Year: 2026
Type: conference-paper
Abstract: Multi-hop reasoning remains a fundamental challenge for Retrieval-Augmented Generation (RAG) systems.Recent approaches-from adaptive retrieval to agentic pipelines-struggle to maintain coherent intermediate reasoning states as chains grow longer.We introduce State-Aware RAG 1 , a framework that addresses this limitation through an explicit working memory that serves as a dynamic cognitive workspace for reasoning.Our modular architecture features a lightweight, trainable extractor that learns to actively filter, consolidate, and update this working memory via a novel Path-Outcome Dual Reward paradigm, which balances local coherence with global strategy.The retriever and generator remain frozen, enabling plug-and-play flexibility.Experiments on eight QA benchmarks demonstrate state-of-the-art results, on average achieving +8.6% over the best memory-augmented baseline and +9.3% over the best RL-enhanced baseline.Our architecture generalizes seamlessly to stronger generators and retrievers without retraining, establishing dynamic memory management as a critical yet underexplored dimension for advancing RAG systems.
Institutions: University of Oregon, Amazon (United States)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166875301