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

2026-05-29 · Siyuan Qi, Xinyuan Wang, Yingxuan Yang, Haochuan Guo, Jianghao Lin, Weiwen Liu, Yong Yu, Weinan Zhang arxiv

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 of a query topic. In the online stage, DynaTree performs lightweight daily subtree selection over a time-localized evaluation proxy, without further agentic reasoning, tree modification, or retraining. Experiments on a multi-day Syft news benchmark and multiple BEIR datasets show that DynaTree achieves strong recall and ranking performance, consistently outperforming standard RAG and prior agentic baselines. We further deploy DynaTree in the Syft production system and evaluate it through online A/B testing from Jan. 28 to Feb. 6, 2026. The dynamically adapted variant improves survival rate from 0.32-0.53 to 0.59-0.73 over a fixed offline-selected subtree and outperforms existing production recallers on every evaluation day. These results show that persistent, structure-aware semantic expansion can translate offline agentic reasoning into practical improvements in coverage, freshness, and relevance for real-world news retrieval.

📄 PDF Abstract BibTeX arXiv:2605.31377

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RAPTOR-AI for Disaster OODA Loop: Hierarchical Multimodal RAG with Experience-Driven Agentic Decision-Making

2026-01-18 · Takato Yasuno arxiv

Humanitarian Assistance and Disaster Relief (HADR) operations demand rapid synthesis of multimodal information for time-critical decision-making under extreme uncertainty. Traditional information systems struggle with th…

TreePS-RAG: Tree-based Process Supervision for Reinforcement Learning in Agentic RAG

2026-01-11 · Tianhua Zhang, Kun Li, Junan Li, Yunxiang Li 외 arxiv

Agentic retrieval-augmented generation (RAG) formulates question answering as a multi-step interaction between reasoning and information retrieval, and has recently been advanced by reinforcement learning (RL) with outco…

Reinforcement Learning Information Retrieval Question Answering

VecTree-RAG: An Agentic Retrieval-Augmented Generation Framework Combining Vector and Tree Retrieval for Efficiency and Accuracy

2026-07-25 · Xinyan Zhong, Yuwei Shi, Yuqi Wei, Chen Shen 외 arxiv

Scientific question answering requires a retrieval system to solve two distinct problems: identifying which papers are relevant and locating the supporting evidence within those papers. Conventional retrieval-augmented g…

Question Answering

Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents

2026-06-03 · Yifan Simon Liu, Liam Gallagher, Faeze Moradi Kalarde, Jiazhou Liang 외 arxiv

Long-horizon conversational agents need to interact with users through evolving events, tasks, and goals. Such histories are naturally temporal, yet many existing memory systems organize information primarily by topical …

Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

2025-01-15 · Aditi Singh, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei

Large Language Models (LLMs) have revolutionized artificial intelligence (AI) by enabling human like text generation and natural language understanding. However, their reliance on static training data limits their abilit…

Natural Language Understanding RAG Retrieval Retrieval-augmented Generation+2