Rule-Based Reasoning

Learning to Learn: A LATS-Based Framework for Self-Aware AI Pipelines

Learning to Learn: A LATS-Based Framework for Self-Aware AI Pipelines

Self-Improving Systems, Symbolic Reasoning, Multi-Dimensional Scoring, Reflection and Self-Tuning

📖 Summary

In this post, we introduce the LATSAgent, an implementation of LATS: Language Agent Tree Search Unifies Reasoning.. within the co_ai framework. Unlike prior agents that followed a single reasoning chain, this agent explores multiple reasoning paths in parallel, evaluates them using multidimensional scoring, and learns symbolic refinements over time. This is our most complete integration yet of search, simulation, scoring, and symbolic tuning bringing together all of our previous work on sharpening, pipeline reflection, and symbolic rules into a unified, intelligent reasoning loop.

Programming Intelligence: Using Symbolic Rules to Steer and Evolve AI

Programming Intelligence: Using Symbolic Rules to Steer and Evolve AI

AI Research, Symbolic Reasoning, Self-Tuning Systems, LLM Architecture, Agent Design, Prompt Optimization, AI Evaluation Methods, Co AI Framework

🧪 Summary

“What if AI systems could learn how to improve themselves not just at the level of weights or prompts, but at the level of strategy itself? In this post, we show how to build such a system, powered by symbolic rules and reflection.

The paper Symbolic Agents: Symbolic Learning Enables Self-Evolving Agents introduces a framework where symbolic rules guide, evaluate, and evolve agent behavior.