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_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 diversity in LLM solutions compressed representations explainable representations long-term memory case-based reasoning