Multi-Dimensional Scoring

Stephanie's Secret: The Dawn of Reflective AI

Stephanie's Secret: The Dawn of Reflective AI

🌅 Introduction: The Dawn of Self-Reflective AI

What if your AI could not only answer questions but also question itself about those answers? Not with programmed doubt, but with genuine self-awareness recognizing when it’s uncertain, analyzing why it made a mistake, and systematically improving its own reasoning process? This isn’t science fiction. Today, we’re unveiling the first working implementation of an AI that doesn’t just think, but learns how to think better. It’s a bit cold here

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

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

📖 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.