Paper Citation Record · LEDGER

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models

As of 15 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2412.19603.

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

pith.paper-citation-record.v1
2412.19603 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:19:17.064272Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

22 of 22 outbound references displayed

verified exact0
verified fuzzy19
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a4cbdced-1c12-4c07-9204-d460837003b8 · outbound

This paper cites Simons institute talk on watermarking of large language models, 2023.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Simons institute talk on watermarking of large language models, 2023

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.405338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ef0fbac5-4e4f-40e8-bd2d-2b2b646228ee · outbound

This paper cites Fast- detectGPT: Efficient zero-shot detection of machine-generated text via condi- tional probability curvature.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Fast- detectGPT: Efficient zero-shot detection of machine-generated text via condi- tional probability curvature

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.391678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 59ee1cf7-9a3c-4963-92bd-b1bb7036c42e · outbound

This paper cites Pseudorandom error-correcting codes.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Pseudorandom error-correcting codes

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.378387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 25ef6449-085b-4346-9635-9eb264fce059 · outbound

This paper cites Undetectable watermarks for language models.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Undetectable watermarks for language models

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.364503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a68bc486-9572-4f27-8f28-602bcf6686f2 · outbound

This paper cites Watermarking language models for many adaptive users.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Watermarking language models for many adaptive users

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.349780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 39c363c1-583e-49a4-9064-d41eb8838fd4 · outbound

This paper cites Publicly-detectable watermarking for language models, 2024.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Publicly-detectable watermarking for language models, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.334920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2cd2659f-bbd7-4576-823d-a4696f68f7ab · outbound

This paper cites Edit distance robust watermarks for language models, 2024.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Edit distance robust watermarks for language models, 2024

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.318665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fa395aa3-f67d-4055-9122-3de5677f0296 · outbound

This paper cites Spotting LLMs with binoculars: Zero-shot detection of machine-generated text.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Spotting LLMs with binoculars: Zero-shot detection of machine-generated text

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.301796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 81108b8b-476c-4310-be13-073e57f1e089 · outbound

This paper cites Carnegie Mellon University, 2004.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Carnegie Mellon University, 2004

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.286616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ddd212b7-c9ec-4f1e-9387-144beb3ab346 · outbound

This paper cites Radar: Robust ai-text detection via adversarial learning.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Radar: Robust ai-text detection via adversarial learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.271574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7660a811-9c16-47ce-89b0-97fdfe09c132 · outbound

This paper cites Categorical reparametrization with gumble-softmax.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Categorical reparametrization with gumble-softmax

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.256174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 238576b8-5911-459d-9d3f-050098df9cb0 · outbound

This paper cites Jois, Matthew Green, and Aviel D.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Jois, Matthew Green, and Aviel D

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.240256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7f05222c-2417-47b9-bba9-1002a784337a · outbound

This paper cites A watermark for large language models.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models A watermark for large language models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T00:19:17.028973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f1d0963-d51e-41da-979a-ec7d008f548d · outbound

This paper cites On the reliability of watermarks for large language models, 2024.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models On the reliability of watermarks for large language models, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.215841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d727b365-fa9c-4241-a9de-c4be83daba07 · outbound

This paper cites Ro- bust distortion-free watermarks for language models.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Ro- bust distortion-free watermarks for language models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.201245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1fb6c51f-fb16-487e-8f72-b96c3244d254 · outbound

This paper cites an unresolved cited work.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-11T00:19:17.185990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 266b8c5c-1d74-4eb8-b196-4f5b1f4c7431 · outbound

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

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Detectgpt: Zero-shot machine-generated text detection using probability curvature

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.171047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 625cb5ed-8891-4505-8cad-67313168d765 · outbound

This paper cites Provably robust multi-bit watermarking for ai-generated text, 2024.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Provably robust multi-bit watermarking for ai-generated text, 2024

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.156769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3072a92b-2e24-4476-aa43-b6eccb7246be · outbound

This paper cites SeqXGPT: Sentence-level AI-generated text detection.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models SeqXGPT: Sentence-level AI-generated text detection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.142547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 681f9557-587d-43b9-9ed3-9f405c0f8591 · outbound

This paper cites Zero-shot detection of machine-generated codes, 2023.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Zero-shot detection of machine-generated codes, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.127854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a389ca05-b126-47cf-ac8d-7b313005de1f · outbound

This paper cites Excuse me, sir? Your language model is leaking (information).

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Excuse me, sir? Your language model is leaking (information)

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T00:19:17.060367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 729ee12b-8585-4b95-a176-cff60c0ada73 · outbound

This paper cites Prov- able robust watermarking for AI-generated text.

Let Watermarks Speak: A Robust and Unforgeable Watermark for Language Models Prov- able robust watermarking for AI-generated text

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:19:17.113478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.