AI Evaluation Methods

Thoughts of Algorithms

Thoughts of Algorithms

AI Research, Symbolic AI, Agent Architectures, Prompt Engineering, Machine Learning, LLM Optimization, Self-Improving Systems, Reasoning Frameworks, AI Evaluation Methods, Autonomous Agents

How a self-evolving AI learns to reflect, score, and rewrite its own reasoning

🧪 Summary

What if an AI could think not just solve problems, but reevaluate its beliefs in the face of new information?

In this post, we introduce a system that does exactly that. At the core of our pipeline is a lightweight scoring model called MR.Q, responsible for evaluating ideas and choosing the best ones. But when it encounters a new domain, a new goal, or a shift in task format, it doesn’t freeze it adapts.

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.