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arxiv: 2508.12782 · v2 · submitted 2025-08-18 · 💻 cs.AI

HeroBench: A Benchmark for Long-Horizon Planning and Structured Reasoning in Virtual Worlds

Petr Anokhin , Roman Khalikov , Stefan Rebrikov , Viktor Volkov , Artyom Sorokin , Vincent Bissonnette This is my paper

Pith reviewed 2026-05-18 22:42 UTC · model grok-4.3

classification 💻 cs.AI
keywords long-horizon planningLLM benchmarksvirtual worldsstructured reasoningresource managementexecutable plansfailure analysis
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The pith

HeroBench shows no current LLM reliably produces executable plans for the hardest long-horizon tasks in a complex virtual world.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents HeroBench as a new evaluation setting that forces models to output complete, numerically feasible plans involving equipment selection, multi-level crafting chains, spatial navigation, and resource management across hundreds or thousands of steps. It runs these plans in simulation to measure both overall success and detailed failure modes. When 25 state-of-the-art LLMs are tested, performance spreads widely and even the strongest reasoning models fall short on the most difficult instances. A reader would care because the benchmark isolates end-to-end planning failures that simpler math or code tasks do not expose. The results indicate that current models still lack robust autonomous planning under realistic constraints.

Core claim

HeroBench requires models to generate single end-to-end plans that satisfy symbolic, numeric, and spatial constraints inside an RPG-inspired virtual world; simulation then verifies whether the plan is executable. Across 25 LLMs, reasoning-oriented models achieve markedly higher success rates than standard models, yet no model consistently succeeds on the highest-difficulty tasks that combine deep resource dependencies with long action sequences.

What carries the argument

HeroBench's simulation engine that validates plans by executing them against numeric combat, crafting trees, and resource limits, exposing both success and precise failure points.

If this is right

  • Standard step-by-step reasoning benchmarks underestimate the difficulty of maintaining feasibility over long sequences.
  • Reasoning models provide measurable gains but remain insufficient for reliable autonomous execution.
  • Fine-grained failure analysis from simulation can direct targeted improvements in planning capabilities.
  • Scalable task difficulty supports tracking future model progress on hierarchical planning.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Benchmarks of this form could be extended to test planning transfer between virtual and physical simulation environments.
  • Persistent shortfalls suggest value in hybrid approaches that combine language models with explicit search or constraint solvers.
  • Large performance gaps imply that training distributions may under-represent extended chains of dependent actions.

Load-bearing premise

The virtual-world tasks and constraints accurately represent the core difficulties of real-world long-horizon planning rather than creating benchmark-specific artifacts.

What would settle it

Finding a model that produces valid, executable plans for every task at the highest difficulty level, or showing that success rates on HeroBench do not predict planning performance in a separate real-world domain.

read the original abstract

Large language models (LLMs) perform well on step-by-step reasoning benchmarks such as mathematics and code generation, yet their ability to carry out robust long-horizon planning under realistic constraints remains insufficiently evaluated. Existing planning benchmarks often rely on abstract domains or interactive feedback, obscuring end-to-end planning failures and feasibility errors. We introduce HeroBench, a benchmark for evaluating long-horizon, hierarchical planning and structured reasoning in a complex RPG-inspired virtual world. Tasks require models to select numerically feasible equipment, reason over multi-level crafting and resource dependencies, and execute hundreds to thousands of actions as a single end-to-end plan. HeroBench integrates symbolic planning, numeric combat simulation, spatial reasoning, and resource management, while supporting scalable difficulty and adversarial distractors. HeroBench evaluates executable plans through simulation, enabling both success-based and fine-grained progress metrics, as well as detailed failure mode analysis. An evaluation of 25 state-of-the-art LLMs reveals large performance disparities rarely observed in conventional reasoning benchmarks. While reasoning models perform substantially better, no model reliably solves the hardest tasks, highlighting persistent challenges in long-horizon autonomous planning.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript introduces HeroBench, a benchmark for long-horizon planning and structured reasoning in an RPG-inspired virtual world. Tasks require LLMs to produce end-to-end executable plans involving numerically feasible equipment selection, multi-level crafting/resource dependencies, spatial reasoning, and up to thousands of actions. The benchmark supports symbolic planning, numeric combat simulation, scalable difficulty, and adversarial distractors. Evaluation of 25 state-of-the-art LLMs shows large performance disparities (reasoning models perform substantially better), yet no model reliably solves the hardest tasks, which the authors interpret as evidence of persistent challenges in long-horizon autonomous planning.

Significance. If the virtual-world tasks are shown to isolate general planning deficits rather than RPG-specific numeric or dependency artifacts, the benchmark would provide a valuable, simulation-grounded evaluation tool that goes beyond abstract reasoning benchmarks. The executable-plan evaluation protocol and fine-grained failure-mode analysis are methodological strengths that could support reproducible progress tracking in the field.

major comments (1)
  1. [§3 and §4] §3 (Task Design) and §4 (Evaluation Protocol): The central claim that observed failures demonstrate 'persistent challenges in long-horizon autonomous planning' rests on the assumption that RPG mechanics (numeric feasibility checks, multi-level crafting dependencies, combat simulation) do not introduce benchmark-specific constraints. The manuscript does not provide a dedicated analysis or ablation showing that model failures on hard tasks stem from planning deficits rather than inability to internalize these simulation rules; without such evidence the performance gaps could partly reflect domain artifacts.
minor comments (2)
  1. [Abstract and §2] The abstract and introduction would benefit from an explicit statement of how task difficulty is scaled (e.g., number of actions, dependency depth) and how adversarial distractors are constructed to avoid circularity with model capabilities.
  2. [§5] Table or figure reporting per-model success rates on the hardest tier should include confidence intervals or statistical tests to support the claim of 'large performance disparities rarely observed in conventional benchmarks'.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback on our manuscript. We address the major comment below and have made revisions to strengthen the paper's claims regarding the sources of model failures.

read point-by-point responses

  1. Referee: [§3 and §4] §3 (Task Design) and §4 (Evaluation Protocol): The central claim that observed failures demonstrate 'persistent challenges in long-horizon autonomous planning' rests on the assumption that RPG mechanics (numeric feasibility checks, multi-level crafting dependencies, combat simulation) do not introduce benchmark-specific constraints. The manuscript does not provide a dedicated analysis or ablation showing that model failures on hard tasks stem from planning deficits rather than inability to internalize these simulation rules; without such evidence the performance gaps could partly reflect domain artifacts.

    Authors: We agree that the manuscript would be strengthened by a more explicit analysis separating failures attributable to long-horizon planning deficits from those due to incomplete internalization of the RPG simulation rules. The current evaluation protocol already enforces all numeric feasibility, crafting dependencies, and combat constraints through executable simulation, and the fine-grained failure-mode breakdown in §4 shows that the majority of errors on hard tasks involve sequencing, resource chaining, and spatial coordination over hundreds of steps rather than isolated rule violations. Nevertheless, to directly address the concern, we will add a dedicated ablation subsection in the revised §4. This will include (1) a comparison of model performance when explicit rule summaries are prepended to prompts versus the standard setting, and (2) error categorization on simplified short-horizon variants of the same tasks. These additions will clarify the relative contribution of planning versus rule comprehension. We have also expanded the discussion in §4 to explicitly acknowledge this distinction. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark with independent simulation-based claims

full rationale

The paper introduces HeroBench as an empirical evaluation benchmark for LLM long-horizon planning in a virtual RPG world. It reports performance disparities across 25 models based on executable plan simulation outcomes, with no mathematical derivations, parameter fittings, predictions, or first-principles results that could reduce to inputs by construction. All load-bearing claims rest on external simulation results rather than self-referential definitions or self-citation chains. This is the standard case of a self-contained benchmark paper whose validity can be assessed against the provided task descriptions and outcomes without circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the constructed virtual world and task suite constitute a faithful proxy for general long-horizon planning; no free parameters or invented physical entities are introduced.

axioms (1)
  • domain assumption Success or failure in the RPG-inspired simulation meaningfully indicates planning capability outside the benchmark.
    This premise is required to interpret the LLM evaluation results as evidence of broader planning limitations.

pith-pipeline@v0.9.0 · 5745 in / 1252 out tokens · 54840 ms · 2026-05-18T22:42:25.168342+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies

    cs.CL 2026-02 conditional novelty 6.0

    EcoGym is a new open benchmark with three economic environments that reveals no leading LLM dominates at sustained plan-and-execute decision making across scenarios.