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Enhancing Cache-Augmented Generation (CAG) with Adaptive Contextual Compression for Scalable Knowledge Integration

Rishabh Agrawal, Himanshu Kumar

TL;DR

The paper tackles scaling Cache-Augmented Generation (CAG) to large, dynamic knowledge bases while preserving latency. It introduces Adaptive Contextual Compression (ACC), which uses relevance scoring, hierarchical summarization, and reinforcement learning to compress preloaded context, paired with a Hybrid CAG–RAG framework that adds selective retrieval only when needed. Empirical results on HotPotQA and NaturalQuestions show ACC–CAG and the Hybrid framework achieve higher QA quality and lower memory/latency than RAG and standard CAG baselines, with notable gains in multi-hop reasoning. This work offers a scalable, modular approach to knowledge integration in LLMs, including incremental updates and efficient cache management, and points to future directions in multimodal caches and adaptive attention strategies.

Abstract

The rapid progress in large language models (LLMs) has paved the way for novel approaches in knowledge-intensive tasks. Among these, Cache-Augmented Generation (CAG) has emerged as a promising alternative to Retrieval-Augmented Generation (RAG). CAG minimizes retrieval latency and simplifies system design by preloading knowledge into the model's context. However, challenges persist in scaling CAG to accommodate large and dynamic knowledge bases effectively. This paper introduces Adaptive Contextual Compression (ACC), an innovative technique designed to dynamically compress and manage context inputs, enabling efficient utilization of the extended memory capabilities of modern LLMs. To further address the limitations of standalone CAG, we propose a Hybrid CAG-RAG Framework, which integrates selective retrieval to augment preloaded contexts in scenarios requiring additional information. Comprehensive evaluations on diverse datasets highlight the proposed methods' ability to enhance scalability, optimize efficiency, and improve multi-hop reasoning performance, offering practical solutions for real-world knowledge integration challenges.

Enhancing Cache-Augmented Generation (CAG) with Adaptive Contextual Compression for Scalable Knowledge Integration

TL;DR

The paper tackles scaling Cache-Augmented Generation (CAG) to large, dynamic knowledge bases while preserving latency. It introduces Adaptive Contextual Compression (ACC), which uses relevance scoring, hierarchical summarization, and reinforcement learning to compress preloaded context, paired with a Hybrid CAG–RAG framework that adds selective retrieval only when needed. Empirical results on HotPotQA and NaturalQuestions show ACC–CAG and the Hybrid framework achieve higher QA quality and lower memory/latency than RAG and standard CAG baselines, with notable gains in multi-hop reasoning. This work offers a scalable, modular approach to knowledge integration in LLMs, including incremental updates and efficient cache management, and points to future directions in multimodal caches and adaptive attention strategies.

Abstract

The rapid progress in large language models (LLMs) has paved the way for novel approaches in knowledge-intensive tasks. Among these, Cache-Augmented Generation (CAG) has emerged as a promising alternative to Retrieval-Augmented Generation (RAG). CAG minimizes retrieval latency and simplifies system design by preloading knowledge into the model's context. However, challenges persist in scaling CAG to accommodate large and dynamic knowledge bases effectively. This paper introduces Adaptive Contextual Compression (ACC), an innovative technique designed to dynamically compress and manage context inputs, enabling efficient utilization of the extended memory capabilities of modern LLMs. To further address the limitations of standalone CAG, we propose a Hybrid CAG-RAG Framework, which integrates selective retrieval to augment preloaded contexts in scenarios requiring additional information. Comprehensive evaluations on diverse datasets highlight the proposed methods' ability to enhance scalability, optimize efficiency, and improve multi-hop reasoning performance, offering practical solutions for real-world knowledge integration challenges.
Paper Structure (48 sections, 1 equation, 5 figures)

This paper contains 48 sections, 1 equation, 5 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Retrieval-Augmented Generation (RAG)
  4. Self-Reflective RAG
  5. Retrieval-Augmented Fine-Tuning (RAFT)
  6. Hierarchical Retrieval
  7. Knowledge-Graph Augmented Retrieval
  8. Dynamic Routing
  9. Index and ANN Optimizations
  10. Retriever-Generator Co-Training
  11. Evaluation Benchmarks
  12. Cache-Augmented Generation (CAG)
  13. Compression Techniques in LLMs
  14. Extractive Selection
  15. Abstractive Summarization
  16. ...and 33 more sections

Figures (5)

  • Figure 1: Adaptive Contextual Compression (ACC) pipeline: (1) Snippet Ranking, (2) Multi-Level Summarization, (3) Policy Optimization.
  • Figure 2: Hybrid CAG–RAG Framework: (1) Foundation Context Preloading, (2) Selective Retrieval Augmentation, (3) Dynamic Context Integration into the LLM.
  • Figure 3: Efficient Cache Management: (1) Incremental Updates, (2) Token Truncation, (3) Segmented Cache Storage.
  • Figure 4: Overall methodology: Adaptive Contextual Compression, Hierarchical Summarization, Selective Retrieval, and Segmented Cache Storage, unified in the LLM inference pipeline.
  • Figure 5: BERTScore comparison across methods and datasets.