title: Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking
    Through Options
summary: 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.
paper_authors._id: 67b546dc2b2ec6908f00c771
paper_authors.name: Lakshmi Nair
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-19T14:37:39.033Z
paper_authors._id: 643837ef581e6bf0fa9c72f8
paper_authors.avatarUrl: /avatars/5b95d2509d1c7640d77a3405ebd53eaf.svg
paper_authors.fullname: Lakshmi Nair
paper_authors.type: user
paper_authors.user: lnair
paper_authors._id: 67b546dc2b2ec6908f00c772
paper_authors.name: Ian Trase
paper_authors._id: 67b546dc2b2ec6908f00c773
paper_authors.name: Mark Kim
paper_title: Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking
    Through Options
paper_summary: 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.
paper_ai_summary: A novel reasoning method called Flow-of-Options (FoO) enhances Large Language Models (LLMs) by exploring diverse possibilities and optimizing performance in various tasks, including AutoML, reinforcement learning, and image generation, with improved efficiency and explainability.
paper_ai_keywords: Flow-of-Options FoO Large Language Models LLMs agentic system autonomously solving Machine Learning tasks AutoML standard data science tasks therapeutic chemistry tasks reinforcement learning image generation case-based reasoning