# Do Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?

arXiv: https://arxiv.org/abs/2607.15439 · HTML: https://mlsanity.com/paper/2607.15439/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)
- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 12 of 12)

- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://mlsanity.com/paper/2304.11477/) (2023)
- [DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning](https://mlsanity.com/paper/2006.08381/) (2020)
- [WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment](https://mlsanity.com/paper/2402.12275/) (2024)
- [LILO: Learning Interpretable Libraries by Compressing and Documenting Code](https://mlsanity.com/paper/2310.19791/) (2023)
- [Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search](https://mlsanity.com/paper/2405.15383/) (2024)
- [ARC-AGI-3: A New Challenge for Frontier Agentic Intelligence](https://mlsanity.com/paper/2603.24621/) (2026)
- [OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration for ARC-AGI-3](https://mlsanity.com/paper/2607.01531/) (2026)
- [Workspace Optimization: How to Train Your Agent](https://mlsanity.com/paper/2605.09650/) (2026)
- [Graph-Based Exploration for ARC-AGI-3 Interactive Reasoning Tasks](https://mlsanity.com/paper/2512.24156/) (2025)
- [Refactoring Codebases through Library Design](https://mlsanity.com/paper/2506.11058/) (2025)
- [AGI Maze as a Benchmark Framework for World-Modeling Agents](https://mlsanity.com/paper/2607.00627/) (2026)

## Built on by (most cited first, 3 of 3)

- [Tycho: Active Abstraction with Programmatic World Models for ARC-AGI-3](https://mlsanity.com/paper/2607.28287/) (2026)
- [VISTA: A Visual Harness for Reasoning in an Interactive World](https://mlsanity.com/paper/2610.02200/) (2026)
- [Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks](https://mlsanity.com/paper/2610.11794/) (2026)

## Abstract (arXiv)

Our previous ARC-AGI-3 agent bundled executable world modeling, prompted simplification, and exact replay verification, leaving their individual contributions unclear. An executable world model is a persistent, agent-authored environment hypothesis embodied in runnable code. We compare four Codex-based variants: textual; flexible-interface executable; executable with simplification prompts; and a fixed-interface variant with simplification and exact replay verification against recorded observations. The main study evaluates them with gpt-5.4 and gpt-5.5 at high and xhigh reasoning effort on 25 public games; exploratory follow-ups compare textual and verification with gpt-5.6-sol. In the main study, every variant scores higher as model capability and reasoning effort increase. These gains often exceed variant differences, which are smaller than anticipated and vary across settings. Requiring an executable deliverable is not universally beneficial: textual outperforms flexible-interface executable in both gpt-5.5 conditions. The simplification variant scores higher than its executable-only counterpart in three of four settings; the weakest is the exception. The complete verification treatment ranks first throughout, sometimes narrowly, but uses substantially more resources. With gpt-5.6-sol, the verification variant completes every public level at xhigh and max with about 99% human-relative action efficiency while using fewer than half the human baseline's total actions. At max, however, the textual variant completes every level with 41% fewer actions than the human baseline. Thus, at max, the three imposed mechanisms are not required for action-efficient public-set completion; verification nevertheless scores higher and succeeds at lower effort. Because gpt-5.6-sol postdates the games and held-out performance is untested, results indicate public-set saturation only.