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Paper Citation Record · LEDGER

Watermarking across Modalities for Content Tracing and Generative AI

As of 13 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2502.05215.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.05215 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:46:23.205545Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

  • verified exact1
  • verified fuzzy3
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

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Outbound references

Observation 17da1c9a-18c3-4e11-bfa1-38d071b070e8 · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Watermarking across Modalities for Content Tracing and Generative AI An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 7

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source=pdf_text observed=2026-08-09T11:46:23.088415Z digest=sha256:b7d06bbde0f48c00b10765aadfbab227204435d71f73b45eeb85a61d52691cac

Observation b6fbdde1-239e-4e38-a130-071eb18838a0 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Watermarking across Modalities for Content Tracing and Generative AI Training Compute-Optimal Large Language Models

Reference 9

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source=pdf_text observed=2026-08-09T11:46:23.097403Z digest=sha256:f8bceaee13c3f2664b808fb0f0d3280f589f0b1cac358f41c702d281b670e699

Observation 6c31919c-bde5-486b-9e53-1caf687483e8 · outbound

This paper cites Mixtral of Experts.

Watermarking across Modalities for Content Tracing and Generative AI Mixtral of Experts

Reference 11

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source=pdf_text observed=2026-08-09T11:46:23.106421Z digest=sha256:7ce96cfb28eaee6e23d8d3981f83a8cfb7619a479c17d680a484af77bbac2a75

Observation 69cc228f-e7a1-4d2f-9750-ea66f8df7daf · outbound

This paper cites Watermark Stealing in Large Language Models.

Watermarking across Modalities for Content Tracing and Generative AI Watermark Stealing in Large Language Models

Reference 12

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source=pdf_text observed=2026-08-09T11:46:23.110929Z digest=sha256:bc1920a5ee3dcd2e8cca3793de56b3c34677b592229be84109bd8cce8eeaff4d

Observation 7b6c77fb-1536-4e81-8c84-d30d1c5cd30c · outbound

This paper cites Scaling Laws for Neural Language Models.

Watermarking across Modalities for Content Tracing and Generative AI Scaling Laws for Neural Language Models

Reference 13

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source=pdf_text observed=2026-08-09T11:46:23.115672Z digest=sha256:6bf5297b57b7c544ab1344164f5feeb9a6da4431ae521e3720ed0ef4de8e36df

Observation ea6fdf99-c86f-4979-9a02-e9f0147347e9 · outbound

This paper cites AudioGen: Textually Guided Audio Generation.

Watermarking across Modalities for Content Tracing and Generative AI AudioGen: Textually Guided Audio Generation

Reference 14

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source=pdf_text observed=2026-08-09T11:46:23.120195Z digest=sha256:74fec9d433ead51b1ccfabffe544d330d6e845b5a360ec6135af9c5d0d04fdcb

Observation 43324ef5-5fdc-4982-8528-a408de310342 · outbound

This paper cites Membership Inference on Word Embedding and Beyond.

Watermarking across Modalities for Content Tracing and Generative AI Membership Inference on Word Embedding and Beyond

Reference 16

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source=pdf_text observed=2026-08-09T11:46:23.129851Z digest=sha256:aa45db98049e523523136f74b1f69600b643a07863d57d1abb1376c17a808277

Observation 15bd14ff-4a5d-4766-8bd4-c86d4fecee78 · outbound

This paper cites The Llama 3 Herd of Models.

Watermarking across Modalities for Content Tracing and Generative AI The Llama 3 Herd of Models

Reference 17

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source=pdf_text observed=2026-08-09T11:46:23.134318Z digest=sha256:8450322edf9be5672b77702115875e2e1cc86c6c2a0c198da8dd4c1710bea7ac

Observation 80372540-ad80-455d-9481-446f38e02808 · outbound

This paper cites Null-text Inversion for Editing Real Images using Guided Diffusion Models.

Watermarking across Modalities for Content Tracing and Generative AI Null-text Inversion for Editing Real Images using Guided Diffusion Models

Reference 18

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source=pdf_text observed=2026-08-09T11:46:23.138935Z digest=sha256:d5609760a8847352dce11aab9d41cb9a4a2cf308bec8e9343d3f0f941319e2dc

Observation 91d4dc7d-9225-4bdb-823d-779aebc5b0d5 · outbound

This paper cites Robust image watermarking in the spatial domain.Signal processing, 1998.

Watermarking across Modalities for Content Tracing and Generative AI Robust image watermarking in the spatial domain.Signal processing, 1998

Reference 19

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source=pdf_text observed=2026-08-09T11:46:23.144239Z digest=sha256:54f6648b7ad885211cb44cae6963de559b5924f47064801737b2c572f46d0ffd

Observation f4c5e5fd-2ed4-40b3-99f2-8d84993ab1e1 · outbound

This paper cites MarkLLM: An Open-Source Toolkit for LLM Watermarking.

Watermarking across Modalities for Content Tracing and Generative AI MarkLLM: An Open-Source Toolkit for LLM Watermarking

Reference 20

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source=pdf_text observed=2026-08-09T11:46:23.148479Z digest=sha256:f67e4364304887f625e7dba0c0442da54b4fbf7ec8cb42afb22d83facc5acb66

Observation 24d63e47-4748-4b49-925a-c9b3616f7e47 · outbound

This paper cites Dct-based watermark recovering without resorting to the uncorrupted original image.

Watermarking across Modalities for Content Tracing and Generative AI Dct-based watermark recovering without resorting to the uncorrupted original image

Reference 21

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-09T11:46:23.152898Z digest=sha256:f9e881cdd061c5f9c5d2e1779b6cdbacba604664eee82a16a5603de60cf7fb09

Observation bdd8fde9-6f77-45a3-a1a8-a24357632ad0 · outbound

This paper cites Provably Robust Multi-bit Watermarking for AI-generated Text.

Watermarking across Modalities for Content Tracing and Generative AI Provably Robust Multi-bit Watermarking for AI-generated Text

Reference 22

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source=pdf_text observed=2026-08-09T11:46:23.157014Z digest=sha256:033b7d13e07dc9a8c31d0173730bfe906f4c10981b00f886b2a2a226e659f5e5

Observation e70fc1e1-303f-4a96-9b3f-2e8e6bb61cf4 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Watermarking across Modalities for Content Tracing and Generative AI Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 23

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source=pdf_text observed=2026-08-09T11:46:23.161628Z digest=sha256:fb33259224342c305e921fb8e08830ed23e853f19fe348f474d762b5ee3260a5

Observation a7a52313-6d62-4cc4-9c18-d1b341771f40 · outbound

This paper cites Neural Machine Translation of Rare Words with Subword Units.

Watermarking across Modalities for Content Tracing and Generative AI Neural Machine Translation of Rare Words with Subword Units

Reference 24

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source=pdf_text observed=2026-08-09T11:46:23.166231Z digest=sha256:f88dd4a8c1e197951d192860320112807e45bb810eb0c24143db77f896282e00

Observation 908b72a9-d1da-4e88-a172-bb08a85933b6 · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Watermarking across Modalities for Content Tracing and Generative AI RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 25

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source=pdf_text observed=2026-08-09T11:46:23.170935Z digest=sha256:42693a1f225c98ef9c1511fa958fb1905d2b05928f823e0543b03effd2767642

Observation e9e764aa-33fb-4f6c-99c5-9c761be60717 · outbound

This paper cites Snr-constrained heuristics for optimizing the scaling parameter of robust audio watermarking.IEEE Trans.

Watermarking across Modalities for Content Tracing and Generative AI Snr-constrained heuristics for optimizing the scaling parameter of robust audio watermarking.IEEE Trans

Reference 26

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source=pdf_text observed=2026-08-09T11:46:23.175048Z digest=sha256:cf617c77db1ce6f5cc87690e22b883eaac224242cef8c80892cae31c176c6670

Observation 85da5622-e2f4-48bc-8013-25534c4e67a7 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Watermarking across Modalities for Content Tracing and Generative AI LLaMA: Open and Efficient Foundation Language Models

Reference 27

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source=pdf_text observed=2026-08-09T11:46:23.179300Z digest=sha256:94fa8da4c592dc11aa565c762a2eb8c3efdac2bb07c6b34779d326bee10aa408

Observation 42745c84-4f9a-4081-98e2-6ba54818cf0f · outbound

This paper cites Lightfieldmessagingwithdeepphotographicsteganography.

Watermarking across Modalities for Content Tracing and Generative AI Lightfieldmessagingwithdeepphotographicsteganography

Reference 31

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source=pdf_text observed=2026-08-09T11:46:23.196985Z digest=sha256:602696da652c6b2b33e24f271cb191eedc12b7fc23230b0d36fe108e70c83161

Observation 8c89fa09-e44d-4aac-baef-3d9051e49870 · outbound

This paper cites Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance.

Watermarking across Modalities for Content Tracing and Generative AI Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance

Reference 32

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source=pdf_text observed=2026-08-09T11:46:23.201046Z digest=sha256:3fc98d453e75c88366c3759a18dfb85f41c0603a54854c07ab0df39b07dd3e3b

Observation 53944e93-4520-4cfd-9871-f1e153b6021a · outbound

This paper cites Vector-quantized Image Modeling with Improved VQGAN.

Watermarking across Modalities for Content Tracing and Generative AI Vector-quantized Image Modeling with Improved VQGAN

Reference 33

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source=pdf_text observed=2026-08-09T11:46:23.205545Z digest=sha256:d7f98ff297ee14068ee983d0b267ec608f95cf9b3839a4863f265d2f4ec48535

Observation 1d60a02f-a961-4453-a433-5e76ee8022b1 · outbound

This paper cites On the Importance of Difficulty Calibration in Membership Inference Attacks.

Watermarking across Modalities for Content Tracing and Generative AI On the Importance of Difficulty Calibration in Membership Inference Attacks

Reference 1993

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source=pdf_text observed=2026-08-09T11:46:23.192542Z digest=sha256:122dab99a58289146f7d5d2b5ceba5147668d6646ab27d0d210f4ffb434599d7

Observation 10688d71-1672-4556-8c46-c3c322f0810c · outbound

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

Watermarking across Modalities for Content Tracing and Generative AI HowkGPT: Investigating the Detection of ChatGPT-generated University Student Homework through Context-Aware Perplexity Analysis

Reference 1994

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source=pdf_text observed=2026-08-09T11:46:23.183741Z digest=sha256:96a27e00e557192394c01f074f9ba148ee823aaf8306c30021903282e7099b53

Observation 23c907a3-1547-40a4-a428-d01fa510d1ef · outbound

This paper cites SoundStorm: Efficient Parallel Audio Generation.

Watermarking across Modalities for Content Tracing and Generative AI SoundStorm: Efficient Parallel Audio Generation

Reference 1996

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source=pdf_text observed=2026-08-09T11:46:23.072517Z digest=sha256:8e6f1ececda4b3dd4fa013bd9b659528e32104fbb3970a4815b96fc95d28d6f1

Observation fdf8cc5d-6164-4670-9a03-168e14d9e85d · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Watermarking across Modalities for Content Tracing and Generative AI Measuring Massive Multitask Language Understanding

Reference 1997

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source=pdf_text observed=2026-08-09T11:46:23.093021Z digest=sha256:cc8dd3d8f4b9eca41b26489219658747d3dfea40d48a4152fbd6fc98ab4d6a4a

Observation 46aeac13-7e49-4029-add8-f691bb580e80 · outbound

This paper cites Efficient Image Generation with Variadic Attention Heads.

Watermarking across Modalities for Content Tracing and Generative AI Efficient Image Generation with Variadic Attention Heads

Reference 2012

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local_arxiv, observed 2026-08-09T11:46:23.299718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-09T11:46:23.187963Z digest=sha256:82df14c392c23961fa113427a8f366ff23aacfbf4bd7b2c91be8d225ac0c3b3f

Observation 7a3ed29e-7ebd-477d-bbc8-11f4ccf548e8 · outbound

This paper cites EAGLE: A Domain Generalization Framework for AI-generated Text Detection.

Watermarking across Modalities for Content Tracing and Generative AI EAGLE: A Domain Generalization Framework for AI-generated Text Detection

Reference 2013

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source=pdf_text observed=2026-08-09T11:46:23.067675Z digest=sha256:413a8611b81c78f6048c6942b76ab39f1f5a203ce168c0060c9486e7f5b9a2e3

Observation 354f022b-6d08-42c9-badf-07a5187e1013 · outbound

This paper cites Who Wrote this Code? Watermarking for Code Generation.

Watermarking across Modalities for Content Tracing and Generative AI Who Wrote this Code? Watermarking for Code Generation

Reference 2015

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source=pdf_text observed=2026-08-09T11:46:23.125023Z digest=sha256:434333904e77380b6d77ab85408bef35d2947df7e3ee1fcec08b840ca3e2bf9c

Observation afd6a256-8f37-482b-9e3a-fab4d8938168 · outbound

This paper cites Stable Signature is Unstable: Removing Image Watermark from Diffusion Models.

Watermarking across Modalities for Content Tracing and Generative AI Stable Signature is Unstable: Removing Image Watermark from Diffusion Models

Reference 2018

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source=pdf_text observed=2026-08-09T11:46:23.101769Z digest=sha256:c9b8adb380fb661608c56b6361ffed5e0d63d81e6bc3fd8d8b0c9765403a6349

Observation 6214a7f4-1f51-436b-a9e0-ccdf9edab361 · outbound

This paper cites WMAdapter: Adding WaterMark Control to Latent Diffusion Models.

Watermarking across Modalities for Content Tracing and Generative AI WMAdapter: Adding WaterMark Control to Latent Diffusion Models

Reference 2019

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source=pdf_text observed=2026-08-09T11:46:23.077744Z digest=sha256:8f0a7857f0f4acc85abf8f0a88de008e421b21b14992d5ba572375e792b4af9c

Observation 8a970900-4a3f-4af4-b1e8-96d6986b1fea · outbound

This paper cites The 2021 Image Similarity Dataset and Challenge.

Watermarking across Modalities for Content Tracing and Generative AI The 2021 Image Similarity Dataset and Challenge

Reference 2021

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source=pdf_text observed=2026-08-09T11:46:23.082966Z digest=sha256:59f037fcabfd4eedb51909ee83c4a8729455bfc8d9b8cfa4d21fdcffb60d8623

Observation 25f25f0e-8ac1-44d4-949e-6aa22a1d3f8c · outbound

This paper cites eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers.

Watermarking across Modalities for Content Tracing and Generative AI eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

Reference 2022

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source=pdf_text observed=2026-08-09T11:46:23.056519Z digest=sha256:eed814f6bbd6a18a85d351247c9cc3124750f44f39be70d5d8266a11f288f0cb

Observation 1ff48bd7-75c7-47e9-abd5-5dc7d3e3703c · outbound

This paper cites CompressAI: a PyTorch library and evaluation platform for end-to-end compression research.

Watermarking across Modalities for Content Tracing and Generative AI CompressAI: a PyTorch library and evaluation platform for end-to-end compression research

Reference 2023

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source=pdf_text observed=2026-08-09T11:46:23.062311Z digest=sha256:4a2e0ec41c2e4090ff4d175b724a35b19485d89b7657de9fcda19a3e79beacc3

Pith citing papers

No inbound Pith citation observations are available.