@inproceedings{zhang-etal-2026-test,
title = "Test of Time: Rethinking Temporal Signal of Benchmark Contamination",
author = {Zhang, Terry Jingchen and
Dev, Gopal and
Wang, Ning and
Obreiter, Max and
Jiang, Wenyuan and
Pandey, Punya Syon and
Samway, Keenan and
Huang, Yinya and
Sch{\"o}lkopf, Bernhard and
Sachan, Mrinmaya and
Jin, Zhijing},
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1693/",
pages = "36538--36555",
ISBN = "979-8-89176-390-6",
abstract = "Post-cutoff performance decay has been widely interpreted as a temporal signal for benchmark contamination.We critically examine this belief and demonstrate that this temporal signal is highly sensitive to how benchmark questions are constructed.Specifically, we show that LLM-generated questions can produce remarkably different temporal patterns compared to fill-in-the-blank questions directly retrieved from the very same materials.We validated this finding on previous benchmarks that reported clear post-cutoff performance decay such as LiveCodeBench and further showed simple LLM transformation could effectively remove this temporal pattern when evaluated on the same models.We also provide a mechanistic understanding of our observation using influence function analysis.Overall, this work offers a new perspective on the sensitivity of temporal contamination signal and highlights the need for more robust contamination detection methods for reliable AI evaluation."
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<abstract>Post-cutoff performance decay has been widely interpreted as a temporal signal for benchmark contamination.We critically examine this belief and demonstrate that this temporal signal is highly sensitive to how benchmark questions are constructed.Specifically, we show that LLM-generated questions can produce remarkably different temporal patterns compared to fill-in-the-blank questions directly retrieved from the very same materials.We validated this finding on previous benchmarks that reported clear post-cutoff performance decay such as LiveCodeBench and further showed simple LLM transformation could effectively remove this temporal pattern when evaluated on the same models.We also provide a mechanistic understanding of our observation using influence function analysis.Overall, this work offers a new perspective on the sensitivity of temporal contamination signal and highlights the need for more robust contamination detection methods for reliable AI evaluation.</abstract>
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%0 Conference Proceedings
%T Test of Time: Rethinking Temporal Signal of Benchmark Contamination
%A Zhang, Terry Jingchen
%A Dev, Gopal
%A Wang, Ning
%A Obreiter, Max
%A Jiang, Wenyuan
%A Pandey, Punya Syon
%A Samway, Keenan
%A Huang, Yinya
%A Schölkopf, Bernhard
%A Sachan, Mrinmaya
%A Jin, Zhijing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhang-etal-2026-test
%X Post-cutoff performance decay has been widely interpreted as a temporal signal for benchmark contamination.We critically examine this belief and demonstrate that this temporal signal is highly sensitive to how benchmark questions are constructed.Specifically, we show that LLM-generated questions can produce remarkably different temporal patterns compared to fill-in-the-blank questions directly retrieved from the very same materials.We validated this finding on previous benchmarks that reported clear post-cutoff performance decay such as LiveCodeBench and further showed simple LLM transformation could effectively remove this temporal pattern when evaluated on the same models.We also provide a mechanistic understanding of our observation using influence function analysis.Overall, this work offers a new perspective on the sensitivity of temporal contamination signal and highlights the need for more robust contamination detection methods for reliable AI evaluation.
%U https://aclanthology.org/2026.acl-long.1693/
%P 36538-36555
Markdown (Informal)
[Test of Time: Rethinking Temporal Signal of Benchmark Contamination](https://aclanthology.org/2026.acl-long.1693/) (Zhang et al., ACL 2026)
ACL
- Terry Jingchen Zhang, Gopal Dev, Ning Wang, Max Obreiter, Wenyuan Jiang, Punya Syon Pandey, Keenan Samway, Yinya Huang, Bernhard Schölkopf, Mrinmaya Sachan, and Zhijing Jin. 2026. Test of Time: Rethinking Temporal Signal of Benchmark Contamination. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 36538–36555, San Diego, California, United States. Association for Computational Linguistics.