Multidimensional Scoring

📖 Summary

Imagine drowning in a sea of research papers, each holding a fragment of the knowledge you need for your next breakthrough. How does an AI system, striving for self-improvement, navigate this information overload to find precisely what it needs? This is the core challenge our Document Intelligence pipeline addresses, transforming chaotic documents into organized, searchable knowledge.

In this post we combine insights from Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers and Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training to build an AI document profiler that transforms unstructured papers into structured, searchable knowledge graphs.

📖 Summary

This post introduces a multidimensional reward modeling pipeline built on top of the CO_AI 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.