Fast segmentation of watermarked texts from large language models through an epidemic change-point framework | Paper Citation Record

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This paper cites write newline.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework write newline

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This paper cites Watermarking of large language models.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Watermarking of large language models

Reference 2

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This paper cites Least squares estimation of a shift in linear processes.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Least squares estimation of a shift in linear processes

Reference 3

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This paper cites Openai, google, others pledge to watermark ai content for safety, white house says.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Openai, google, others pledge to watermark ai content for safety, white house says

Reference 4

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This paper cites On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pp.\ 610--623, 2021.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pp.\ 610--623, 2021

Reference 5

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Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Unresolved cited work

Reference 6

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This paper cites Testing synchronization of change-points for multiple time series.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Testing synchronization of change-points for multiple time series

Reference 7

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This paper cites A statistical hypothesis testing framework for data misappropriation detection in large language models.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework A statistical hypothesis testing framework for data misappropriation detection in large language models

Reference 8

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This paper cites Towards Better Statistical Understanding of Watermarking LLMs.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Towards Better Statistical Understanding of Watermarking LLMs

Reference 9

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This paper cites Can LLM-Generated Misinformation Be Detected?.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Can LLM-Generated Misinformation Be Detected?

Reference 10

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This paper cites Detecting change-points in epidemic models.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Detecting change-points in epidemic models

Reference 11

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This paper cites Undetectable watermarks for language models.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Undetectable watermarks for language models

Reference 12

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This paper cites Machine-generated text: A comprehensive survey of threat models and detection methods.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Machine-generated text: A comprehensive survey of threat models and detection methods

Reference 13

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This paper cites Limit theorems in change-point analysis.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Limit theorems in change-point analysis

Reference 14

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This paper cites an unresolved cited work.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Unresolved cited work

Reference 15

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This paper cites Measuring Association on Topological Spaces Using Kernels and Geometric Graphs.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Measuring Association on Topological Spaces Using Kernels and Geometric Graphs

Reference 16

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This paper cites Donsker and S.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Donsker and S

Reference 17

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This paper cites Three bricks to consolidate watermarks for large language models.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Three bricks to consolidate watermarks for large language models

Reference 18

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Observation 1a0520b5-684f-4f56-a6d0-79f58e4c208e · outbound

This paper cites Probability inequalities for sums of independent random variables.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Probability inequalities for sums of independent random variables

Reference 19

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This paper cites Model equality testing: Which model is this API serving? In The Thirteenth International Conference on Learning Representations, 2025.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Model equality testing: Which model is this API serving? In The Thirteenth International Conference on Learning Representations, 2025

Reference 20

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This paper cites GLTR: Statistical Detection and Visualization of Generated Text.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework GLTR: Statistical Detection and Visualization of Generated Text

Reference 21

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This paper cites Edit distance robust watermarks via indexing pseudorandom codes.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Edit distance robust watermarks via indexing pseudorandom codes

Reference 22

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This paper cites H\' a jek and A.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework H\' a jek and A

Reference 23

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This paper cites Hall and C.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Hall and C

Reference 24

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This paper cites Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text

Reference 25

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This paper cites Unbiased watermark for large language models.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Unbiased watermark for large language models

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Observation 3f2d7952-bb19-4ae2-9f5e-c29e25d9d535 · outbound

This paper cites Towards optimal statistical watermarking.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Towards optimal statistical watermarking

Reference 27

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This paper cites Estimators for epidemic alternatives.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Estimators for epidemic alternatives

Reference 28

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This paper cites Use of cumulative sums of squares for retrospective detection of changes of variance.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Use of cumulative sums of squares for retrospective detection of changes of variance

Reference 29

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This paper cites A watermark for large language models.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework A watermark for large language models

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This paper cites On the reliability of watermarks for large language models.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework On the reliability of watermarks for large language models

Reference 31

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Observation 7a0d14cc-6adb-4973-88e6-95988f3e2ea0 · outbound

This paper cites Change-point analysis with irregular signals.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Change-point analysis with irregular signals

Reference 32

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Observation a9bca3c5-e11c-407d-982d-1e3642ca2b75 · outbound

This paper cites Robust distortion-free watermarks for language models.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Robust distortion-free watermarks for language models

Reference 33

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Observation e1e50b9b-259d-499b-8d37-189714bb0e6a · outbound

This paper cites Detecting fake content with relative entropy scoring.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Detecting fake content with relative entropy scoring

Reference 34

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Observation 7f805867-28d6-4d84-b87e-c5d3c19043c7 · outbound

This paper cites The cusum test of homogeneity with an application in spontaneous abortion epidemiology.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework The cusum test of homogeneity with an application in spontaneous abortion epidemiology

Reference 35

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Observation a06a59ef-7fba-40e3-801f-0fa30abe5d0d · outbound

This paper cites Robust Detection of Watermarks for Large Language Models Under Human Edits.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Robust Detection of Watermarks for Large Language Models Under Human Edits

Reference 36

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This paper cites A statistical framework of watermarks for large language models: Pivot, detection efficiency and optimal rules.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework A statistical framework of watermarks for large language models: Pivot, detection efficiency and optimal rules

Reference 37

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This paper cites Optimal Estimation of Watermark Proportions in Hybrid AI-Human Texts.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Optimal Estimation of Watermark Proportions in Hybrid AI-Human Texts

Reference 38

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This paper cites Segmenting watermarked texts from language models.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Segmenting watermarked texts from language models

Reference 39

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This paper cites Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews

Reference 40

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This paper cites Adaptive text watermark for large language models.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Adaptive text watermark for large language models

Reference 41

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This paper cites Architecture of a fake news detection system combining digital watermarking, signal processing, and machine learning.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Architecture of a fake news detection system combining digital watermarking, signal processing, and machine learning

Reference 42

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This paper cites Large language models challenge the future of higher education.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Large language models challenge the future of higher education

Reference 43

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This paper cites Detectgpt: Zero-shot machine-generated text detection using probability curvature.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Detectgpt: Zero-shot machine-generated text detection using probability curvature

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:55:30.565438Z

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This paper cites Empirical likelihood ratio test for the epidemic change model.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Empirical likelihood ratio test for the epidemic change model

Reference 45

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Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Unresolved cited work

Reference 46

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Observation 75d0b4f1-a20b-4329-8d97-d23ced816d9e · outbound

This paper cites A more efficient algorithm to compute the Rand Index for change-point problems.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework A more efficient algorithm to compute the Rand Index for change-point problems

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This paper cites Evaluating Durability: Benchmark Insights into Multimodal Watermarking.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Evaluating Durability: Benchmark Insights into Multimodal Watermarking

Reference 48

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This paper cites H \"o lder norm test statistics for epidemic change.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework H \"o lder norm test statistics for epidemic change

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:55:30.509645Z

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This paper cites Testing epidemic changes of infinite dimensional parameters.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Testing epidemic changes of infinite dimensional parameters

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:55:30.493425Z

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This paper cites Robust speech recognition via large-scale weak supervision.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Robust speech recognition via large-scale weak supervision

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This paper cites Zero-shot statistical tests for llm-generated text detection using finite sample concentration inequalities.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Zero-shot statistical tests for llm-generated text detection using finite sample concentration inequalities

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Resolution: verified exact; raw_fallback, observed 2026-08-15T15:55:29.868011Z

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Observation 6c9cd551-6e9e-49c7-b26f-fd8aaac93a37 · outbound

This paper cites Release Strategies and the Social Impacts of Language Models.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Release Strategies and the Social Impacts of Language Models

Reference 53

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This paper cites Deep kernel relative test for machine-generated text detection.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Deep kernel relative test for machine-generated text detection

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:55:30.464456Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7459cbdf-8c87-40aa-9403-8d711709f642 · outbound

This paper cites Detect LLM : Leveraging log rank information for zero-shot detection of machine-generated text.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Detect LLM : Leveraging log rank information for zero-shot detection of machine-generated text

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:55:30.445869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e38c4b38-91bf-4b36-bc5e-73a9ac553574 · outbound

This paper cites HowkGPT: Investigating the Detection of ChatGPT-generated University Student Homework through Context-Aware Perplexity Analysis.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework HowkGPT: Investigating the Detection of ChatGPT-generated University Student Homework through Context-Aware Perplexity Analysis

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:29.569786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00cdd031-4c08-492c-a017-504991c4249c · outbound

This paper cites Ai is tearing wikipedia apart, May 2023.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Ai is tearing wikipedia apart, May 2023

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:55:30.422780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6dbfddbf-1eee-4f44-b5e0-8166a6fbbcdb · outbound

This paper cites A resilient and accessible distribution-preserving watermark for large language models.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework A resilient and accessible distribution-preserving watermark for large language models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:55:30.400721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9e4e742d-4989-4e42-b182-d7525968c1a9 · outbound

This paper cites Tests for change-points with epidemic alternatives.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Tests for change-points with epidemic alternatives

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:55:30.378909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ffe8913b-6171-400d-a03f-53afaf6fbedd · outbound

This paper cites Provable robust watermarking for AI -generated text.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Provable robust watermarking for AI -generated text

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:55:30.358724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 92cb6d63-15b7-493b-806c-56637842e1d0 · outbound

This paper cites Efficiently identifying watermarked segments in mixed-source texts.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Efficiently identifying watermarked segments in mixed-source texts

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:55:30.336352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6a174d3e-a5b6-4ae0-aefb-a12eb11a6f27 · outbound

This paper cites Permute-and-flip: An optimally stable and watermarkable decoder for LLM s.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Permute-and-flip: An optimally stable and watermarkable decoder for LLM s

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:55:30.317612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5a2ca69d-034a-4ab6-af99-9e3920828c7d · outbound

This paper cites Duwak: Dual watermarks in large language models.

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework Duwak: Dual watermarks in large language models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:29.605822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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