In-Context Learning

Epistemic Engines: Building Reflective Minds with Belief Cartridges and In-Context Learning

Epistemic Engines: Building Reflective Minds with Belief Cartridges and In-Context Learning

Self-Improving Systems, In-Context Learning, AI Architectures, Knowledge Representation, Agent-Based AI, Machine Reasoning, Adaptive Learning, LLM Evaluation Strategies, Autonomous Knowledge Systems

🔍 Summary: Building the Engine of Understanding

This is not a finished story. It’s the beginning of one and likely the most ambitious post we’ve written yet.

We’re venturing into new ground: designing epistemic engines modular, evolving AI systems that don’t just respond to prompts, but build understanding, accumulate beliefs, and refine themselves through In-Context Learning.

In this series, we’ll construct a self-contained system separate from our core framework Stephanie that runs its own pipelines, evaluates its own beliefs, and continuously improves through repeated encounters with new data. Its core memory will be made of cartridges: scored, structured markdown artifacts distilled from documents, papers, and the web. These cartridges form a kind of belief substrate that guides the system’s judgments.