Paper Citation Record · LEDGER

Invariant-based Robust Weights Watermark for Large Language Models

As of 8 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2507.08288.

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

pith.paper-citation-record.v1
2507.08288 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:33:08.036669Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-18T22:45:31.935618Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T22:46:53.263952Z

Reference resolution

53 of 53 outbound references displayed

verified exact2
verified fuzzy30
unresolved21
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d96db4b8-68c0-456d-b9cb-208acf25646f · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Invariant-based Robust Weights Watermark for Large Language Models Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:12.004884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d2380525-37ee-4c7c-a0fd-b09147a5dfe2 · outbound

This paper cites GPT-4 Technical Report.

Invariant-based Robust Weights Watermark for Large Language Models GPT-4 Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:03.845841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8abca265-a20a-4417-8a42-dd1b335eaa93 · outbound

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

Invariant-based Robust Weights Watermark for Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:03.937145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcb62fa2-91c4-4023-8237-fc82f1b80ebb · outbound

This paper cites Natural language processing: an introduction.

Invariant-based Robust Weights Watermark for Large Language Models Natural language processing: an introduction

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:11.741694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 569e6b5a-597a-4759-a774-286c8ad64384 · outbound

This paper cites No Language Left Behind: Scaling Human-Centered Machine Translation.

Invariant-based Robust Weights Watermark for Large Language Models No Language Left Behind: Scaling Human-Centered Machine Translation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:04.184484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 116a8329-64a5-4a66-a869-594924b913b2 · outbound

This paper cites How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation.

Invariant-based Robust Weights Watermark for Large Language Models How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:04.299795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2aaa9d3e-3043-439e-a448-8cf925d58d1f · outbound

This paper cites Lever: Learning to verify language-to-code generation with execution.

Invariant-based Robust Weights Watermark for Large Language Models Lever: Learning to verify language-to-code generation with execution

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:11.511428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 84bcabcd-5e41-48e8-9870-21cf6b29a090 · outbound

This paper cites Expectation vs.

Invariant-based Robust Weights Watermark for Large Language Models Expectation vs

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:11.401011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cb1f3bda-1651-4edb-8891-28548da43c6c · outbound

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

Invariant-based Robust Weights Watermark for Large Language Models Can LLM-Generated Misinformation Be Detected?

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:04.694542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa60ef29-9cca-4477-b738-5bef021f2323 · outbound

This paper cites Red Teaming Language Models with Language Models.

Invariant-based Robust Weights Watermark for Large Language Models Red Teaming Language Models with Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:04.807252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b5b42e2-6c70-44a8-ae77-e5a4256d748f · outbound

This paper cites A watermark for large language models.

Invariant-based Robust Weights Watermark for Large Language Models A watermark for large language models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:11.310545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ef6a9ef8-08e9-4381-bcb4-c7d3561c011b · outbound

This paper cites Adaptive Text Watermark for Large Language Models.

Invariant-based Robust Weights Watermark for Large Language Models Adaptive Text Watermark for Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:05.014843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b312db7-dfcc-4acb-9b47-03da6806b38c · outbound

This paper cites ModelShield: Adaptive and Robust Watermark against Model Extraction Attack.

Invariant-based Robust Weights Watermark for Large Language Models ModelShield: Adaptive and Robust Watermark against Model Extraction Attack

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:05.099947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3f7a0f1-d09b-4b12-ae53-4ea515d4f8a1 · outbound

This paper cites Watermarking Pre-trained Language Models with Backdooring.

Invariant-based Robust Weights Watermark for Large Language Models Watermarking Pre-trained Language Models with Backdooring

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:05.191485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a9c98a1-bb6f-4253-b1a6-742bdb88d2e9 · outbound

This paper cites Specmark: A spectral watermarking framework for ip protection of speech recognition systems.

Invariant-based Robust Weights Watermark for Large Language Models Specmark: A spectral watermarking framework for ip protection of speech recognition systems

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:11.226665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6f704bfb-d9d1-4bef-8c40-3c72a0ab482e · outbound

This paper cites Embedding watermarks into deep neural networks.

Invariant-based Robust Weights Watermark for Large Language Models Embedding watermarks into deep neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:11.157367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ff85569d-3991-483f-9e8d-ca9ad10cc1fd · outbound

This paper cites Deepsigns: An end-to-end watermarking framework for ownership protection of deep neural networks.

Invariant-based Robust Weights Watermark for Large Language Models Deepsigns: An end-to-end watermarking framework for ownership protection of deep neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:11.057964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 10c5e01d-98bb-41b1-a955-241319faae6c · outbound

This paper cites Deepmarks: A secure fingerprinting framework for digital rights management of deep learning models.

Invariant-based Robust Weights Watermark for Large Language Models Deepmarks: A secure fingerprinting framework for digital rights management of deep learning models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:10.969388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c5c1cf3a-ff97-474a-9f0c-af00b07888f5 · outbound

This paper cites Efficient decentralized tracing protocol for fingerprinting system with index table.

Invariant-based Robust Weights Watermark for Large Language Models Efficient decentralized tracing protocol for fingerprinting system with index table

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:10.854567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e3577c99-03d3-43d1-9f6e-17c1164f721f · outbound

This paper cites Watermarking neural network with compensation mechanism.

Invariant-based Robust Weights Watermark for Large Language Models Watermarking neural network with compensation mechanism

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:10.747920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9d0483b0-0f20-47d2-89fa-bc2672993dd6 · outbound

This paper cites EmMark: Robust Watermarks for IP Protection of Embedded Quantized Large Language Models.

Invariant-based Robust Weights Watermark for Large Language Models EmMark: Robust Watermarks for IP Protection of Embedded Quantized Large Language Models

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:33:08.369837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 75ffaa54-2c85-4adc-bea9-93f5c86d55c5 · outbound

This paper cites Rethinking \ White-Box \ watermarks on deep learning models under neural structural obfuscation.

Invariant-based Robust Weights Watermark for Large Language Models Rethinking \ White-Box \ watermarks on deep learning models under neural structural obfuscation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:10.648785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9050d485-f7f9-4d9b-b60b-cb7124b6a2dc · outbound

This paper cites Cracking white-box dnn watermarks via invariant neuron transforms.

Invariant-based Robust Weights Watermark for Large Language Models Cracking white-box dnn watermarks via invariant neuron transforms

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:10.556835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ee405dac-0303-4b10-8023-9b92ab7da91c · outbound

This paper cites Functional invariants to watermark large transformers.

Invariant-based Robust Weights Watermark for Large Language Models Functional invariants to watermark large transformers

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:10.454114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d52679fc-667b-4e4f-9b17-6468072c8418 · outbound

This paper cites AquaLoRA: Toward White-box Protection for Customized Stable Diffusion Models via Watermark LoRA.

Invariant-based Robust Weights Watermark for Large Language Models AquaLoRA: Toward White-box Protection for Customized Stable Diffusion Models via Watermark LoRA

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:06.059175Z

Source-reported events for the cited work

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Observation 1af78fe5-a288-4ee2-8080-324512a924c4 · outbound

This paper cites Review of watermarking for deep neural networks.

Invariant-based Robust Weights Watermark for Large Language Models Review of watermarking for deep neural networks

Reference 26

Resolution: verified exact; doi, observed 2026-08-06T18:33:08.161530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4f52d689-b0fa-4cdc-a8c8-8a9c6a5e1cd3 · outbound

This paper cites Collusion-resistant multimedia fingerprinting: a unified framework.

Invariant-based Robust Weights Watermark for Large Language Models Collusion-resistant multimedia fingerprinting: a unified framework

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:10.349838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation efede49d-14c6-46b7-bafe-67d63cb70fe7 · outbound

This paper cites Turning your weakness into a strength: Watermarking deep neural networks by backdooring.

Invariant-based Robust Weights Watermark for Large Language Models Turning your weakness into a strength: Watermarking deep neural networks by backdooring

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:10.227386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 867949f8-fca5-4d85-ad3b-b1620a461aa8 · outbound

This paper cites Sok: How robust is image classification deep neural network watermarking? In 2022 IEEE Symposium on Security and Privacy (SP), pages 787--804.

Invariant-based Robust Weights Watermark for Large Language Models Sok: How robust is image classification deep neural network watermarking? In 2022 IEEE Symposium on Security and Privacy (SP), pages 787--804

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:10.088865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c45cf22c-1b8e-404e-8d82-3436ecff5739 · outbound

This paper cites A note on the limits of collusion-resistant watermarks.

Invariant-based Robust Weights Watermark for Large Language Models A note on the limits of collusion-resistant watermarks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:09.972477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2f12ffb1-24fa-4836-aaee-839f5e7facf7 · outbound

This paper cites Resistance of digital watermarks to collusive attacks.

Invariant-based Robust Weights Watermark for Large Language Models Resistance of digital watermarks to collusive attacks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:09.806363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 428fbbad-7f36-4880-8cc3-a903c188b9d3 · outbound

This paper cites A secure, robust watermark for multimedia.

Invariant-based Robust Weights Watermark for Large Language Models A secure, robust watermark for multimedia

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:09.601559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 61428237-b2c1-48e1-8019-ef7a050ebc1f · outbound

This paper cites Watermarking deep neural networks with greedy residuals.

Invariant-based Robust Weights Watermark for Large Language Models Watermarking deep neural networks with greedy residuals

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:09.509681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 57d0d9bd-b7ba-478b-acf8-905e4105feec · outbound

This paper cites Find the lady: Permutation and re-synchronization of deep neural networks.

Invariant-based Robust Weights Watermark for Large Language Models Find the lady: Permutation and re-synchronization of deep neural networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:09.401109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d57e63bd-1dd2-4a82-aebf-3608176f7a85 · outbound

This paper cites Attacks on digital watermarks for deep neural networks.

Invariant-based Robust Weights Watermark for Large Language Models Attacks on digital watermarks for deep neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:09.290814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d1a92eee-6eb2-4840-add9-4858ce1aae52 · outbound

This paper cites Fixed point quantization of deep convolutional networks.

Invariant-based Robust Weights Watermark for Large Language Models Fixed point quantization of deep convolutional networks

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:09.198649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 172950be-de24-480d-8364-b6e49874a56c · outbound

This paper cites Opencompass: A universal evaluation platform for foundation models.

Invariant-based Robust Weights Watermark for Large Language Models Opencompass: A universal evaluation platform for foundation models

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:09.102095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7d4b040e-d125-422a-b74c-f6cf46871024 · outbound

This paper cites C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models.

Invariant-based Robust Weights Watermark for Large Language Models C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:09.025662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cc1e5397-3378-442d-a202-3248be4d618a · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Invariant-based Robust Weights Watermark for Large Language Models Measuring Massive Multitask Language Understanding

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:07.326775Z

Source-reported events for the cited work

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Observation e906107e-6643-443f-8b9f-a0de4b897bd6 · outbound

This paper cites WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations.

Invariant-based Robust Weights Watermark for Large Language Models WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:07.388864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 701b56aa-4424-48b5-a760-55ee8f51694e · outbound

This paper cites The winograd schema challenge.

Invariant-based Robust Weights Watermark for Large Language Models The winograd schema challenge

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:07.417484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 059c1eb2-5a58-4d2a-906f-7468a16d062f · outbound

This paper cites Choice of plausible alternatives: An evaluation of commonsense causal reasoning.

Invariant-based Robust Weights Watermark for Large Language Models Choice of plausible alternatives: An evaluation of commonsense causal reasoning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:08.937046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7c11addd-6142-4905-9412-6714914f4a19 · outbound

This paper cites The commitmentbank: Investigating projection in naturally occurring discourse.

Invariant-based Robust Weights Watermark for Large Language Models The commitmentbank: Investigating projection in naturally occurring discourse

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:07.590471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d69234b-71f9-4db2-9a88-c2a23f244194 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Invariant-based Robust Weights Watermark for Large Language Models BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:07.646492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3669098-fc82-446d-a96a-1db97230a541 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

Invariant-based Robust Weights Watermark for Large Language Models Piqa: Reasoning about physical commonsense in natural language

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:07.678069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1de5e847-0976-4429-80a3-585db9d852f6 · outbound

This paper cites Looking beyond the surface: A challenge set for reading comprehension over multiple sentences.

Invariant-based Robust Weights Watermark for Large Language Models Looking beyond the surface: A challenge set for reading comprehension over multiple sentences

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:07.721994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cd7fa41-645d-408a-a98c-0e226c03a0d2 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Invariant-based Robust Weights Watermark for Large Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:07.757395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b4dae49-bb35-45c4-96a7-203ffd77a583 · outbound

This paper cites Gpt-j-6b: A 6 billion parameter autoregressive language model, 2021.

Invariant-based Robust Weights Watermark for Large Language Models Gpt-j-6b: A 6 billion parameter autoregressive language model, 2021

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:08.815875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 13aa0a96-9d13-4519-9aa0-a62bf201551f · outbound

This paper cites A framework for few-shot language model evaluation.

Invariant-based Robust Weights Watermark for Large Language Models A framework for few-shot language model evaluation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:08.720137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c9492d50-6ce5-4b04-bdcf-c9d856e340f1 · outbound

This paper cites Chain of Hindsight Aligns Language Models with Feedback.

Invariant-based Robust Weights Watermark for Large Language Models Chain of Hindsight Aligns Language Models with Feedback

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:07.896741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa1a2249-1454-4172-83e7-923fe1354b8c · outbound

This paper cites Blossom math v2 dataset, 2023.

Invariant-based Robust Weights Watermark for Large Language Models Blossom math v2 dataset, 2023

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:33:08.619505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 37982a25-2f37-4d72-8a26-d23115aa7d44 · outbound

This paper cites Stanford alpaca: An instruction-following llama model, 2023.

Invariant-based Robust Weights Watermark for Large Language Models Stanford alpaca: An instruction-following llama model, 2023

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:07.978928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28b0cdda-708a-4017-8a18-8cf560447ce3 · outbound

This paper cites ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models.

Invariant-based Robust Weights Watermark for Large Language Models ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:08.036669Z

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Pith citing papers

Observation 916aed3f-bbdd-41af-96ba-e06914869d41 · inbound

Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends cites this paper.

Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends Invariant-based Robust Weights Watermark for Large Language Models

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-18T22:46:53.266175Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.