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Paper Citation Record · LEDGER

Adaptively Robust LLM Monitoring via Activation Watermarking

As of 8 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 2 inbound Pith citation observations for arXiv:2603.23171.

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

pith.paper-citation-record.v1
2603.23171 v3

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T17:39:44.013978Z

measured 27 of 27 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T05:57:19.399363Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T05:59:48.908825Z

Reference resolution

25 of 25 outbound references displayed

  • verified exact0
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 80302a1d-188c-4a7d-b4a5-ca56e33b47e1 · outbound

This paper cites GPT-4 Technical Report.

Adaptively Robust LLM Monitoring via Activation Watermarking GPT-4 Technical Report

Reference 1

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source=pdf_text observed=2026-08-02T17:39:41.154929Z digest=sha256:fbc560a1caf270bd784c313f749ff6cd9eb7ca539f07d3187144d87713586929

Observation 4d129cdd-2b53-43af-909f-e0d256dd8e59 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Adaptively Robust LLM Monitoring via Activation Watermarking Training Verifiers to Solve Math Word Problems

Reference 4

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source=pdf_text observed=2026-08-02T17:39:41.568606Z digest=sha256:e0d9c70410ad516f8b4fa26595d9d179c998d17440f9ad8ce923cb26accd104b

Observation 168ce311-8d49-4ef1-9c82-cc66d1aa94dc · outbound

This paper cites MirrorCheck: Efficient Adversarial Defense for Vision-Language Models.

Adaptively Robust LLM Monitoring via Activation Watermarking MirrorCheck: Efficient Adversarial Defense for Vision-Language Models

Reference 6

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source=pdf_text observed=2026-08-02T17:39:41.825588Z digest=sha256:5319be17993617c190ce19525bac23cbcd5a3d973d157dde967c8b9b52963281

Observation 2130c3f7-ec79-428c-a265-1d11bb15dfc9 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Adaptively Robust LLM Monitoring via Activation Watermarking Measuring Mathematical Problem Solving With the MATH Dataset

Reference 7

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source=pdf_text observed=2026-08-02T17:39:41.949795Z digest=sha256:cbe05efb0f3b32e36b81aa695deb79b31c77390c7850316f8d01b6ac53cdf6d7

Observation c1a959fb-3477-4dfc-a69c-75e4b35d5cf3 · outbound

This paper cites Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations.

Adaptively Robust LLM Monitoring via Activation Watermarking Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Reference 8

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source=pdf_text observed=2026-08-02T17:39:42.084298Z digest=sha256:ee2146668504471b7895bdc1bab1f6e0b8a6b50419781890c4acd9497fff05e6

Observation 2e4e9bf4-0386-432b-8f8c-e4ab437f8c22 · outbound

This paper cites HiddenDetect: Detecting Jailbreak Attacks against Large Vision-Language Models via Monitoring Hidden States.

Adaptively Robust LLM Monitoring via Activation Watermarking HiddenDetect: Detecting Jailbreak Attacks against Large Vision-Language Models via Monitoring Hidden States

Reference 10

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source=pdf_text observed=2026-08-02T17:39:42.343655Z digest=sha256:d6bab344ed3fec260f56dd498492ff711df2d8f014f89cf9ea7c9f18e2b95f3b

Observation 6aafcc01-92ce-4356-a137-8f3106c88bbb · outbound

This paper cites DeepInception: Hypnotize Large Language Model to Be Jailbreaker.

Adaptively Robust LLM Monitoring via Activation Watermarking DeepInception: Hypnotize Large Language Model to Be Jailbreaker

Reference 12

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source=pdf_text observed=2026-08-02T17:39:42.537821Z digest=sha256:6c5ef623df3daa42eb70f71910b8736cda2bd5d7324f67bbfcc9db8f8e607540

Observation cdd5f30b-c4b5-425a-ac88-4e8af75bfd13 · outbound

This paper cites Against The Achilles' Heel: A Survey on Red Teaming for Generative Models.

Adaptively Robust LLM Monitoring via Activation Watermarking Against The Achilles' Heel: A Survey on Red Teaming for Generative Models

Reference 13

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source=pdf_text observed=2026-08-02T17:39:42.714332Z digest=sha256:09f176559b2a7578df46bd9a762533d44b188e12aed54edde94510c8772d9a84

Observation 4bd3435b-6a83-4460-a7b4-e1128aa18ced · outbound

This paper cites AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models.

Adaptively Robust LLM Monitoring via Activation Watermarking AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models

Reference 14

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source=pdf_text observed=2026-08-02T17:39:42.780056Z digest=sha256:63bb8d82faef95a6608e9eb9f96db33fa7f246d24ffdaadd78478fae25c6de30

Observation 2cdf4dad-04a2-40a0-9eef-ad3ff45a8632 · outbound

This paper cites The Llama 3 Herd of Models.

Adaptively Robust LLM Monitoring via Activation Watermarking The Llama 3 Herd of Models

Reference 15

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source=pdf_text observed=2026-08-02T17:39:42.876621Z digest=sha256:c10bf21d3008fdac0278786bc8041b023e0f16c4c46554a1143030a472edbbd5

Observation 443d7cf7-31b2-4a71-9d84-65be7c98f303 · outbound

This paper cites XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models.

Adaptively Robust LLM Monitoring via Activation Watermarking XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models

Reference 16

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source=pdf_text observed=2026-08-02T17:39:43.019845Z digest=sha256:7ce10c27c5a10245bbbedadfcc41f42eff0beb34987ea2e0f2fc1fbc6a548965

Observation f7f84e7b-ee6e-4ae1-ad62-eb45eb2fefd5 · outbound

This paper cites Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming.

Adaptively Robust LLM Monitoring via Activation Watermarking Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming

Reference 17

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source=pdf_text observed=2026-08-02T17:39:43.163305Z digest=sha256:94e357931c3365c304c6d68f4e146ba93f05f2f5b38e220b95ffe84b5cf48467

Observation e8786eb1-1888-4eff-be47-7eddf6def933 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Adaptively Robust LLM Monitoring via Activation Watermarking Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 18

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source=pdf_text observed=2026-08-02T17:39:43.299576Z digest=sha256:34c0a80957394eb44ecbeb42240d46974d7cc5836eeef9b6d27770871dbe5e9e

Observation 80efb0bd-9825-4256-92e4-a6d350c0c80c · outbound

This paper cites MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.

Adaptively Robust LLM Monitoring via Activation Watermarking MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 19

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source=pdf_text observed=2026-08-02T17:39:43.379650Z digest=sha256:91d65287b0fedca3e6b7398c0ca558c3916dd1ad17a891be082433ceca3023a8

Observation 089d5599-51c5-4e85-863e-53799a233b26 · outbound

This paper cites Qwen3Guard Technical Report.

Adaptively Robust LLM Monitoring via Activation Watermarking Qwen3Guard Technical Report

Reference 20

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source=pdf_text observed=2026-08-02T17:39:43.471239Z digest=sha256:72d0ff418bd0c94bb4b299a652168c3837f3eab242a81c9c271653b07d7467e1

Observation 25cfcb93-92fa-4f6b-849e-c6e6a8d85b96 · outbound

This paper cites SoK: Watermarking for AI-Generated Content.

Adaptively Robust LLM Monitoring via Activation Watermarking SoK: Watermarking for AI-Generated Content

Reference 21

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source=pdf_text observed=2026-08-02T17:39:43.550309Z digest=sha256:1c37e9d810f5b01a92606e6d6edbf6fd5d69b551fcd9569f688b2bad03d05acb

Observation 9d3951d5-eaf9-4f24-ab2a-e4036b427202 · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

Adaptively Robust LLM Monitoring via Activation Watermarking Instruction-Following Evaluation for Large Language Models

Reference 22

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source=pdf_text observed=2026-08-02T17:39:43.658628Z digest=sha256:81c5f805668dbeb9eb6c25b88098095841f505eaea20e8e07e40fb70a70d9a47

Observation c1e5f8ce-f3d7-45ee-be63-6b742e14bc9d · outbound

This paper cites Representation Engineering: A Top-Down Approach to AI Transparency.

Adaptively Robust LLM Monitoring via Activation Watermarking Representation Engineering: A Top-Down Approach to AI Transparency

Reference 23

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source=pdf_text observed=2026-08-02T17:39:43.766293Z digest=sha256:db6cfa75b43be08323f5992e04613eb4818e83216bc1ed3cd3da2fe631002960

Observation b4f38a60-5ee7-47a6-a6f4-c4d7d2608618 · outbound

This paper cites an unresolved cited work.

Adaptively Robust LLM Monitoring via Activation Watermarking Unresolved cited work

Reference 24

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source=pdf_text observed=2026-08-02T17:39:43.884154Z digest=sha256:049be371e48ec805b7d20efaba49dcfd550df737914e3ec0c2885fc3ff3e42e8

Observation c05d3460-5916-4c79-a27e-666356ee0d6b · outbound

This paper cites The attacker then issues the translated prompt to the model and, for evaluation, translates the answer back into English.

Adaptively Robust LLM Monitoring via Activation Watermarking The attacker then issues the translated prompt to the model and, for evaluation, translates the answer back into English

Reference 25

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source=pdf_text observed=2026-08-02T17:39:44.013978Z digest=sha256:69e6c2149d41b4e82425b278520986b31fce02aa560ddcf35c82b707913f7f73

Observation 816375f6-b7aa-484b-9df8-095c7c70de76 · outbound

This paper cites Optimizing Adaptive Attacks against Watermarks for Language Models.

Adaptively Robust LLM Monitoring via Activation Watermarking Optimizing Adaptive Attacks against Watermarks for Language Models

Reference 2021

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source=pdf_text observed=2026-08-02T17:39:41.676660Z digest=sha256:3c7cbc737154584fa8b1578d67032e26449294156233d374b8ddff0dce7c56ff

Observation 24a9af66-5191-471a-93b5-341c475e8be1 · outbound

This paper cites Obfuscated Activations Bypass LLM Latent-Space Defenses.

Adaptively Robust LLM Monitoring via Activation Watermarking Obfuscated Activations Bypass LLM Latent-Space Defenses

Reference 2022

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source=pdf_text observed=2026-08-02T17:39:41.404497Z digest=sha256:4e11a3e6b656f826c9fe6bcb2f5afdcb3312c3cf39d67b3d5acf2b9c20022d2d

Observation 1da578b5-3c97-45b9-a11b-dcc30e0382f2 · outbound

This paper cites Beavertails: Towards improved safety alignment of llm via a human-preference dataset.arXiv preprint arXiv:2307.04657,.

Adaptively Robust LLM Monitoring via Activation Watermarking Beavertails: Towards improved safety alignment of llm via a human-preference dataset.arXiv preprint arXiv:2307.04657,

Reference 2023

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source=pdf_text observed=2026-08-02T17:39:42.200858Z digest=sha256:3f0e94c3c899f6b843a260b9e92ca6609e1e0a96a166b230e58bda50ff479fe7

Observation 9303b4d7-e24e-4ab1-9ff5-947e0386b362 · outbound

This paper cites Mitigating Watermark Forgery in Generative Models via Randomized Key Selection.

Adaptively Robust LLM Monitoring via Activation Watermarking Mitigating Watermark Forgery in Generative Models via Randomized Key Selection

Reference 2024

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source=pdf_text observed=2026-08-02T17:39:41.297826Z digest=sha256:f0cce5cb929c1870cf379ff8bdc6b95452bc8af944d22e65a7031ec1d6414cab

Observation 0a045218-3b2d-4b1f-a4e2-4c2ded9bac89 · outbound

This paper cites ISBN 9798400720406.

Adaptively Robust LLM Monitoring via Activation Watermarking ISBN 9798400720406

Reference 2025

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source=pdf_text observed=2026-08-02T17:39:42.434758Z digest=sha256:055fb736fc90b21c4408859eddb54ad893f100e518c14fc8bcbd7188200feca2

Pith citing papers

Observation a946a793-65b3-4915-9170-80454a373aa6 · inbound

Watermarking Should Be Treated as a Monitoring Primitive cites this paper.

Watermarking Should Be Treated as a Monitoring Primitive Adaptively Robust LLM Monitoring via Activation Watermarking

Reference 4

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arxiv_id, observed 2026-07-30T02:04:38.827270Z

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

source=pdf_text observed=2026-05-14T19:10:59.937855Z digest=sha256:e489dd8757ac104b6f06024406b2028db6144d71c3038cf8cd3bcd1dc30fb78c

Observation 54f1da3f-0259-4820-aa44-f130864553fe · inbound

Watermarking Should Be Treated as a Monitoring Primitive cites this paper.

Watermarking Should Be Treated as a Monitoring Primitive Adaptively Robust LLM Monitoring via Activation Watermarking

Reference 4

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arxiv_id, observed 2026-07-30T02:04:38.827270Z

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.

source=pdf_text observed=2026-05-15T05:57:19.399363Z digest=sha256:ded3ad9d13967b55e6fc3ef56fa508a594dd90a9fdf3cee1a6f5a7608772fbdb