Summary of Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level, by Antoine Grosnit et al.

Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level

by Antoine Grosnit, Alexandre Maraval, James Doran, Giuseppe Paolo, Albert Thomas, Refinath Shahul Hameed Nabeezath Beevi, Jonas Gonzalez, Khyati Khandelwal, Ignacio Iacobacci, Abdelhakim Benechehab, Hamza Cherkaoui, Youssef Attia El-Hili, Kun Shao, Jianye Hao, Jun Yao, Balazs Kegl, Haitham Bou-Ammar, Jun Wang

First submitted to arxiv on: 5 Nov 2024

GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This research introduces Agent K v1.0, an autonomous data science agent that automates, optimizes, and generalizes across diverse tasks using a structured reasoning framework. The agent learns from experience by dynamically processing memory in a nested structure, allowing it to refine decisions without fine-tuning or backpropagation. Evaluations on Kaggle competitions demonstrate the agent’s capabilities, employing Bayesian optimization for hyperparameter tuning and feature engineering. Results show Agent K v1.0 achieving a 92.5% success rate across tabular, computer vision, NLP, and multimodal domains, ranking in the top 38% against human competitors with an Elo-MMR score equivalent to Expert-level users.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper introduces a new AI agent that can do many data science tasks on its own. The agent is called Agent K v1.0 and it uses a special way of thinking called structured reasoning. This helps the agent learn from experience and make good decisions without needing to be taught or fixed. The researchers tested the agent by having it compete in Kaggle competitions, which are like big math problems. The agent did very well, solving most of the problems correctly and even beating some human experts.