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Paper Citation Record · LEDGER

Watermarking across Modalities for Content Tracing and Generative AI

As of 18 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 2026-08-18T06:34:40.430872+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

No source-named external measurement is stored.

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:ddeafee7f6cdcb60fdc41f3edbc052ccabd760e3fc4b26a5de210e331aa65a45

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=sha256:4ccd806488711b4f2561e25c2f635736a538fef2040d05106b854db9bca25503

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=sha256:12308146aadbf93f8c8cf7702262de4988d5c132713520be7a5f4f000ce9dd33

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=sha256:220226694de979a2ea55a2dbf05885a44591d579e64aaa011eb88b6be1acabca

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:cedb6c8d564fb41653d3e3ebfd09e9516872da30072c590c53a602e7deca5991

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=sha256:1cd04e3a06895b6c06301118b1ad4bf4cabd7bac72f7ba604a69d748e3033ed0

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:a7b7d3a0f9c4061e549ded168e9b146b8a883cbf51e13ac7e44877c7f1a1a8e3

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=sha256:0680f275f1afdd9fcd7ad85342925358eb2c2e55f421c26c3d1b120d2f3fcce8

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:bd0015442b71b99d0a4f6d56cfb45a83dd6f5cf92245c9befbc9793cf6aa94b3

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-reported events for the cited work

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source=pdf_text observed=2026-08-09T11:46:23.144239Z digest=sha256:8cb15e83534c2527ef66ce535a65fb00630fdcd659d304d7644389c0ba47f45f

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=sha256:16de32fb601ad11e43f0ace74317151285eb67762d14c96afc6ae5e14ab08423

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-09T11:46:23.152898Z digest=sha256:baf652c4126601e4aad96f6c75c53d17e5d42ce0f508bda55c938117ca8d9b96

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=sha256:85126d99701d64bb201d667a099328cebfa97464411ca628cc22f57aa8662bfe

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:aee6b725f864b937caeaa9630e433e10ca4e4d4abd565d9e3b05b7fb1d619ac0

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=sha256:22e9239d94218caf0f93656d88f79429bff4f7ebfd75e2f56909ee2f8d2a9e74

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:e81120eee9a2a1168906500dcf669f5937de51b640cc966b5e769272d601d7ea

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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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-09T11:46:23.175048Z digest=sha256:07e316a84c7807a11e5b20a83288ce12babac7e2b17a9171783c3f0d68e9f35f

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:dd2a820e40d5ff35f678e961c48fd2e3af89ccb6f34c83f59f2bde1e326924d2

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:ce8d0d4188fcd37123df9978241f5d5019bffe15f308a82800646a11f6a63c9d

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=sha256:2254e38d7521d6936ff9ebbfc9e90b3a5bd0d866160fbbd83c5f5230a3d90398

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:e2bfd1053cc9d4bbc172f1805989bb92bdcdbffb2c5c6156dda18684ac7ad5f6

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=sha256:3f0799462b32b5c08b8b36cd8b6c0eb909616634c146a37f54a6c8908ffbcf7e

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:d4297b06b4e9b8cae579be6d7a0c149722f8393e1f753e62f765327860431ae9

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:e524c5e7081515459bb6c249b58ac0a3fbe2f8df3342adc7ee9c10b3f626e7d5

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=sha256:3ae1d19cbbdf8824f632c50d1a09100efe1e78de1ceba82cc4c7cc49d6b34e38

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-09T11:46:23.187963Z digest=sha256:408ee0dc0a7fd186b4b353dbd36ed28e0adf50be17c0ed7b66694604eefefc93

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=sha256:00aab81246bb14d2fe662112d862df57b771e0bd925df1d390b332c467c2b428

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=sha256:8b98f41e28d2b6000ddb3ab409a6995108abd0add95c80c5a7e24e8aceed56a9

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:f38d6cdde9256e193ce779351cee192403e8696bd5b3a8ed97fade2b0d5d36f2

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=sha256:500353db684f88b85a0ca2939689080b81b03cfe0453c8946c373d65d8a5828c

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=sha256:2aea945d73fcd27d036ae71b4901feeb3da8f5f41f83050cdbdcedba31658c9e

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=sha256:3a821b0dc432f81ec4b040dbd399bf89c3ec66773fc578b741fa5a7727f1555b

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=sha256:5b5b9e2e42ed4d9f9ed63da360b65583af9051d5cd5a7f4882d1e80db44de8b8

Pith citing papers

No inbound Pith citation observations are available.