Abilities Sukai Huang, Chenyuan Zhang, Fucai Ke, Zhixi Cai, Naim Rastgoo, Gholamreza Haffari, Hamid Rezatofighi, Faculty of Information Technology, Monash University Correspondence: [sukai.huang@monash.edu](sukai.huang@monash.edu) arXiv :2607 . 1 1 197v 1 [ cs .AI] 13 Jul 2026 Abstract When LLMs exhibit uneven performance across planning tasks, these gaps are often attributed to task difficulty. We argue that this explanation is incomplete, as task-level variation may reflect distinct latent planning competencies rather than differences along a single ability spectrum. We study this question on ACPBench-Hard by evaluating multiple LLM families under varying test-time reasoning budgets and applying a multidimensional item response theory model to uncover the latent competency structure underlying LLM planning. The analysis reveals two principal dimensions that shape planning performance: operational reasoning, the ability to evaluate local action applicability and immediate state transitions, and structural enumeration, the ability to reason about goal reachability and landmark structure. Operational reasoning improving under model scaling and longer reasoning traces, while structural enumeration remains comparatively insensitive. Our findings motivate competency-level evaluation of LLM planning, shifting the focus from whether models improve overall to which planning competencies improve, under what conditions, and why 1. 1 Introduction As large language models (LLMs) transition from conversational interfaces to agentic systems equipped with tool use, persistent memory, and multi-step execution capabilities, planning has become a core skill underlying agentic behavior.(Chowa et al., 2026 ; Li et al., 2026) . Symbolic planning offers a controlled testbed for this purpose: it requires finding a sequence of actions that transforms an initial state into a goal state under specified constraints, while allowing instances tobe generated from formal specifications, solutions to be verified automatically, and difficulty to be var- 1code and data are provided via the Openreview platform. ied systematically (Valmeekam et al., 2023b ; Stein et al., 2025 ; Kokel et al., 2026) . Among recent benchmarks that use symbolic planning domains to probe LLM reasoning skills, ACPBench (Kokel et al., 2025) assesses reasoning about actions, state transitions, and plan construction and validation using multiple-choice and Boolean question types. Its recent extension, ACPBench Hard (Kokel et al., 2026), moves to generative, open-ended questions and partitions planning into eight distinct subtasks. Such multi-faceted probing is common in the literature: PlanBench (Valmeekam et al., 2023b) and TRAC (He et al., 2023) likewise evaluate planning through diverse question types. While these subtasks are useful for organizing evaluation and localizing failures, they should not be assumed to directly map onto the latent abilities governing model performance. This distinction matters because recent progress in LLM reasoning has encouraged the expectation that LLM planning skill should improve with larger models, Chain-of-Thought (CoT), and stronger agent harness with tools or memory (Chen et al., 2025a ; Xu et al., 2025 ; Li et al., 2026) . Yet automated planning studies repeatedly find that LLMs remain brittle on planning benchmarks, even as models and test-time reasoning mechanisms improve (Kambhampati et al., 2024 ; Huang et al., 2025a) . These results are often interpreted as evidence that planning is fundamentally difficult for LLMs, or that some subtasks are simply harder than others. An open question is whether uneven task-wise performance reflects merely difficulty variation along a single planning ability, or does it reveal multiple latent competencies that respond differently to scale and reasoning traces? Without addressing this question, we

FAQs

Why do the authors challenge the common explanation that planning gaps are caused only by task difficulty?

Task-level variation can represent distinct latent planning competencies rather than differences along a single ability spectrum. This motivates modeling performance with latent dimensions instead of treating it as one continuum.

What dataset and evaluation setting are used to study LLM planning abilities?

The study evaluates multiple LLM families (Qwen, Gemma, Granite) on ACPBench-Hard, under three inference conditions: direct answering, Chain-of-Thought prompting, and scratchpad-based agent-harness traversal.

Which two planning dimensions explain the model’s planning performance patterns?

The analysis supports a two-dimensional MIRT structure: operational reasoning and structural enumeration. Operational reasoning improves with scaling and longer reasoning traces, while structural enumeration is comparatively insensitive.

Jul 16, 2026

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