DynaTree: Dynamic Agentic Retrieval Tree for Time-Sensitive News Retrieval

DynaTree is a two-stage framework for efficient news retrieval that combines offline agentic reasoning with lightweight online subtree selection, achieving significant improvements in real-world deployment. The system demonstrated a 59-73% survival rate versus 32-53% for fixed approaches in production A/B testing, highlighting the practical value of persistent semantic expansion for time-sensitive information retrieval.

DynaTree addresses a critical limitation in existing agentic retrieval systems: the computational inefficiency of continuous semantic reasoning during runtime. Traditional agentic RAG couples expansion with retrieval in short loops, creating latency and cost challenges unsuitable for real-time news applications where freshness directly impacts relevance. By separating planning from execution, DynaTree materializes semantic understanding offline, then applies lightweight daily selection online without repeated agentic inference. This architectural innovation transforms how systems handle dynamic information landscapes.

The framework emerges from a broader industry shift toward hybrid AI systems that balance sophistication with operational efficiency. As retrieval-augmented generation becomes standard in production systems, the bottleneck shifts from capability to performance. DynaTree demonstrates that structured semantic expansion—when properly materialized—persists meaningfully across time, enabling adaptive behavior without constant recomputation. The research reflects growing maturity in applying agentic reasoning thoughtfully rather than exhaustively.

For news platforms and information retrieval companies, DynaTree offers measurable benefits: the 59-73% survival rate improvement directly translates to better content coverage and user satisfaction. The approach particularly suits enterprise applications where retrieval quality drives engagement metrics. Developers building on LLM infrastructure can adopt DynaTree's two-stage pattern to reduce inference costs while maintaining adaptive performance, establishing a template for efficient agentic systems at scale.

The production deployment through Syft validates research-to-practice translation. As time-sensitive applications proliferate across finance, media, and search, systems combining persistent knowledge structures with lightweight adaptation become competitive advantages. The consistent outperformance across multiple benchmarks suggests the approach generalizes beyond news retrieval.

• →DynaTree's two-stage architecture separates offline semantic planning from online lightweight selection, reducing inference costs for time-sensitive retrieval.
• →Production A/B testing achieved 59-73% survival rate versus 32-53% for fixed approaches, demonstrating substantial real-world improvements.
• →The framework outperforms standard RAG and prior agentic baselines on both benchmark datasets and production evaluation metrics.
• →Persistent semantic expansion materialized offline enables adaptive behavior without continuous agentic reasoning or model retraining.
• →The approach establishes a reusable template for deploying agentic systems in latency-sensitive applications requiring dynamic adaptation.