arXiv:2605.31377v1 Announce Type: cross Abstract: Agentic Retrieval-Augmented Generation improves retrieval by integrating planning, tool use, and iterative reasoning, but existing agentic RAG methods often couple semantic expansion with retrieval decisions in short-horizon inference loops, leading to high inference cost and limited suitability for time-sensitive news retrieval. We propose DynaTree, a two-stage framework for efficient and adaptive news retrieval. In the offline stage, DynaTree uses coordinated agents to construct a reusable retrieval tree that materializes the semantic space o
The proliferation of agentic systems and the increasing demand for real-time, relevant information necessitate more efficient and adaptive retrieval methods, especially for time-sensitive data like news.
Efficient agentic retrieval for time-sensitive information reduces inference costs and could significantly enhance the performance and applicability of AI in fields requiring rapid knowledge updates, such as financial analysis or intelligence.
This framework offers a more performant and cost-effective approach to RAG, potentially enabling new applications of AI agents that were previously too expensive or slow for dynamic data environments.
- · AI-powered news aggregators
- · Financial data providers
- · Intelligence agencies
- · Cloud providers benefiting from optimized AI workloads
- · Inefficient RAG systems
- · High-latency information analysis platforms
Improved accuracy and speed of AI agents tasked with real-time information processing.
Expansion of AI agent applications into high-frequency, time-sensitive domains previously challenging due to computational overhead.
Potential for new business models and services built around hyper-efficient, real-time information synthesis by AI agents.
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Read at arXiv cs.AI