CAISSON: Concept-Augmented Inference Suite of Self-Organizing Neural Networks

TL;DR

CAISSON introduces a hierarchical, multi-view clustering framework using dual Self-Organizing Maps to enhance retrieval-augmented generation, especially for complex multi-entity queries.

Contribution

It presents a novel multi-view clustering approach with dual SOMs for improved document organization in RAG systems, along with a synthetic evaluation framework, SynFAQA.

Findings

Significant performance improvements on complex multi-entity retrieval tasks.

Enhanced document discovery by combining semantic and conceptual views.

Maintains practical response times for interactive applications.

Abstract

We present CAISSON, a novel hierarchical approach to Retrieval-Augmented Generation (RAG) that transforms traditional single-vector search into a multi-view clustering framework. At its core, CAISSON leverages dual Self-Organizing Maps (SOMs) to create complementary organizational views of the document space, where each view captures different aspects of document relationships through specialized embeddings. The first view processes combined text and metadata embeddings, while the second operates on metadata enriched with concept embeddings, enabling a comprehensive multi-view analysis that captures both fine-grained semantic relationships and high-level conceptual patterns. This dual-view approach enables more nuanced document discovery by combining evidence from different organizational perspectives. To evaluate CAISSON, we develop SynFAQA, a framework for generating synthetic financial analyst notes and question-answer pairs that systematically tests different aspects of information retrieval capabilities. Drawing on HotPotQA's methodology for constructing multi-step reasoning questions, SynFAQA generates controlled test cases where each question is paired with the set of notes containing its ground-truth answer, progressing from simple single-entity queries to complex multi-hop retrieval tasks involving multiple entities and concepts. Our experimental results demonstrate substantial improvements over both basic and enhanced RAG implementations, particularly for complex multi-entity queries, while maintaining practical response times suitable for interactive applications.