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.
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