# MinesweeperEBRM: A Minesweeper Solver Based on the Energy-Based Reasoning Model

> MinesweeperEBRM is an open-source project based on the Energy-Based Reasoning Model (EBRM) that efficiently solves the classic Minesweeper game, achieving a 94% win rate at the maximum thinking depth.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-03-28T00:36:06.000Z
- 最近活动: 2026-03-28T00:51:58.356Z
- 热度: 163.7
- 关键词: 能量模型, Energy-Based Model, 推理模型, 扫雷, Minesweeper, 逻辑推理, 约束满足, 推理时计算, 开源, Jupyter Notebook
- 页面链接: https://www.zingnex.cn/en/forum/thread/minesweeperebrm
- Canonical: https://www.zingnex.cn/forum/thread/minesweeperebrm
- Markdown 来源: floors_fallback

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## MinesweeperEBRM: An Energy-Based Reasoning Model for Minesweeper Solving

MinesweeperEBRM is an open-source project using the Energy-Based Reasoning Model (EBRM) to solve the classic Minesweeper game. It achieves a 94% win rate at maximum thinking depth and is implemented via Jupyter Notebook. This project demonstrates the potential of energy models in logical reasoning tasks involving constraint satisfaction and probabilistic decision-making.

## The Rise of Reasoning Models & Energy-Based Approaches

Large language models excel in many tasks but struggle with complex reasoning. Methods like Chain-of-Thought (CoT) and Inference-Time Computation enhance reasoning. Energy-Based Models (EBMs) evaluate state quality via energy functions and find optimal states through minimization, fitting multi-step reasoning scenarios requiring logical consistency checks.

## MinesweeperEBRM Project Overview

Created by developer training4usaco, MinesweeperEBRM applies EBM to Minesweeper (a game relying on logical inference). Implemented in Jupyter Notebook, it reaches a 94% win rate on standard 9x9 boards (10 mines) at maximum thinking depth—strong for learning-based solvers.

## Core Mechanism of EBRM for Minesweeper

EBRM's key steps: 
1. **State Representation**: Encode board state (revealed cells, flags, unrevealed cell probabilities). 
2. **Energy Function**: High energy for inconsistent states (e.g., conflicting flagged cells), low for consistent ones. 
3. **Inference-Time Optimization**: Iteratively search for low-energy states (simulating deep thinking). 
4. **Decision Sampling**: Choose best action from optimized energy distribution; higher depth means more iterations.

## Minesweeper as a Reasoning Benchmark

Minesweeper tests multi-layered reasoning: 
- **Deterministic**: Definitive safe/mined cells. 
- **Probabilistic**: Risk assessment for uncertain cases. 
- **Global Constraints**: Interconnected rules form complex networks. 
- **Risk Tradeoff**: Balance risk/reward in uncertain choices. It evaluates local/global reasoning abilities.

## Implementation & Performance Analysis

MinesweeperEBRM uses a 13KB Jupyter Notebook codebase. Its 94% win rate outperforms human experts (80-90%) and rule-based solvers (struggle with probabilistic reasoning). Adjustable thinking depth lets users trade computation time for accuracy, similar to LLM inference-time expansion.

## Applications & Extensions Beyond Minesweeper

EBRM applies to: 
- **Constraint Satisfaction**: Sudoku, logic puzzles. 
- **Planning**: Pathfinding, resource allocation. 
- **Decision Support**: Uncertain environment choices. 
- **Verification**: System property testing. It also serves as an inference-time computation case study for LLMs.

## Limitations & Future Directions

Limitations: Closed rule-bound game (hard to expand to open domains), high computation cost. Future steps: 
- Integrate EBRM with neural networks for better energy functions. 
- Develop efficient optimization algorithms. 
- Extend to real-world tasks. 
- Study reasoning depth-performance relationship.