Dimensional Scoring

Compiling Thought: Building a Prompt Compiler for Self-Improving AI

Compiling Thought: Building a Prompt Compiler for Self-Improving AI

AI Infrastructure, Self-Improving Systems, Symbolic Reasoning, Prompt Engineering, LLM Architectures, Autonomous Agents, Meta-Learning, AI Compiler Design, Experimental AI Design, AI Research Engineering

How to design a pipeline that turns vague goals into smart prompts

🧪 Summary

Why spend hours engineering prompts when AI can optimize its own instructions. This blog post introduces a novel approach toward creating a self-improving AI by treating prompts as programs. Traditional AI systems often rely on static instructions rigid and limited in adaptability. Here, we present a different perspective: viewing the Large Language Model (LLM) as a prompt compiler capable of dynamically transforming raw instructions into optimized prompts through iterative cycles of decomposition, evaluation, and intelligent reassembly.

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.