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Paper Citation Record · LEDGER

Fast Proxies for LLM Robustness Evaluation

As of 18 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2502.10487.

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

pith.paper-citation-record.v1
2502.10487 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:32:24.149582Z

measured 25 of 25 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

25 of 25 outbound references displayed

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External citation measurements

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Outbound references

Observation a52b6e34-9cab-4c84-9a96-34083bd83ec2 · outbound

This paper cites Phi-4 Technical Report.

Fast Proxies for LLM Robustness Evaluation Phi-4 Technical Report

Reference 1

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source=pdf_text observed=2026-08-07T19:32:24.021078Z digest=sha256:3a070fcce0148c7f60a124b0b0df86d9eacd52b0603ab34e2b9b9bb8f45b6b6c

Observation 3d1ae997-d9f3-4cb6-ad23-14bd87d03dec · outbound

This paper cites Qwen Technical Report.

Fast Proxies for LLM Robustness Evaluation Qwen Technical Report

Reference 3

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source=pdf_text observed=2026-08-07T19:32:24.030248Z digest=sha256:21fb45c95959485e93ebb292feee7ab777f5372eb07c4332d4081fb96b81e7ab

Observation f60c0e12-aceb-4feb-8476-77092bd92f39 · outbound

This paper cites The Llama 3 Herd of Models.

Fast Proxies for LLM Robustness Evaluation The Llama 3 Herd of Models

Reference 5

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source=pdf_text observed=2026-08-07T19:32:24.037279Z digest=sha256:4cbc8361f50817c22e2554f0fdb4b142a9bed423d23244132acdf5eb68a84cb8

Observation e24461f8-db07-468b-a3d3-54643b315dea · outbound

This paper cites Mistral 7B.

Fast Proxies for LLM Robustness Evaluation Mistral 7B

Reference 6

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source=pdf_text observed=2026-08-07T19:32:24.041487Z digest=sha256:bf9aa08c99280f57f8cabdecc4164d3e870202c53d336b87a7283fbbb8fd8f75

Observation c9d9c45c-9194-4078-a496-571e02d37ab6 · outbound

This paper cites LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet.

Fast Proxies for LLM Robustness Evaluation LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet

Reference 7

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source=pdf_text observed=2026-08-07T19:32:24.045500Z digest=sha256:bde62baed6ffc9bb423c5bbea11f4800bfca772d1fd607ab6a484514e0174188

Observation feb381d0-e013-4b82-8a6d-8a4e6a39f74b · outbound

This paper cites AmpleGCG: Learning a Universal and Transferable Generative Model of Adversarial Suffixes for Jailbreaking Both Open and Closed LLMs.

Fast Proxies for LLM Robustness Evaluation AmpleGCG: Learning a Universal and Transferable Generative Model of Adversarial Suffixes for Jailbreaking Both Open and Closed LLMs

Reference 8

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source=pdf_text observed=2026-08-07T19:32:24.048917Z digest=sha256:e55fca7b8e18d74b0b3311b93792d7e103689bfa3b4d2511ce722df17f89dda7

Observation 27897394-233f-4ca9-85d8-19ce25af9711 · outbound

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

Fast Proxies for LLM Robustness Evaluation AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models

Reference 9

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source=pdf_text observed=2026-08-07T19:32:24.092803Z digest=sha256:911f8764c044faf8766721d585815ab0bc4ca1be11a7195fe5b49117a0e4a029

Observation d33b781c-606c-4524-b38f-1ce9ab2f1526 · outbound

This paper cites HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal.

Fast Proxies for LLM Robustness Evaluation HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal

Reference 10

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source=pdf_text observed=2026-08-07T19:32:24.096447Z digest=sha256:54738557b9d18388ed26dd24e6d85f8b18f9437990a41ae0597b832f51ff813f

Observation fc2f63f1-eaeb-460b-8ca7-d6a49861fdf3 · outbound

This paper cites Fast Adversarial Attacks on Language Models In One GPU Minute.

Fast Proxies for LLM Robustness Evaluation Fast Adversarial Attacks on Language Models In One GPU Minute

Reference 11

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source=pdf_text observed=2026-08-07T19:32:24.099738Z digest=sha256:ac287ab2cf0ea987b6147a9aee418434b85582fa9fbd66ef288a26e7f0ae7dff

Observation 598f292b-bfcb-41c2-b320-390c0e68616d · outbound

This paper cites Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts.

Fast Proxies for LLM Robustness Evaluation Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts

Reference 12

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source=pdf_text observed=2026-08-07T19:32:24.103244Z digest=sha256:7cdf4bd1523a1c20e489e9b64dfb255071422899318b7e5a0e82de8813987632

Observation 5b879a50-5670-4f39-bab6-18f92f6b5a20 · outbound

This paper cites Adversarial Attacks and Defenses in Large Language Models: Old and New Threats.

Fast Proxies for LLM Robustness Evaluation Adversarial Attacks and Defenses in Large Language Models: Old and New Threats

Reference 13

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source=pdf_text observed=2026-08-07T19:32:24.106441Z digest=sha256:8ae68a2ebdb6831e4f10a6798c9bc49214c64c1d119fd6b8ac28151a9c9b3538

Observation fc9c4729-bf71-48df-b815-87370bc68e8b · outbound

This paper cites Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space.

Fast Proxies for LLM Robustness Evaluation Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space

Reference 14

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source=pdf_text observed=2026-08-07T19:32:24.110160Z digest=sha256:8984575ba3d000c346b580a8445cd4b63101fd2e26504bf81df1e335344e1f97

Observation a00685c8-17d2-4db3-a754-c3591a9824e6 · outbound

This paper cites Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs.

Fast Proxies for LLM Robustness Evaluation Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs

Reference 15

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source=pdf_text observed=2026-08-07T19:32:24.113138Z digest=sha256:7fb2dc2659c7ba14b0e3ff9cb1a7ff2d44579684493d3b4a59078a66006f528d

Observation a1c8e577-5f46-40c3-813d-6e8fb19398ba · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Fast Proxies for LLM Robustness Evaluation Gemma 2: Improving Open Language Models at a Practical Size

Reference 16

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source=pdf_text observed=2026-08-07T19:32:24.116471Z digest=sha256:2b20e99c2ef8f7862562b4cf273bea03ed86e849f26b6cfa65979f0b0c672cd0

Observation cc48bd29-eb95-488a-b3c8-7e52a14dd6e6 · outbound

This paper cites Hermes 3 Technical Report.

Fast Proxies for LLM Robustness Evaluation Hermes 3 Technical Report

Reference 17

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source=pdf_text observed=2026-08-07T19:32:24.119477Z digest=sha256:ed5139e9a5a0c633e491eb1187023788fb969c716487b94a4e0ed29c1776f6c4

Observation a7bf6e61-cf7a-44ff-9b5b-dc2ad610cfe7 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Fast Proxies for LLM Robustness Evaluation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 18

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source=pdf_text observed=2026-08-07T19:32:24.122684Z digest=sha256:1df4832278799a1177d8ea880368dde2cc7c68469632ec6161c7d5d3bd8ce287

Observation 075bd7b0-4836-41d1-9708-80cf6a8e12ca · outbound

This paper cites Zephyr: Direct Distillation of LM Alignment.

Fast Proxies for LLM Robustness Evaluation Zephyr: Direct Distillation of LM Alignment

Reference 19

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source=pdf_text observed=2026-08-07T19:32:24.126931Z digest=sha256:d447274d001424f2025245c830ecfe4bf334b8352889969e08c0ab3dd649185c

Observation c2ac0286-28ca-49a7-a4d0-56b6ededdb7f · outbound

This paper cites Bypassing the Safety Training of Open-Source LLMs with Priming Attacks.

Fast Proxies for LLM Robustness Evaluation Bypassing the Safety Training of Open-Source LLMs with Priming Attacks

Reference 20

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source=pdf_text observed=2026-08-07T19:32:24.131114Z digest=sha256:ee6b7d819b2e5d1e36ef713bc18b47d17be8e6e91391ab70b7cab4cee604e73f

Observation 2f10703b-0917-4198-963a-5a4b71051455 · outbound

This paper cites Efficient Adversarial Training in LLMs with Continuous Attacks.

Fast Proxies for LLM Robustness Evaluation Efficient Adversarial Training in LLMs with Continuous Attacks

Reference 21

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source=pdf_text observed=2026-08-07T19:32:24.135448Z digest=sha256:809d1a20c9689ab23a9d201d08f2882ab7e3be184cff7d0d7d8b73cf2ceca40c

Observation e7c891ac-d14e-4f5e-b3d4-dd6310e1dada · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

Fast Proxies for LLM Robustness Evaluation Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 22

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source=pdf_text observed=2026-08-07T19:32:24.139663Z digest=sha256:8d18d050d501c94cf068fab94460d4ada4e5f2079c655b9b5dacb7f364ec3573

Observation d0aca436-8f82-40bf-b540-7853eb4be69e · outbound

This paper cites We use bfloat16 quantization for all models.

Fast Proxies for LLM Robustness Evaluation We use bfloat16 quantization for all models

Reference 23

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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=2026-08-07T19:32:24.142978Z digest=sha256:5febffd9fcc326757f16088e20d78b74088092a5e9fb7f65513dbf8b5813bd67

Observation cef188b4-8d56-413f-bc37-3f598af7432f · outbound

This paper cites 250, use a batch size of 512, Top-K “ 256, and initialize using the string “x x x x x x x x x x x x x x x x x x x x.

Fast Proxies for LLM Robustness Evaluation 250, use a batch size of 512, Top-K “ 256, and initialize using the string “x x x x x x x x x x x x x x x x x x x x

Reference 24

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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=2026-08-07T19:32:24.146365Z digest=sha256:615a729dc385c182f5cc19477fe18670b1129a4e6e791ca354df30a7cfb16e5c

Observation 294d91a6-966b-4e0b-8fed-b97afafd9f85 · outbound

This paper cites x x x x x x x x x x x x x x x x x x x x.

Fast Proxies for LLM Robustness Evaluation x x x x x x x x x x x x x x x x x x x x

Reference 25

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source=pdf_text observed=2026-08-07T19:32:24.149582Z digest=sha256:48be2f7e429a562464c07e44ddd9b15e297561f59d630bfb14cd254a66e6e74c

Observation af7ce8b5-a93b-483c-8906-abb739783706 · outbound

This paper cites Jailbreaking Black Box Large Language Models in Twenty Queries.

Fast Proxies for LLM Robustness Evaluation Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 2023

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source=pdf_text observed=2026-08-07T19:32:24.033840Z digest=sha256:9b2f4bc77f6095fefad88a45283d8a0669955b1a062e533f0b42181f1ce68a84

Observation 31201e82-4e66-438f-9316-cc01e2e0ec73 · outbound

This paper cites Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks.

Fast Proxies for LLM Robustness Evaluation Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks

Reference 2024

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source=pdf_text observed=2026-08-07T19:32:24.025979Z digest=sha256:291a70037a991895031da301c5f2e949f12647a782954a2c40ffb26b66f59395

Pith citing papers

No inbound Pith citation observations are available.