arxiv: 2605.13052 · v1 · submitted 2026-05-13 · 💻 cs.IR · cs.CL

Recognition: unknown

RAG-Enhanced Large Language Models for Dynamic Content Expiration Prediction in Web Search

Tingyu Chen , Wenkai Zhang , Li Gao , Lixin Su , Ge Chen , Dawei Yin , Daiting Shi

Authors on Pith no claims yet

Pith reviewed 2026-05-14 18:37 UTC · model grok-4.3

classification 💻 cs.IR cs.CL

keywords LLMweb searchcontent expirationvalidity horizonsemantic freshnessdynamic predictionRAGsearch ranking

0 comments

The pith

Large language models infer query-specific validity horizons to replace static time filters in web search.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces an LLM-based framework that extracts temporal contexts from documents and predicts a query-specific semantic boundary for when content becomes obsolete. This replaces one-size-fits-all time windows with dynamic, intent-aware expiration in commercial search. Offline and online A/B tests on production traffic show gains in freshness and user metrics, confirming the approach scales to industrial use. The core idea is that LLMs can reason about semantic lifespan when guided by hallucination controls.

Core claim

Timeliness in web search is reframed as a dynamic validity inference task where LLMs deduce a query-specific validity horizon from fine-grained document temporal contexts, integrated with hallucination mitigation to enable reliable semantic expiration prediction at scale.

What carries the argument

The validity horizon, defined as a semantic boundary that marks when information becomes obsolete relative to a specific user query.

If this is right

Search rankings can exclude chronologically recent but semantically expired documents for a given query.
User experience improves because results better match the actual lifespan of the underlying information.
The same pipeline supports live A/B testing on production traffic without requiring new infrastructure.
Static time-window methods become unnecessary once query-aware horizons are available.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The method could extend to other domains with rapidly changing facts, such as product reviews or regulatory documents.
Combining the horizon predictions with retrieval-augmented generation might further reduce hallucination in related tasks.
Production systems could use horizon scores to prioritize crawling or re-indexing of short-lifespan content.

Load-bearing premise

Large language models can accurately deduce query-specific semantic expiration from document text alone when supplied with the described hallucination mitigation steps.

What would settle it

A controlled study that compares the framework's predicted validity horizons against human judgments on a held-out set of queries and documents, measuring disagreement rate on expiration dates.

Figures

Figures reproduced from arXiv: 2605.13052 by Daiting Shi, Dawei Yin, Ge Chen, Li Gao, Lixin Su, Tingyu Chen, Wenkai Zhang.

**Figure 2.** Figure 2: An overview of our proposed LLM-based Query-Aware Dynamic Content Expiration Prediction Framework. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

read the original abstract

In commercial web search, aligning content freshness with user intent remains challenging due to the highly varied lifespans of information. Traditional industrial approaches rely on static time-window filtering, resulting in "one-size-fits-all" rankings where content may be chronologically recent but semantically expired. To address the limitation, we present a novel Large Language Models (LLMs)-based Query-Aware Dynamic Content Expiration Prediction Framework deployed in Baidu search, reformulating timeliness as a dynamic validity inference task. Our framework extracts fine-grained temporal contexts from documents and leverages LLMs to deduce a query-specific "validity horizon"-a semantic boundary defining when information becomes obsolete based on user intent. Integrated with robust hallucination mitigation strategies to ensure reliability, our approach has been evaluated through offline and online A/B testing on live production traffic. Results demonstrate significant improvements in search freshness and user experience metrics, validating the effectiveness of LLM-driven reasoning for solving semantic expiration at an industrial scale.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper deploys an LLM+RAG system at Baidu to predict query-specific semantic expiration for search results instead of static time windows, but the abstract supplies no metrics or validation details to support the claimed gains.

read the letter

The paper describes a system deployed in Baidu search that uses RAG-enhanced LLMs to predict a query-specific validity horizon for web content, determining when information becomes semantically obsolete rather than relying on static time windows. They extract temporal contexts from documents and have the model infer the expiration point based on user intent, with some built-in checks against hallucinations. Offline and online A/B tests reportedly showed gains in freshness and user experience. What the work does well is tackle a genuine pain point in commercial search. Different queries have very different freshness needs, and fixed cutoffs often keep stale results or drop useful ones too soon. Applying LLMs to reason about semantic boundaries is a natural extension of recent temporal reasoning capabilities in these models, and doing it at industrial scale with live traffic testing is noteworthy. The soft spots are in the evidence. The description asserts significant improvements but provides no specific metrics, no baseline details, no error analysis on the predicted horizons, and only high-level mention of hallucination mitigation without showing how well it works or what failure modes remain. This leaves open the possibility that any observed gains come from changes in ranking behavior rather than accurate expiration predictions. The stress test concern about lacking validation against human labels or production failure cases seems on point based on what's presented. If the full paper includes those numbers and a breakdown of accuracy, it would be much stronger. As it stands, the central claim rests on unshown empirical results. This paper is for researchers and engineers working on web search relevance and freshness, particularly those exploring LLM integrations in production IR systems. A reader looking for ideas on applying LLMs to temporal aspects of search could find the framework useful as a starting point. I would bring it to a reading group to discuss the practical challenges of LLM deployment in search. I wouldn't cite it yet without the quantitative backing. It deserves peer review because the problem is important and the approach is deployed, but the reviewers should insist on detailed validation of the LLM's inference accuracy.

Referee Report

2 major / 1 minor

Summary. The manuscript presents a RAG-enhanced LLM framework for query-aware dynamic content expiration in web search. It extracts fine-grained temporal contexts from documents, uses LLMs to infer a query-specific 'validity horizon' as a semantic expiration boundary, incorporates hallucination mitigation, and claims significant gains in freshness and user-experience metrics from offline and online A/B tests on Baidu production traffic.

Significance. If the A/B results can be substantiated with quantitative metrics and validation, the work would represent a practical advance for industrial IR systems by replacing static time-window filters with intent-aware semantic expiration. The real-world deployment at scale is a concrete strength that could influence production practices, though the current absence of supporting numbers limits immediate impact assessment.

major comments (2)

[Abstract] Abstract: The assertion of 'significant improvements in search freshness and user experience metrics' supplies no quantitative values, baseline comparisons, statistical tests, or effect sizes, leaving the central empirical claim without visible support.
[Evaluation] Evaluation section: No accuracy metrics, human-label agreement, or error breakdown for the LLM-inferred validity horizons (e.g., over- or under-estimation on slowly evolving topics) are reported, which is required to attribute A/B gains specifically to correct semantic expiration rather than ranking artifacts.

minor comments (1)

[Abstract] Abstract: 'RAG' appears in the title but is not expanded on first use in the abstract; a brief parenthetical definition would aid readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comments point by point below and will incorporate revisions to strengthen the empirical support in the manuscript.

read point-by-point responses

Referee: [Abstract] Abstract: The assertion of 'significant improvements in search freshness and user experience metrics' supplies no quantitative values, baseline comparisons, statistical tests, or effect sizes, leaving the central empirical claim without visible support.

Authors: We agree that the abstract would be strengthened by including quantitative details. In the revised version, we will update the abstract to report specific relative improvements from the A/B tests (e.g., percentage gains in freshness and engagement metrics) and note statistical significance, while respecting production data confidentiality by using relative rather than absolute figures. revision: yes
Referee: [Evaluation] Evaluation section: No accuracy metrics, human-label agreement, or error breakdown for the LLM-inferred validity horizons (e.g., over- or under-estimation on slowly evolving topics) are reported, which is required to attribute A/B gains specifically to correct semantic expiration rather than ranking artifacts.

Authors: This observation is correct and highlights a gap in the current draft. We will expand the Evaluation section to include offline accuracy metrics for validity horizon prediction, human annotation agreement rates, and an error analysis stratified by topic evolution speed (including slowly changing topics). These additions will provide direct evidence linking the A/B gains to the semantic expiration component. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical A/B validation only

full rationale

The paper describes an LLM+RAG framework for query-aware validity horizon prediction, evaluated solely via offline metrics and live production A/B tests on Baidu search traffic. No equations, parameter fits, uniqueness theorems, or derivation chains are present; the central claim reduces to observed freshness gains rather than any self-referential construction or renamed input. Self-citations, if any, are not load-bearing for the reported results.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities beyond the high-level concept of a validity horizon; all technical assumptions remain unstated.

invented entities (1)

validity horizon no independent evidence
purpose: query-specific semantic boundary defining when information becomes obsolete
Introduced as the central output of the LLM reasoning step

pith-pipeline@v0.9.0 · 5476 in / 1168 out tokens · 81625 ms · 2026-05-14T18:37:50.515418+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

21 extracted references · 20 canonical work pages · 2 internal anchors

[1]

Abdelrahman Abdallah, Bhawna Piryani, Jonas Wallat, Avishek Anand, and Adam Jatowt. 2025. TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questions. arXiv:2502.21024 [cs.IR] https://arxiv.org/abs/ 2502.21024

work page arXiv 2025
[2]

Anlei Dong, Yi Chang, Zhaohui Zheng, Gilad Mishne, Jing Bai, Ruiqiang Zhang, Karolina Buchner, Ciya Liao, and Fernando Diaz. 2010. Towards recency ranking in web search. InProceedings of the Third ACM International Conference on Web Search and Data Mining(New York, New York, USA)(WSDM ’10). Association for Computing Machinery, New York, NY, USA, 11–20. ht...

work page arXiv 2010
[3]

Anlei Dong, Ruiqiang Zhang, Pranam Kolari, Jing Bai, Fernando Diaz, Yi Chang, Zhaohui Zheng, and Hongyuan Zha. 2010. Time is of the essence: improv- ing recency ranking using Twitter data. InProceedings of the 19th Interna- tional Conference on World Wide Web(Raleigh, North Carolina, USA)(WWW ’10). Association for Computing Machinery, New York, NY, USA, 3...

work page doi:10.1145/1772690.1772725 2010
[4]

Rujun Han, Xiang Ren, and Nanyun Peng. 2021. ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning. arXiv:2012.15283 [cs.CL] https://arxiv.org/abs/2012.15283

work page arXiv 2021
[5]

Hailey Joren, Jianyi Zhang, Chun-Sung Ferng, Da-Cheng Juan, Ankur Taly, and Cyrus Rashtchian. 2025. Sufficient Context: A New Lens on Retrieval Augmented Generation Systems. arXiv:2411.06037 [cs.CL] https://arxiv.org/abs/2411.06037

work page arXiv 2025
[6]

Yansheng Mao, Yufei Xu, Jiaqi Li, Fanxu Meng, Haotong Yang, Zilong Zheng, Xiyuan Wang, and Muhan Zhang. 2026. LIFT: A Novel Framework for En- hancing Long-Context Understanding of LLMs via Long Input Fine-Tuning. arXiv:2502.14644 [cs.CL] https://arxiv.org/abs/2502.14644

work page internal anchor Pith review Pith/arXiv arXiv 2026
[7]

Joon Park, Kyohei Atarashi, Koh Takeuchi, and Hisashi Kashima. 2025. Emulating Retrieval Augmented Generation via Prompt Engineering for Enhanced Long Context Comprehension in LLMs. arXiv:2502.12462 [cs.CL] https://arxiv.org/ abs/2502.12462

work page arXiv 2025
[8]

Chao Shang, Peng Qi, Guangtao Wang, Jing Huang, Youzheng Wu, and Bowen Zhou. 2021. Open temporal relation extraction for question answering. In3rd Conference on Automated Knowledge Base Construction

2021
[9]

Yedan Shen, Kaixin Wu, Yuechen Ding, Jingyuan Wen, Hong Liu, Mingjie Zhong, Zhouhan Lin, Jia Xu, and Linjian Mo. 2025. Alleviating LLM-based Generative Retrieval Hallucination in Alipay Search. InProceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’25). ACM, 4294–4298. https://doi.org/10.1...

work page doi:10.1145/3726302.3731951 2025
[10]

Xintong Song, Bin Liang, Yang Sun, Chenhua Zhang, Bingbing Wang, and Ruifeng Xu. 2025. Bridging Time Gaps: Temporal Logic Relations for Enhancing Temporal Reasoning in Large Language Models. InProceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (Padua, Italy)(SIGIR ’25). Association for Computing...

work page doi:10.1145/3726302.3730173 2025
[11]

Andrey Styskin, Fedor Romanenko, Fedor Vorobyev, and Pavel Serdyukov. 2011. Recency ranking by diversification of result set. InProceedings of the 20th ACM international conference on Information and knowledge management (CIKM ’11). ACM, 1949–1952. https://doi.org/10.1145/2063576.2063862

work page doi:10.1145/2063576.2063862 2011
[12]

Zhaochen Su, Juntao Li, Jun Zhang, Tong Zhu, Xiaoye Qu, Pan Zhou, Yan Bowen, Yu Cheng, and Min zhang. 2024. Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? arXiv:2406.09072 [cs.CL] https://arxiv. org/abs/2406.09072

work page arXiv 2024
[13]

Zhaochen Su, Jun Zhang, Tong Zhu, Xiaoye Qu, Juntao Li, Min Zhang, and Yu Cheng. 2024. Timo: Towards Better Temporal Reasoning for Language Models. arXiv:2406.14192 [cs.CL] https://arxiv.org/abs/2406.14192

work page arXiv 2024
[14]

Zhao Wang, Ziliang Zhao, and Zhicheng Dou. 2025. TimeRAG: Enhancing Com- plex Temporal Reasoning with Search Engine Augmentation. InProceedings of the 34th ACM International Conference on Information and Knowledge Management (Seoul, Republic of Korea)(CIKM ’25). Association for Computing Machinery, New York, NY, USA, 3230–3239. https://doi.org/10.1145/374...

work page doi:10.1145/3746252.3761425 2025
[15]

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv:2201.11903 [cs.CL] https: //arxiv.org/abs/2201.11903

work page internal anchor Pith review Pith/arXiv arXiv 2023
[16]

Siheng Xiong, Ali Payani, Ramana Kompella, and Faramarz Fekri. 2024. Large Language Models Can Learn Temporal Reasoning. arXiv:2401.06853 [cs.CL] https://arxiv.org/abs/2401.06853

work page arXiv 2024
[17]

Wanqi Yang, Yanda Li, Meng Fang, and Ling Chen. 2024. Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering. InFindings of the Association for Computational Linguistics: EMNLP 2024, Yaser Al-Onaizan, Mo- hit Bansal, and Yun-Nung Chen (Eds.). Association for Computational Linguistics, Miami, Florida, USA, 14495–14508. https://d...

work page doi:10.18653/v1/2024.findings- 2024
[18]

Liang Yao. 2025. Large Language Models are Contrastive Reasoners. arXiv:2403.08211 [cs.CL] https://arxiv.org/abs/2403.08211

work page arXiv 2025
[19]

Jintian Zhang, Yuqi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, and Ningyu Zhang. 2025. LightThinker: Thinking Step-by- Step Compression. arXiv:2502.15589 [cs.CL] https://arxiv.org/abs/2502.15589

work page arXiv 2025
[20]

Xinliang Frederick Zhang, Nick Beauchamp, and Lu Wang. 2024. Narrative- of-Thought: Improving Temporal Reasoning of Large Language Models via Re- counted Narratives. InFindings of the Association for Computational Linguis- tics: EMNLP 2024, Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (Eds.). Association for Computational Linguistics, Miami, Florida,...

work page doi:10.18653/v1/2024.findings-emnlp.963 2024
[21]

Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Haonan Chen, Zheng Liu, Zhicheng Dou, and Ji-Rong Wen. 2025. Large Language Models for Information Retrieval: A Survey.ACM Transactions on Information Systems44, 1 (Nov. 2025), 1–54. https://doi.org/10.1145/3748304

work page doi:10.1145/3748304 2025