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[TMLR] Cumulative Reasoning With Large Language Models (https://arxiv.org/abs/2308.04371)
| Date | Stars |
|---|---|
| 2026-07-31 | 308 |
| 2026-08-02 | 308 |
| 2026-08-06 | 308 |
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# Cumulative Reasoning with Large Language Models <div align="center"> [](https://arxiv.org/pdf/2308.04371) [](https://arxiv.org/abs/2308.04371) [](https://www.python.org/downloads/release/python-3100/) [](https://cumulative-reasoning.github.io) </div> This is the official implementation for the paper **"Cumulative Reasoning with Large Language Models"**, published in Transactions on Machine Learning Research (TMLR). Cumulative Reasoning (CR) is a structured framework that enhances LLM problem-solving by emulating human-like iterative and cumulative thought processes. CR orchestrates LLMs in three distinct roles—**Proposer**, **Verifier(s)**, and **Reporter**—to systematically decompose tasks, validate intermediate steps, and compose them into a final solution. --- ## 🚀 Key Achievements CR demonstrates state-of-the-art performance across multiple complex reasoning benchmarks: - **Game of 24**: Achieves **98% accuracy**, a **+24%** absolute improvement over Tree-of-Thoughts (ToT). - **MATH Dataset (No Code Interpreter)**: Attains **58% accuracy** with GPT-4, outperforming Progressive-Hint Prompting (PHP) by **+4.2%**. - **MATH Dataset (Hardest Problems)**: Shows a **43% relative improvement** on Level 5 problems (from 22.4% to 32.1%). - **MATH Dataset (with Code Interpreter)**: The CR Agent reaches **72.2% accuracy**, surpassing PAL (PoT) by **+20.2%**. On Level 5 problems, this represents a **66.8% relative improvement** over PAL. --- ## 🔧 Installation To get started, clone the repository and set up the environment: ```bash # Clone the repository git clone https://github.com/iiis-ai/cumulative-reasoning.git cd cumulative-reasoning # Create and activate a conda environment conda create -n cr python=3.10 conda activate cr # Install the required packages pip install -r requirements.txt ``` For detailed instructions on specific experiments, please refer to the `README.md` files within each subdirectory. --- ## 🤖 CR Agent: Solving MATH with a Code Environment The `CR-Agent` directory contains our implementation for solving the MATH dataset using a code interpreter. This agent demonstrates how CR can be integrated with external tools for robust, semi-symbolic reasoning. ### Experimental Results Our CR Agent, using `GPT-4-1106-preview`, significantly outperforms previous methods. The agent uses a minimalist setup, accumulating context as a simple string without complex frameworks. #### Performance on MATH by Category | Method | Algebra | Counting & Prob. | Geometry | Interm. Algebra | Num. Theory | Prealgebra | Precalculus | **Overall** | | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | PAL (PoT) | 65.3 | 57.9 | 31.7 | 30.9 | 66.1 | 73.2 | 23.2 | 52.0 | | ToRA | 71.8 | 68.4 | 48.8 | 49.5 | 66.1 | 67.1 | 44.6 | 60.8 | | **CR Agent** | **86.3** | **71.1** | **53.7** | **51.5** | **88.7** | **86.6** | **51.8** | **72.2** | #### Performance on MATH by Difficulty | Method | Level 1 | Level 2 | Level 3 | Level 4 | Level 5 | | :--- | :---: | :---: | :---: | :---: | :---: | | PAL (PoT) | 88.4 | 65.6 | 60.0 | 45.3 | 31.3 | | ToRA | 74.4 | 75.6 | 69.5 | 53.9 | 46.3 | | **CR Agent** | **90.7** | **90.0** | **81.9** | **66.4** | **52.2**| --- ## ✨ CR Agent Assistant & Meta Prompting We also explore a simplified implementation of the CR Agent using the OpenAI Assistants API, guided by the principles of **Meta Prompting**. - **Demo**: Try the [**CR Agent v0.1 on the GPT Store**](https://chat.openai.com/g/g-L3a4ZCIHx-cr-agent-v0-1). - **Implementation**: See the minimalist prompt structure in `./CR-Agent-Assistant/cr-agent-assistant-v0.1.md`. > **Meta Prompting** is a technique that emphasizes the structure and syntax of prompts, p
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