Experiential Chain of Thought (E-CoT): A Framework for Self-Improving Reasoning via Segmented Experience Memory

Author(s): Marc Lopez

Originally published on Towards AI.

How AI can learn from its own successes and failures to reason more effectively.

Large Language Models (LLMs) have shown an incredible ability to reason, largely thanks to techniques like Chain of Thought (CoT) prompting, where we ask the model to “think step-by-step.” This method breaks down complex problems, making them easier to solve and offering a transparent look into the model’s “thought process.”

The article introduces the Experiential Chain of Thought (E-CoT) framework, which enhances the Chain of Thought method by integrating a Segmented Experience Memory (SEM) that allows AI systems to learn from past successes and failures. It outlines the limitations of the current stateless reasoning approaches and describes how E-CoT addresses these by providing a dual-cache architecture, facilitating a continuous learning loop that combines active learning with safety measures, ultimately improving AI reasoning capabilities and robustness through a memory system.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI