# Exploring Decision-Making Capabilities of LLM Agents: An Experimental Study on Jump-Jump Game

arXiv: https://arxiv.org/abs/2509.00483 · HTML: https://mlsanity.com/paper/2509.00483/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 30 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency](https://mlsanity.com/paper/2407.09519/) (2024)
- [Jointly RS Image Deblurring and Super-Resolution with Adjustable-Kernel and Multi-Domain Attention](https://mlsanity.com/paper/2412.05696/) (2024)
- [Joint Lossless Compression and Steganography for Medical Images via Large Language Models](https://mlsanity.com/paper/2508.01782/) (2025)
- [ChronoSelect: Robust Learning with Noisy Labels via Dynamics Temporal Memory](https://mlsanity.com/paper/2507.18183/) (2025)

## Abstract (arXiv)

The Jump-Jump game, as a simple yet challenging casual game, provides an ideal testing environment for studying LLM decision-making capabilities. The game requires players to precisely control jumping force based on current position and target platform distance, involving multiple cognitive aspects including spatial reasoning, physical modeling, and strategic planning. It illustrates the basic gameplay mechanics of the Jump-Jump game, where the player character (red circle) must jump across platforms with appropriate force to maximize score.