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Paper Citation Record · LEDGER

Fast Proxies for LLM Robustness Evaluation

As of 8 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 2026-08-08T06:32:00.761636+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=sha256:53f800d5efa4c9b95a5bd8a3228e86825cf6ddfac15f2b4fad110a2b2bf910b8

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=sha256:51a2f39409437ca516475066fc9f0112a2cdc3c6b2a968ae54622b87b4d2ebb6

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=sha256:0a0afff528216e9b32f449e169fe261c547c0889a0946d8e3906a90e1e097fea

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=sha256:099b7fb2c14122925312f79f37739a70e12c190ff6ad0cf9dd05a5121aa94326

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:a67441a4b9494b4b9169e7bb848bf4b71062e8105730a2fd94a630e7eb36e2bf

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=sha256:58bdaf7c6493105445e56eb9ff993675e138491de7318178c181b8a7511c723a

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:d2be93edda5e04e1ee9b788d324134f95d27aabf7fafcefeb0921f5c495a2d7e

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=sha256:6bf0b8924d8b665633c8b83e118e15c514aaae1aeca806f575f632b836be0fa0

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=sha256:005319e709a984ca5d77140561e69dd4e888c14859eea58b0df562164b2c2371

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:ebf20910dffbebf9da4bd3b17b5942ced0e99c1beb29aef4b658feeb4beaee38

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=sha256:229941c75cbdbd9bec87378f50a800bcd570190a0d77ed6ee577b60aa5aacfab

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:a5462860d4ad214285aed851bdf9c8e3ad6b2a3fa8aa542c7464ad747875d5c5

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:bde00315a75301d1d6ae70088c4a94604d6c549d5a462d6eb13f2b0c2b63b808

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:a0fea84d0c9a23c2cb208800017ded1e1aa30ef5b8173b0b7569f63591d0d3f2

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:ff655e5752321c43567aa94efebfaf32f40416e0efc8b38c68576af7ad7a839c

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=sha256:644e56641d5e4fda530dd7401512ea52515d94cd7bf734689dcca74f7966b933

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=sha256:82eea0f35ec4b4c8d0481d58fd87db94520bdc9a3df8316a7f36df47acacdb64

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:f21040b5c873e91002022422753e623d4a34813c05fe8050539f7526c9d2522e

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=sha256:436d45de18acf939286ef36eccf6e3372aeb56076a43a8e3802a9eb9da2e7c4d

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:af51253bf973153c2ef3c605d1157a7faa44ea7e92a5bde23b5fe57e395694ff

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=pdf_text observed=2026-08-07T19:32:24.142978Z digest=sha256:b6e9571ac83eab2d4e064cf729dfa22154e9b9fdd15409010a547f795bccc8d7

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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source=pdf_text observed=2026-08-07T19:32:24.146365Z digest=sha256:d1c21f64764482ddf49a85c2bfb3434f25987a3a99065d965006a95a56daa1a4

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:e7b7e860d7582487e123c97f73b1061f45aaae106af0b83963f7d95b89997aec

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=sha256:7936397a49428aaff375223dbf61c3e7ae93abfd9873aac192881db1dde779ae

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=sha256:642f22d8377d9b0e064b9773d6dbc9e7a1e85af5eaee94333ddfa05e26ddb48c

Pith citing papers

No inbound Pith citation observations are available.