Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding
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.
69493ea1d2000e944d383ab5
Yuqing Li
claimed_verified
2026-02-13T09:38:19.625Z
6891bbff78946201296b4592
https://cdn-avatars.huggingface.co/v1/production/uploads/6891bbff78946201296b4592/ECmWBrlfeonPg0HzmQ_sW.png
Yuqing Li
MindscapeRAG
user
MindscapeRAG
69493ea1d2000e944d383ab6
Jiangnan Li
claimed_verified
2026-02-13T09:38:21.581Z
6670f52a7a0446ec70e11c96
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Jiangnan Li
lossisnotanumber
user
lossisnotanumber
69493ea1d2000e944d383ab7
Zheng Lin
69493ea1d2000e944d383ab8
Ziyan Zhou
69493ea1d2000e944d383ab9
Junjie Wu
69493ea1d2000e944d383aba
Weiping Wang
69493ea1d2000e944d383abb
Jie Zhou
69493ea1d2000e944d383abc
Mo Yu
claimed_verified
2025-12-31T20:57:56.853Z
67af92045a86287292026808
/avatars/8bad9272fe73ba04e077b5484837c8d3.svg
Mo
BishopGorov
user
BishopGorov
Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding
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.
MiA-RAG enhances long-context reasoning in LLM-based RAG systems by incorporating hierarchical summarization to create global semantic representations that guide both retrieval and generation processes.
Retrieval-Augmented Generation LLM-based RAG systems global context awareness hierarchical summarization mindscape query embeddings coherent global representation long-context retrieval evidence-based understanding global sense-making