Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking
  Through Options
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.
67b546dc2b2ec6908f00c771
Lakshmi Nair
claimed_verified
2025-02-19T14:37:39.033Z
643837ef581e6bf0fa9c72f8
/avatars/5b95d2509d1c7640d77a3405ebd53eaf.svg
Lakshmi Nair
user
lnair
67b546dc2b2ec6908f00c772
Ian Trase
67b546dc2b2ec6908f00c773
Mark Kim
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking
  Through Options
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.
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.
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