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

arXiv: https://arxiv.org/abs/2508.12782 · HTML: https://mlsanity.com/paper/2508.12782/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 22 of 22)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://mlsanity.com/paper/2010.03768/) (2020)
- [Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?](https://mlsanity.com/paper/2504.13837/) (2025)
- [AlphaEvolve: A coding agent for scientific and algorithmic discovery](https://mlsanity.com/paper/2506.13131/) (2025)
- [TravelPlanner: A Benchmark for Real-World Planning with Language Agents](https://mlsanity.com/paper/2402.01622/) (2024)
- [PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change](https://mlsanity.com/paper/2206.10498/) (2022)
- [On the Planning Abilities of Large Language Models : A Critical Investigation](https://mlsanity.com/paper/2305.15771/) (2023)
- [The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity](https://mlsanity.com/paper/2506.06941/) (2025)
- [Survey on Evaluation of LLM-based Agents](https://mlsanity.com/paper/2503.16416/) (2025)
- [MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents](https://mlsanity.com/paper/2503.01935/) (2025)
- [NATURAL PLAN: Benchmarking LLMs on Natural Language Planning](https://mlsanity.com/paper/2406.04520/) (2024)
- [SmartPlay: A Benchmark for LLMs as Intelligent Agents](https://mlsanity.com/paper/2310.01557/) (2023)
- [Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks](https://mlsanity.com/paper/2303.16563/) (2023)
- [Robotouille: An Asynchronous Planning Benchmark for LLM Agents](https://mlsanity.com/paper/2502.05227/) (2025)
- [Plancraft: an evaluation dataset for planning with LLM agents](https://mlsanity.com/paper/2412.21033/) (2024)
- [PLANET: A Collection of Benchmarks for Evaluating LLMs' Planning Capabilities](https://mlsanity.com/paper/2504.14773/) (2025)
- [TaskGen: A Task-Based, Memory-Infused Agentic Framework using StrictJSON](https://mlsanity.com/paper/2407.15734/) (2024)
- [CrafText Benchmark: Advancing Instruction Following in Complex Multimodal Open-Ended World](https://mlsanity.com/paper/2505.11962/) (2025)
- [LogiPlan: A Structured Benchmark for Logical Planning and Relational Reasoning in LLMs](https://mlsanity.com/paper/2506.10527/) (2025)

## Built on by (most cited first, 6 of 6)

- [RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments](https://mlsanity.com/paper/2606.15862/) (2026)
- [MerchantBench: Benchmarking LLM Agents for Long-Term Coherence in E-Commerce Operations](https://mlsanity.com/paper/2607.28956/) (2026)
- [EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies](https://mlsanity.com/paper/2602.09514/) (2026)
- [Mini Amusement Parks (MAPs): A Testbed for Modelling Business Decisions](https://mlsanity.com/paper/2511.15830/) (2025)
- [RetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environments](https://mlsanity.com/paper/2603.16453/) (2026)
- [Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning](https://mlsanity.com/paper/2608.05139/) (2026)

## Abstract (arXiv)

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.