Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level (2411.03562v1)

Abstract: We introduce Agent K v1.0, an end-to-end autonomous data science agent designed to automate, optimise, and generalise across diverse data science tasks. Fully automated, Agent K v1.0 manages the entire data science life cycle by learning from experience. It leverages a highly flexible structured reasoning framework to enable it to dynamically process memory in a nested structure, effectively learning from accumulated experience stored to handle complex reasoning tasks. It optimises long- and short-term memory by selectively storing and retrieving key information, guiding future decisions based on environmental rewards. This iterative approach allows it to refine decisions without fine-tuning or backpropagation, achieving continuous improvement through experiential learning. We evaluate our agent's apabilities using Kaggle competitions as a case study. Following a fully automated protocol, Agent K v1.0 systematically addresses complex and multimodal data science tasks, employing Bayesian optimisation for hyperparameter tuning and feature engineering. Our new evaluation framework rigorously assesses Agent K v1.0's end-to-end capabilities to generate and send submissions starting from a Kaggle competition URL. Results demonstrate that Agent K v1.0 achieves a 92.5\% success rate across tasks, spanning tabular, computer vision, NLP, and multimodal domains. When benchmarking against 5,856 human Kaggle competitors by calculating Elo-MMR scores for each, Agent K v1.0 ranks in the top 38\%, demonstrating an overall skill level comparable to Expert-level users. Notably, its Elo-MMR score falls between the first and third quartiles of scores achieved by human Grandmasters. Furthermore, our results indicate that Agent K v1.0 has reached a performance level equivalent to Kaggle Grandmaster, with a record of 6 gold, 3 silver, and 7 bronze medals, as defined by Kaggle's progression system.

• The paper's main contribution is the development of Agent K v1.0, which revolutionizes data science automation with a flexible structured reasoning framework.
• It demonstrates full lifecycle management from data scraping to model optimization, employing techniques like Bayesian optimization and top-tier libraries.
• The agent achieved a 92.5% success rate with multiple Kaggle medals, showcasing performance that rivals Kaggle Grandmasters in diverse tasks.

Analysis of Agent K v1.0: LLM-Agent for Autonomous Data Science

The paper presents Agent K v1.0, a fully autonomous data science agent designed to manage the complete data science life cycle. Unlike traditional approaches that rely on static reasoning patterns, Agent K v1.0 employs a flexible structured reasoning framework, enhancing its ability to learn from experience. This framework facilitates the dynamic processing of memory, allowing the agent to effectively tackle complex reasoning tasks. As a result, the agent can iteratively refine its decisions, employing both short- and long-term memory to optimise its performance based on environmental feedback, without depending on traditional fine-tuning or backpropagation.

Key Contributions

The authors provide several notable advancements in the domain of data science automation:

1. Structured Reasoning Framework: Rather than rigidly following pre-set reasoning paths, Agent K leverages structured reasoning to dynamically store and retrieve key information. This breakthrough enables the agent to adapt its strategies based on feedback, iterating through tasks and improving its performance incrementally.
2. Full Data Science Lifecycle Management: The agent autonomously scrapes and pre-processes data, utilises Bayesian optimisation for hyperparameter tuning, and integrates feature engineering with libraries such as Torchvision and HuggingFace. Following model training, it optimises submission strategies for platforms like Kaggle, further refining its approach based on performance outcomes.
3. Impressive Performance Metrics: Agent K v1.0 demonstrated a 92.5% success rate across diverse tasks—tabular, computer vision, NLP, and multimodal—using Elo-MMR scores to rank within the top 38% of competitors. The agent achieved performance levels on par with Kaggle Grandmasters, secured 6 gold, 3 silver, and 7 bronze medals across competitions, thereby affirming its efficacy in real-world scenarios.

Implications and Future Developments

The implications of the research extend into both practical applications and theoretical advancements:

• Practical Application: By drastically reducing the need for human intervention in the data science pipeline, Agent K v1.0 exemplifies the potential for LLM-based agents to drive efficiency and scalability in data science endeavors. Businesses investing in data-driven strategies could leverage such technology to streamline operations and enhance competitive advantage.
• Theoretical Advancements: From a theoretical standpoint, the structured reasoning framework and memory optimization approach proposed extend beyond typical reinforcement learning paradigms. By leveraging intrinsic functions and decoupling adaptation from traditional training paradigms, this research paves the way for future LLM agents to tackle increasingly complex, non-linear tasks without prohibitive fine-tuning overheads.

Concluding Thoughts

Agent K v1.0 represents a significant step forward in automated data science. Its ability to orchestrate a complex series of actions and successfully submit to Kaggle competitions showcases the potential of LLM-based agents in achieving human-level performance across data science disciplines. Moving forward, the incorporation of innovative feedback mechanisms and broader toolsets could further enhance the capabilities of such systems, aligning them even more closely with evolving industry needs. The structured reasoning framework not only elevates the agent's adaptability but also sets a new benchmark for how AI can contribute to continual learning and application in volatile data-centric environments.