Stephanie

We turn any text into scorables, grow them into blossoms, and weave them into a living graph. Then we A/B test the graph’s thinking quality.

🥱 TLDR

This post details the implementation of:

• PACS: Implicit Actor–Critic Coupling via a Supervised Learning Framework for RLVR
• NER Retriever: Zero-Shot Named Entity Retrieval with Type-Aware Embeddings within our self-improving AI, Stephanie.

The core idea is to move beyond static, single-point feedback to a richer, more dynamic form of learning:

🔍 The Invisible AI Problem

How do you debug a system that generates thousands of database entries, hundreds of prompts, and dozens of knowledge artifacts for a single query?

SIS is our answer a visual dashboard that transforms Stephanie’s complex internal processes into something developers can actually understand and improve.

📰 In This Post

I

• 🔎 See how Stephanie pipelines really work – from Arxiv search to cartridges, step by step.
• 📜 View logs and pipeline steps clearly – no more digging through raw DB entries.
• 📝 Generate dynamic reports from pipeline runs – structured outputs you can actually use.
• 🤖 Use pipelines to train the system – showing how runs feed back into learning.
• 🧩 Turn raw data into functional knowledge – cartridges, scores, and reasoning traces.
• 🔄 Move from fixed pipelines toward self-learning – what it takes to make the system teach itself.
• 🖥️ SIS isn’t just a pretty GUI - it’s the layer that makes Stephanie’s knowledge visible and usable.
• 🈸️ Configuring Stephanie – We will show you how to get up and running with Stephanie.
• 💡 What we learned – the big takeaway: knowledge without direction is just documentation.

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❓ Why We Built SIS

When you’re developing a self-improving AI like Stephanie, the real challenge isn’t just running pipelines it’s making sense of the flood of logs, evaluations, and scores the system generates.

Summary

AI research tools today are often narrow: one generates summaries, another ranks models, a third suggests ideas. But real scientific discovery isn’t a single step—it’s a pipeline. It’s iterative, structured, and full of feedback loops.

In this post, I show how to build a modular AI system that mirrors this full research lifecycle. From initial idea generation to method planning, each phase is handled by a specialized agent working in concert.