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Paper Citation Record · LEDGER

Fast Proxies for LLM Robustness Evaluation

As of 10 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 2026-08-09T06:31:02.800959+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:ead21f2e6f81c6a7ceadf9a4b1fb8224bc53973905fd2ae8032edac8ed8e0b6c

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:fa3d5eb558e87459516a3ea0d47714949b831de859c6e5bd8899f34042b0c5ba

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=sha256:0423e72f22e4bb9749140c696780c7d310fa8373278b3f5db3b1109beae55df2

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=sha256:41b100c9ab513107a902e8cd6d5124802ca363ba453e08763a53b63c884f660f

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=sha256:20f9d5b7ad846eeea8f587917467c12941c210a6a48e9ace1ba8d7ed4d4c68f8

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=sha256:17576cffd6f12e59f4db44a57ff3465d98a16056f63721c273dfff52ea24389a

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=sha256:65b54069cbc8b81e173565ea35a8d80587621b821b6314abf5ea2db3ff040df1

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:dfdfb328d66b76d41528341416c474673db89a6839e9c6cc2f8bff2b93b37136

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=sha256:3c80461e5fad23fa45672b53d48c2762841878523d940a0ba71eb70572a3e919

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=sha256:9c4e8fec6da7514d020c246edb694e048281a0a29ed653e4636b32744ecc4b1e

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:f52d15ac0da13581f9c9feff626f2b89ba5f876ca9276f8888de0dd704f0e9d9

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=sha256:78363de85022b84cfea2e19cf502474b1bd8fc35e0a43a075ef6f556094fc412

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=sha256:64f803d7dd79c5523c5a1ac2220bffebf399d0c236385cd626ceaaa60b34c0be

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=sha256:74e98b35c9279c6824e1ef372169b4893b7aab053e46b2dd23abb6a269ffc7f3

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=sha256:46dc83e3dd3b59e053bc1dedd28840c0be7cb0ee7e09489c1e269e33652de423

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=sha256:55c64d83c0e1103a4a67728bb52c5d74f522b10cfde916ab9c5bfaf7f89a9599

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=sha256:9fde4b9e8f22dee75d4290c9f14477e36143651733780c1074eff4479305a6f8

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=sha256:328ad8306f6ab1225042df08efe49adff8b6e19f3e19998b024cb7c861428fe0

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=sha256:5f7b9530946e655f242fd46482f9bfc81ff0488e080ff6072092753e2b4e5127

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=sha256:9491f643d10ae58f0d657c55cd6c3f52dfe45edce44e36b3993ae579a61f6c54

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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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T19:32:24.142978Z digest=sha256:aa8736041934b94d06c84912ba6fe74baabd0dc77b80dfbbb6bbabbc79bda086

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T19:32:24.146365Z digest=sha256:16432f08592cd54b9cbb127104967300f80e063e2a1be1cc56a9961fc1b186b3

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=sha256:10ef2ce536d5bfa265e29643d3f6f0d1e5df5c3d939b230768af49e086b38578

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:da55a366e55f3797bf36caad8842150764c7cfb4d13c336ee2912065a88b61c5

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=sha256:52f49a719e655965ade7b95f8f4d3a0fc388d137bfeb4d981b219217b1ff1827

Pith citing papers

No inbound Pith citation observations are available.