Spectral Graph Pruning for Context Optimization in Retrieval-Augmented Generation

Sat, 09 May 2026 00:00:00 +0000

Abstract

Spectral Graph Pruning (SGP) is a framework for efficient context optimization and compression in Retrieval-Augmented Generation (RAG). It models retrieved text segments as a heterogeneous semantic graph and applies query-biased spectral centrality analysis to identify and retain the most structurally important segments. SGP reduces token consumption by 40-50% while maintaining high reasoning accuracy on multi-hop benchmarks.

Methodology

The SGP framework constructs a heterogeneous graph representing Chunk Nodes, Entity Nodes, and Structural document hierarchy.

NLP on Aditya Bavadekar

Spectral Graph Pruning for Context Optimization in Retrieval-Augmented Generation

Abstract #

Methodology #

Abstract

Methodology