CodaRAG: Connecting the Dots with Associativity Inspired by Complementary Learning

• URL: http://arxiv.org/abs/2604.10426v1
• Date: Sun, 12 Apr 2026 02:50:40 GMT
• Title: CodaRAG: Connecting the Dots with Associativity Inspired by Complementary Learning
• Abstract summary: CodaRAG is a framework that evolves retrieval from passive lookup into active associative discovery.<n>CodaRAG achieves absolute gains of 7-10% in retrieval recall and 3-11% in generation accuracy.
• Score: 50.98972985136333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) struggle with knowledge-intensive tasks due to hallucinations and fragmented reasoning over dispersed information. While Retrieval-Augmented Generation (RAG) grounds generation in external sources, existing methods often treat evidence as isolated units, failing to reconstruct the logical chains that connect these dots. Inspired by Complementary Learning Systems (CLS), we propose CodaRAG, a framework that evolves retrieval from passive lookup into active associative discovery. CodaRAG operates via a three-stage pipeline: (1) Knowledge Consolidation to unify fragmented extractions into a stable memory substrate; (2) Associative Navigation to traverse the graph via multi-dimensional pathways-semantic, contextualized, and functional-explicitly recovering dispersed evidence chains; and (3) Interference Elimination to prune hyper-associative noise, ensuring a coherent, high-precision reasoning context. On GraphRAG-Bench, CodaRAG achieves absolute gains of 7-10% in retrieval recall and 3-11% in generation accuracy. These results demonstrate CodaRAG's superior ability to systematically robustify associative evidence retrieval for factual, reasoning, and creative tasks.

Related papers

• Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs [55.78708003681562]
  Retrieval-Augmented Generation (RAG) mitigates hallucination in large language models (LLMs) by incorporating external knowledge during generation.<n>Existing RAG approaches typically retrieve and unstructured text fragments as context.<n>We propose Tri-RAG, a structured triplet-based retrieval framework that improves retrieval efficiency through reasoning-aligned context construction.
  arXiv  Detail & Related papers  (2026-04-14T11:36:29Z)
• Understand Then Memory: A Cognitive Gist-Driven RAG Framework with Global Semantic Diffusion [14.538534837583931]
  Retrieval-Augmented Generation (RAG) effectively mitigates hallucinations in LLMs by incorporating external knowledge.<n>We propose CogitoRAG, a RAG framework that simulates human cognitive memory processes.<n>We show that CogitoRAG significantly outperforms state-of-the-art RAG methods, showcasing superior capabilities in complex knowledge integration and reasoning.
  arXiv  Detail & Related papers  (2026-02-11T12:58:08Z)
• CoG: Controllable Graph Reasoning via Relational Blueprints and Failure-Aware Refinement over Knowledge Graphs [53.199517625701475]
  CoG is a training-free framework inspired by Dual-Process Theory that mimics the interplay between intuition and deliberation.<n>CoG significantly outperforms state-of-the-art approaches in both accuracy and efficiency.
  arXiv  Detail & Related papers  (2026-01-16T07:27:40Z)
• Disco-RAG: Discourse-Aware Retrieval-Augmented Generation [81.53888908988756]
  We propose Disco-RAG, a discourse-aware framework that injects discourse signals into the generation process.<n>Our method constructs intra-chunk discourse trees to capture local hierarchies and builds inter-chunk rhetorical graphs to model cross-passage coherence.<n>Experiments on question answering and long-document summarization benchmarks show the efficacy of our approach.
  arXiv  Detail & Related papers  (2026-01-07T20:32:50Z)
• EcphoryRAG: Re-Imagining Knowledge-Graph RAG via Human Associative Memory [0.0]
  We introduce EcphoryRAG, an entity-centric knowledge graph RAG framework.<n>During indexing, EcphoryRAG extracts and stores only core entities with corresponding metadata.<n>For retrieval, the system first extracts cue entities from queries, then performs a scalable multi-hop associative search.
  arXiv  Detail & Related papers  (2025-10-10T03:07:27Z)
• LeanRAG: Knowledge-Graph-Based Generation with Semantic Aggregation and Hierarchical Retrieval [10.566901995776025]
  LeanRAG is a framework that combines knowledge aggregation and retrieval strategies.<n>It can mitigate the substantial overhead associated with path retrieval on graphs and minimizes redundant information retrieval.
  arXiv  Detail & Related papers  (2025-08-14T06:47:18Z)
• Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs [69.10441885629787]
  Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge.<n>It falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts.<n>This survey synthesizes both strands under a unified reasoning-retrieval perspective.
  arXiv  Detail & Related papers  (2025-07-13T03:29:41Z)
• Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering [75.12322966980003]
  Large Language Models (LLMs) have shown strong inductive reasoning ability across various domains.<n>Most existing RAG pipelines rely on unstructured text, limiting interpretability and structured reasoning.<n>Recent studies have explored integrating knowledge graphs with LLMs for knowledge graph question answering.<n>We propose RAPL, a novel framework for efficient and effective graph retrieval in KGQA.
  arXiv  Detail & Related papers  (2025-06-11T12:03:52Z)
• PropRAG: Guiding Retrieval with Beam Search over Proposition Paths [15.346744525284604]
  PropRAG is a novel RAG framework that shifts from triples to context-rich propositions.<n>PropRAG achieves Recall@5 and F1 scores on 2Wiki, HotpotQA, and MuSiQue.
  arXiv  Detail & Related papers  (2025-04-25T04:47:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.