Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding
This addresses the challenge of long-context tasks for LLM-based RAG systems, representing an incremental advancement by integrating global awareness into existing frameworks.
The paper tackled the problem of long-context understanding in Retrieval-Augmented Generation (RAG) systems by proposing Mindscape-Aware RAG (MiA-RAG), which uses hierarchical summarization to build a global semantic representation, resulting in consistent performance improvements over baselines on diverse benchmarks.
Humans understand long and complex texts by relying on a holistic semantic representation of the content. This global view helps organize prior knowledge, interpret new information, and integrate evidence dispersed across a document, as revealed by the Mindscape-Aware Capability of humans in psychology. Current Retrieval-Augmented Generation (RAG) systems lack such guidance and therefore struggle with long-context tasks. In this paper, we propose Mindscape-Aware RAG (MiA-RAG), the first approach that equips LLM-based RAG systems with explicit global context awareness. MiA-RAG builds a mindscape through hierarchical summarization and conditions both retrieval and generation on this global semantic representation. This enables the retriever to form enriched query embeddings and the generator to reason over retrieved evidence within a coherent global context. We evaluate MiA-RAG across diverse long-context and bilingual benchmarks for evidence-based understanding and global sense-making. It consistently surpasses baselines, and further analysis shows that it aligns local details with a coherent global representation, enabling more human-like long-context retrieval and reasoning.