Dynamic Context Tuning for Retrieval-Augmented Generation: Enhancing Multi-Turn Planning and Tool Adaptation

2025-06-05

arXiv.org

AI Summary

Existing RAG systems suffer from static context windows, single-turn interaction paradigms, and fixed tool inventories, rendering them ill-suited for dynamic domains—such as healthcare and smart homes—where user intent, tool availability, and environmental context evolve continuously. To address these limitations, we propose the Dynamic Context Tuning Framework (DCTF), the first approach enabling fine-tuning-free multi-turn planning consistency, zero-shot tool generalization, and lightweight online retrieval adaptation. Our key innovations include: (1) an evolvable context cache integrating attention-based relevance scoring; (2) a LoRA-driven dynamic tool retrieval mechanism; and (3) an efficient context compression technique preserving semantic fidelity. Experiments demonstrate a 14% improvement in answer accuracy, a 37% reduction in hallucination rate, and performance competitive with GPT-4—while substantially lowering inference latency and memory overhead.

Technology Category

Data Mining & Knowledge Management: Conversational Systems for Recommendation & RetrievalPlanning, Routing, and Scheduling: Planning with Language ModelsSearch and Optimization: Metareasoning and Metaheuristics

Application Category

Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAGUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingGraph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Retrieval-Augmented Generation (RAG) has significantly advanced large language models (LLMs) by grounding their outputs in external tools and knowledge sources. However, existing RAG systems are typically constrained to static, single-turn interactions with fixed toolsets, making them ill-suited for dynamic domains such as healthcare and smart homes, where user intent, available tools, and contextual factors evolve over time. We present Dynamic Context Tuning (DCT), a lightweight framework that extends RAG to support multi-turn dialogue and evolving tool environments without requiring retraining. DCT integrates an attention-based context cache to track relevant past information, LoRA-based retrieval to dynamically select domain-specific tools, and efficient context compression to maintain inputs within LLM context limits. Experiments on both synthetic and real-world benchmarks show that DCT improves plan accuracy by 14% and reduces hallucinations by 37%, while matching GPT-4 performance at significantly lower cost. Furthermore, DCT generalizes to previously unseen tools, enabling scalable and adaptable AI assistants across a wide range of dynamic environments.

Problem

• Enhancing multi-turn planning in dynamic domains
• Adapting retrieval-augmented generation to evolving toolsets
• Reducing hallucinations in dynamic context environments

Innovation

• Lightweight framework for dynamic multi-turn dialogue
• Attention-based context cache for past information tracking
• LoRA-based retrieval for dynamic tool selection