Technical Guides

📖 Summary

This post introduces a multidimensional reward modeling pipeline built on top of the stephanieanie framework. It covers:

• ✅ Structured Evaluation Setup How to define custom evaluation dimensions using YAML or database-backed rubrics.

• 🧠 Automated Scoring with LLMs Using the ScoreEvaluator to produce structured, rationale-backed scores for each dimension.

• 🧮 Embedding-Based Hypothesis Indexing Efficiently embedding hypotheses and comparing them for contrastive learning using similarity.

• 🔄 Contrast Pair Generation Creating training pairs where one hypothesis outperforms another on a given dimension.

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.