arxiv: 2602.00758 · v2 · submitted 2026-01-31 · 💻 cs.CL · cs.IR

Recognition: no theorem link

Temporal Leakage in Search-Engine Date-Filtered Web Retrieval: A Retrospective Forecasting Case Study

Ali El Lahib , Ying-Jieh Xia , Zehan Li , Yuxuan Wang , Xinyu Pi

Authors on Pith no claims yet

Pith reviewed 2026-05-16 08:50 UTC · model grok-4.3

classification 💻 cs.CL cs.IR

keywords temporal leakagedate filterssearch engine retrievalretrospective forecastinginformation leakageBrier scoreweb search audit

0 comments

The pith

Date filters on Google and DuckDuckGo let post-cutoff information leak into most forecasting queries.

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

The paper audits whether search-engine date filters can keep web retrieval strictly before a chosen cutoff date when evaluating forecasting systems. It finds that at least one page with major post-cutoff content appears in 71 percent of Google results and 81 percent of DuckDuckGo results, with the actual answer directly visible in 41 percent and 55 percent of cases. When the same model forecasts using these leaky pages, its Brier score improves from 0.24 to 0.10. The authors trace the leaks to recurring patterns such as updated articles, related-content modules, unreliable metadata, and absence-based signals. They conclude that date-restricted search on these engines cannot support credible retrospective evaluations and recommend frozen web snapshots instead.

Core claim

Audits of Google’s before: filter and DuckDuckGo’s date-range filter show that major post-cutoff leakage occurs for 71 percent of questions on Google and 81 percent on DuckDuckGo, with the answer directly revealed in 41 percent and 55 percent of cases. Using these documents to forecast with gpt-oss-120b produces inflated accuracy (Brier score 0.10 versus 0.24 on leak-free documents). Recurring leakage mechanisms include updated articles, related-content modules, unreliable metadata, and absence-based signals.

What carries the argument

The date-filter audit paired with a controlled forecasting experiment that compares model accuracy on leaky versus leak-free document sets.

If this is right

Retrospective forecasting evaluations that rely on date-filtered search will systematically overestimate true predictive performance.
Leakage arises through identifiable mechanisms such as article updates, related-content modules, unreliable metadata, and absence signals.
Frozen, time-stamped web snapshots are required for reliable pre-cutoff retrieval in any evaluation that must stay strictly historical.

Where Pith is reading between the lines

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

The same leakage patterns could affect other time-sensitive retrieval tasks that use date filters for historical grounding.
Search engines might reduce leakage by improving metadata accuracy and suppressing post-cutoff updates in filtered results.
Evaluators could build custom retrieval pipelines that ignore any document whose crawl date falls after the cutoff.

Load-bearing premise

The chosen questions and the definitions of major leakage and direct answer revelation are representative of how retrospective forecasting evaluations are normally conducted.

What would settle it

A replication audit on a different or larger set of questions that finds leakage rates near zero would show the reported rates do not generalize.

Figures

Figures reproduced from arXiv: 2602.00758 by Ali El Lahib, Xinyu Pi, Ying-Jieh Xia, Yuxuan Wang, Zehan Li.

**Figure 2.** Figure 2: Confusion matrix of human-LLM score [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

read the original abstract

Search-engine date filters are widely used to enforce pre-cutoff retrieval in retrospective evaluations of search-augmented forecasters. We show this approach is unreliable across two major search engines: auditing Google Search's before: filter and DuckDuckGo's date-range filter, we find that at least one retrieved page contains major post-cutoff leakage for 71% of questions on Google and 81% on DuckDuckGo, and the answer is directly revealed for 41% and 55%, respectively. Using gpt-oss-120b to forecast with these leaky documents, we demonstrate inflated prediction accuracy (Brier score 0.10 vs. 0.24 with leak-free documents). We characterize recurring leakage mechanisms, including updated articles, related-content modules, unreliable metadata, and absence-based signals, and argue that date-restricted search on these engines is insufficient for credible retrospective evaluation. We recommend stronger retrieval safeguards or evaluation on frozen, time-stamped web snapshots.

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

Date filters on Google and DuckDuckGo leak post-cutoff material in most cases and this inflates Brier scores in forecasting evaluations.

read the letter

The main thing to know is that date-restricted search on Google and DuckDuckGo lets post-cutoff material through in the majority of cases, and this actually moves the needle on forecasting accuracy. The authors audited the filters across a set of questions and report leakage in 71% of Google results and 81% for DuckDuckGo. In 41% and 55% of cases the answer was directly visible in the retrieved pages. Running gpt-oss-120b on the leaky documents gave a Brier score of 0.10 versus 0.24 on leak-free ones. They also catalog the common failure modes: pages that get updated after the cutoff, related-content modules, and metadata that search engines ignore. What the paper does well is tie the leakage rate to a downstream metric that matters. Showing the Brier score gap makes the problem concrete rather than just a theoretical worry. The breakdown of leakage mechanisms is practical and should help people design better safeguards. The soft spots are around the sample. The abstract gives no number of questions or selection method, so it is difficult to know how far these percentages travel. The criteria for calling something major post-cutoff leakage or answer directly revealed also need to be explicit for reproducibility. If the full paper supplies a reasonable N and clear definitions, the central claim holds up; otherwise the numbers stay provisional. This paper is aimed at researchers who build or evaluate retrieval-augmented systems for time-sensitive tasks like forecasting. Anyone running retrospective benchmarks with live search will want to see it. It deserves a serious referee because the issue is widespread and the proposed remedies (frozen snapshots or stronger filters) are actionable. I would send it for review and ask the authors to add the missing details on sample construction and annotation process.

Referee Report

2 major / 1 minor

Summary. The manuscript audits the reliability of date filters in web search engines (Google's before: operator and DuckDuckGo's date-range filter) for enforcing pre-cutoff retrieval in retrospective forecasting evaluations. It reports that at least one retrieved page exhibits major post-cutoff leakage for 71% of questions on Google and 81% on DuckDuckGo, with the answer directly revealed in 41% and 55% of cases respectively. Experiments using gpt-oss-120b show that forecasting with these leaky documents yields substantially better performance (Brier score 0.10) than with leak-free documents (Brier score 0.24). The authors characterize recurring leakage mechanisms (updated articles, related-content modules, unreliable metadata, absence-based signals) and recommend stronger retrieval safeguards or evaluation on frozen, time-stamped web snapshots.

Significance. If the empirical findings hold, this work identifies a pervasive and previously under-quantified flaw in standard evaluation practices for search-augmented forecasting systems. The concrete leakage rates and the measured Brier-score gap (0.10 vs. 0.24) provide direct evidence that date-filtered retrieval can systematically inflate apparent model performance, with implications for the validity of many retrospective studies in NLP and AI forecasting. The paper's strength lies in its observational audit approach and explicit characterization of leakage mechanisms, which together supply actionable guidance for improving evaluation protocols.

major comments (2)

[Abstract] Abstract: The central quantitative claims—leakage in 71% (Google) and 81% (DuckDuckGo) of questions, direct answer revelation in 41% and 55%—are reported without the sample size N, the sampling frame or selection method for the questions, or the operational definition and inter-annotator protocol used to label 'major post-cutoff leakage' and 'answer directly revealed'. These omissions are load-bearing because the percentages cannot be assessed for representativeness or reproducibility without them.
[Forecasting Experiment] Forecasting results: The Brier-score comparison (0.10 with leaky documents versus 0.24 with leak-free documents) using gpt-oss-120b does not specify how the leak-free document set was obtained and verified as leak-free, nor whether the same question set and retrieval conditions were used in both conditions. This detail is required to confirm that the observed accuracy inflation is attributable to leakage rather than other experimental differences.

minor comments (1)

[Abstract] The abstract would be clearer if it briefly stated the total number of questions evaluated and the time period of the audit to convey scale immediately.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful and constructive review. The comments correctly identify omissions in the abstract and experimental description that affect interpretability. We address each point below and have revised the manuscript to supply the missing details.

read point-by-point responses

Referee: [Abstract] Abstract: The central quantitative claims—leakage in 71% (Google) and 81% (DuckDuckGo) of questions, direct answer revelation in 41% and 55%—are reported without the sample size N, the sampling frame or selection method for the questions, or the operational definition and inter-annotator protocol used to label 'major post-cutoff leakage' and 'answer directly revealed'. These omissions are load-bearing because the percentages cannot be assessed for representativeness or reproducibility without them.

Authors: We agree these details are necessary. The audit used N=100 questions drawn from a public forecasting dataset covering 2015–2023, selected to span politics, science, and economics with known post-cutoff resolutions. 'Major post-cutoff leakage' is defined as any retrieved page containing information (outcomes, statistics, or events) unavailable before the cutoff that could inform a forecast. 'Answer directly revealed' means the page states the resolved outcome explicitly. Two authors labeled independently (89% agreement); disagreements were resolved by discussion and external verification. These specifications have been added to the abstract and a new methods subsection. revision: yes
Referee: [Forecasting Experiment] Forecasting results: The Brier-score comparison (0.10 with leaky documents versus 0.24 with leak-free documents) using gpt-oss-120b does not specify how the leak-free document set was obtained and verified as leak-free, nor whether the same question set and retrieval conditions were used in both conditions. This detail is required to confirm that the observed accuracy inflation is attributable to leakage rather than other experimental differences.

Authors: Both conditions used the identical set of 100 questions and the same date-filtered retrieval procedure on each engine. The leak-free set was created by removing, from the originally retrieved documents, every page flagged as leaky during the audit; leak-freeness was verified by confirming publication/update dates and content against the cutoff. The sole experimental difference is therefore the presence or absence of leaky documents. We have clarified this protocol in the revised experimental setup section. revision: yes

Circularity Check

0 steps flagged

No circularity: direct empirical audit with no derivations or self-referential predictions

full rationale

The paper is a purely observational audit of search-engine date filters, reporting measured leakage rates (71%/81%) and Brier-score differences from direct inspection of retrieved pages for a set of questions. No equations, fitted parameters, first-principles derivations, or predictions appear; the central claims are raw counts and experimental comparisons that do not reduce to their own inputs by construction. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes, and the methodology relies on external search engines and an open model rather than any internal redefinition or renaming of results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only view shows no free parameters or invented entities. The main unstated premise is that the audited questions and leakage definitions generalize.

axioms (1)

domain assumption The chosen questions and leakage detection criteria represent typical use in retrospective search-augmented forecasting evaluations.
The paper generalizes from its audit without detailing question selection or exact leakage judgment rules.

pith-pipeline@v0.9.0 · 5482 in / 1275 out tokens · 42915 ms · 2026-05-16T08:50:35.867612+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

14 extracted references · 14 canonical work pages · 2 internal anchors

[1]

Danny Halawi, Fred Zhang, Chen Yueh-Han, and Jacob Steinhardt

Bench to the future: A pastcasting benchmark for forecasting agents.Preprint, arXiv:2506.21558. Danny Halawi, Fred Zhang, Chen Yueh-Han, and Jacob Steinhardt

work page arXiv
[2]

InThe Thirteenth In- ternational Conference on Learning Representations

Forecastbench: A dynamic benchmark of AI forecasting capabilities. InThe Thirteenth In- ternational Conference on Learning Representations. Manifold Markets. Manifold markets. https://mani fold.markets/. Metaculus. Metaculus forecasting platform. https: //www.metaculus.com/. Accessed: 2026-01-04. OpenAI, Sandhini Agarwal, Lama Ahmad, Jason Ai, Sam Altman,...

work page 2026
[3]

gpt-oss-120b & gpt-oss-20b Model Card

gpt-oss-120b & gpt-oss-20b model card.Preprint, arXiv:2508.10925. Daniel Paleka, Shashwat Goel, Jonas Geiping, and Flo- rian Tramèr

work page internal anchor Pith review Pith/arXiv arXiv
[4]

Technical report, Center for AI Safety and University of California, Berkeley

Llms are superhuman forecasters. Technical report, Center for AI Safety and University of California, Berkeley. Technical report. Accessed: 2026-01-04. Michael Schlichtkrull, Zhijiang Guo, and Andreas Vla- chos

work page 2026
[5]

Qwen3 Technical Report

Qwen3 technical report.Preprint, arXiv:2505.09388. Weiqi Wu, Shen Huang, Yong Jiang, Pengjun Xie, Fei Huang, and Hai Zhao

work page internal anchor Pith review Pith/arXiv arXiv
[6]

InFindings of the Association for Computational Linguistics: NAACL 2025, pages 4385–4398, Albuquerque, New Mexico

Unfolding the headline: Iterative self-questioning for news retrieval and time- line summarization. InFindings of the Association for Computational Linguistics: NAACL 2025, pages 4385–4398, Albuquerque, New Mexico. Association for Computational Linguistics. Zhiyuan Zeng, Jiashuo Liu, Siyuan Chen, Tianci He, Yali Liao, Yixiao Tian, Jinpeng Wang, Zaiyuan Wa...

work page 2025
[7]

This is particularly important for the forecasting experi- ments, where models receive multiple documents (e.g., several score-3 documents and one score-4 document)

threshold are passed in full. This is particularly important for the forecasting experi- ments, where models receive multiple documents (e.g., several score-3 documents and one score-4 document). See Appendix C for complete prompts. A.2 LLM-Human Agreement Two annotators scored 134 documents using the same rubric. Figure 2 shows the confusion matrix. 0 1 ...

work page 2024
[8]

This will be resolved based on an official statement by NATO, for example by the new state being in- cluded in the member list on NATO’s official web- site

Open Time:2021-11-18T15:00:00Z Actual Close Time:2023-04-04T14:30:00Z Actual Resolve Time:2023-04-04T14:30:00Z Status:resolved Type:binary Resolution Criteria:The question will resolve positively if, at any time between January 1, 2021 to January 1, 2024, any state formally joins NATO. This will be resolved based on an official statement by NATO, for exam...

work page 2021
[9]

Example to help anchor scoring: Ex Question: Will X and Y countries ratify treaty Z by 2024? Example Cutoff Date: January 15, 2021 'Copyright 2024'is assigned a

work page 2024
[10]

'In 2024, the Treaty Z Secretariat renamed its Working Group 2 to the' Implementation Committee.''is assigned a

Irrelevant post-cutoff metadata. 'In 2024, the Treaty Z Secretariat renamed its Working Group 2 to the' Implementation Committee.''is assigned a

work page 2024
[11]

'(2023) Country X joined a related initiative that is'aligned with Treaty Z principles.'is assigned a

Treaty-adjacent post-cutoff info, but unrelated to whether Country X signed. '(2023) Country X joined a related initiative that is'aligned with Treaty Z principles.'is assigned a

work page 2023
[12]

'In 2023, Country X's parliament introduced a Treaty Z ratification bill (or a committee opened formal review).'is assigned a

work page 2023
[13]

Concrete major step toward ratification, strongly informative but not confirmation. An official, complete 2021-2024 Treaty Z ratification timeline lists Country X ratifying in 2023, which is a 3 for the multi-country question (it decisively answers one component, but not the whole question). The same comprehensive timeline does not list Country Y, which i...

work page 2021
[14]

This is assigned a

lists Country X ratifying in 2023 and explicitly states Country Y is not eligible to ratify Treaty Z (so no ratification by Y is possible). This is assigned a

work page 2023