🤖 AI Summary
To address inherent biases in large language model (LLM) reasoning, this paper proposes Flow-of-Options (FoO), a framework that explicitly models diverse reasoning paths to enhance robustness and task adaptability. FoO introduces a novel “compressed interpretable representation” mechanism to enforce reasoning diversity and integrates case-based long-term memory for traceable solution generation and generalizable adaptation. The method unifies option-flow modeling, case-based reasoning, agent architecture, and multi-task adaptation to support autonomous machine learning (AutoML) task solving. Experiments demonstrate performance gains of 37.4%–69.2% on data science and therapeutic chemistry benchmarks, with per-task inference cost under $1. Furthermore, FoO successfully generalizes to reinforcement learning and image generation domains, validating its cross-modal applicability and scalability.
📝 Abstract
We present a novel reasoning approach called Flow-of-Options (FoO), designed to address intrinsic biases in Large Language Models (LLMs). FoO enables LLMs to systematically explore a diverse range of possibilities in their reasoning, as demonstrated by an FoO-based agentic system for autonomously solving Machine Learning tasks (AutoML). Our framework outperforms state-of-the-art baselines, achieving improvements of 38.2% - 69.2% on standard data science tasks, and 37.4% - 47.9% on therapeutic chemistry tasks. With an overall operation cost under $1 per task, our framework is well-suited for cost-sensitive applications. Beyond classification and regression, we illustrate the broader applicability of our FoO-based agentic system to tasks such as reinforcement learning and image generation. Our framework presents significant advancements compared to current state-of-the-art agentic systems for AutoML, due to the benefits of FoO in enforcing diversity in LLM solutions through compressed, explainable representations that also support long-term memory when combined with case-based reasoning.