arXiv:2601.09402v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge into the generation process. Benefiting from the reasoning capabilities of LLMs, existing methods have leveraged such capabilities to enable iterative knowledge acquisition and accumulation, thereby better supporting answer generation. However, as the reasoning trajectory grows, the accumulated knowledge and previously generated queries may interfere with subsequent retrieval decisions, resulting in sub-queries with repetitive intent
The rapid advancement of large language models and their integration into practical applications like RAG necessitate continuous innovation in how they process and utilize information.
Improving the reasoning capabilities and knowledge acquisition of LLMs directly translates to more reliable and effective AI systems, enhancing their utility across various industries.
This research outlines a method to mitigate issues of 'noisy' knowledge accumulation in RAG, potentially leading to more targeted and efficient information retrieval by AI agents.
- · AI developers
- · Enterprises adopting RAG systems
- · Generative AI startups
- · Legacy knowledge retrieval systems
- · AI solutions with poor reasoning integration
More robust and less error-prone RAG implementations will become available, improving search and content generation.
Enhanced AI agent autonomy will lead to better task execution and workflow automation in white-collar sectors.
The increased efficiency and accuracy of knowledge retrieval could accelerate scientific discovery and complex problem-solving by AI.
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Read at arXiv cs.CL