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ARXIV:2605.31377 · AI FOR MEDIA · SUBMITTED 01 JUN · 20:19 UTC · FRESHNESS STALE
ARXIV:2605.31377AI FOR MEDIASUBMITTED 01 JUN · 20:19 UTCFRESHNESS STALESiyuan Qi · Xinyuan Wang · Yingxuan Yang · Haochuan Guo · Jianghao Lin · Weiwen Liu · +2 at arXiv
DynaTree offers the most effective time-sensitive news retrieval system, outperforming benchmarks across multiple datasets.
Opportunity summary
Pain DynaTree offers the most effective time-sensitive news retrieval system, outperforming benchmarks across multiple datasets.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
DynaTree offers the most effective time-sensitive news retrieval system, outperforming benchmarks across multiple datasets. We propose DynaTree, a two-stage framework for efficient and adaptive news retrieval.
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…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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…
AI for Media moved forward this cycle; last verified June 2026. Public score 8.0/10. Production flags indicate code availability.
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DynaTree offers the most effective time-sensitive news retrieval system, outperforming benchmarks across multiple datasets.
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10.48550/arXiv.2605.31377DynaTree offers the most effective time-sensitive news retrieval system, outperforming benchmarks across multiple datasets.
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 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.
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PROBLEM
DynaTree offers the most effective time-sensitive news retrieval system, outperforming benchmarks across multiple datasets. We propose DynaTree, a two-stage framework for efficient and adaptive news retrieval.
METHOD
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 li...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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 decisi...
WHY NOW
AI for Media moved forward this cycle; last verified June 2026. Public score 8.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 12, "author": "Siyuan Qi; Xinyuan Wang; Yingxuan Yang; Haochuan Guo; Jianghao Lin; Weiwen Liu; Yong Yu; Weinan Zhang"
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DynaTree offers the most effective time-sensitive news retrieval system, outperforming benchmarks across multiple datasets.
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