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Paper Citation Record · LEDGER

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent

As of 17 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2505.09820.

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

pith.paper-citation-record.v1
2505.09820 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:29:16.565174Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

53 of 53 outbound references displayed

  • verified exact0
  • verified fuzzy12
  • unresolved39
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

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Outbound references

Observation ecfac2b9-d001-430c-80aa-1f47eb2c5117 · outbound

This paper cites Unified Pre-training for Program Understanding and Generation.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Unified Pre-training for Program Understanding and Generation

Reference 1

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Observation 224d9eff-28a6-4853-9cdb-f37c0d937cca · outbound

This paper cites Language mod- els are few-shot learners,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Language mod- els are few-shot learners,

Reference 2

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source=pdf_text observed=2026-08-15T21:29:16.321859Z digest=sha256:b174003a8f78ea604129f3d6a05169639d17fb1eeaa6f29d94f631415c7bd777

Observation 6b344d69-fb6c-4b64-ac68-856baac636e4 · outbound

This paper cites Who is GPT-3? An Exploration of Personality, Values and Demographics.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Who is GPT-3? An Exploration of Personality, Values and Demographics

Reference 3

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source=pdf_text observed=2026-08-15T21:29:16.326057Z digest=sha256:7e132845eb9703eef8fddcb82282b9b9a46e4f0de939a223c0472883ca7295bb

Observation ea3f0ac9-3db0-4b8c-8e99-4af6bf8154d7 · outbound

This paper cites Large language models surpass human experts in predicting neuroscience results.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Large language models surpass human experts in predicting neuroscience results

Reference 4

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source=pdf_text observed=2026-08-15T21:29:16.330351Z digest=sha256:0bf39d7e27f515ef93a9c66b6e6bf9d434947165bd3435157787e69a50f9108b

Observation 395a3caf-83ec-451f-b458-6c6446f698e2 · outbound

This paper cites Chatgpt and large language models in academia: opportunities and challenges,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Chatgpt and large language models in academia: opportunities and challenges,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T21:29:16.334559Z digest=sha256:b3fe7f7e225106606f0b48f6a72283a97facd25da9ff66b94f58b9d911022706

Observation 06155572-288c-4d6b-b618-4ef88c394a2f · outbound

This paper cites Ethical and social risks of harm from Language Models.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Ethical and social risks of harm from Language Models

Reference 6

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source=pdf_text observed=2026-08-15T21:29:16.338492Z digest=sha256:01d0971a928f57808310cca7226b9d158febe9210b92c9611234dea6fc36568e

Observation 3fdffb2c-61e4-4d2f-88a4-4530c5d0ef5f · outbound

This paper cites Extracting training data from large language models,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Extracting training data from large language models,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T21:29:16.343616Z digest=sha256:e2f895fdd73535acf7b20f60e12e8decfb9abd1a869ccf8e0187367741539f15

Observation ade23326-426d-47ea-96a8-08fb19169b21 · outbound

This paper cites Training language models to follow instructions with human feedback,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Training language models to follow instructions with human feedback,

Reference 8

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source=pdf_text observed=2026-08-15T21:29:16.347891Z digest=sha256:082f0a47973b1f2e33efd90352fc96c049035b68f85c6a7ea6224986fcd2bec9

Observation 4bab69ff-0aea-46ea-a11d-5f4c1cc8f16f · outbound

This paper cites Pretraining language models with human preferences,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Pretraining language models with human preferences,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T21:29:16.352218Z digest=sha256:10f62e4cd5fa67766d8a818996da0b478a6c8961149331b50bef51fffc1a4776

Observation bb493853-c1ab-4c47-b369-2084b2ed5838 · outbound

This paper cites RAIN: Your Language Models Can Align Themselves without Finetuning.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent RAIN: Your Language Models Can Align Themselves without Finetuning

Reference 10

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source=pdf_text observed=2026-08-15T21:29:16.356471Z digest=sha256:22e31a1a06f74375228087da4948fef7b6b8c2e56d5d84e1f6cea670d11201f4

Observation dd737c25-6545-473a-a787-3c97b810300d · outbound

This paper cites MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots

Reference 11

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source=pdf_text observed=2026-08-15T21:29:16.361397Z digest=sha256:35da31934faf477ad07562163985b07ca2aa46854cd1ed5a9dd059621d1c19d9

Observation da1edfa5-ebac-49a4-8dac-588f4095140f · outbound

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

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 12

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source=pdf_text observed=2026-08-15T21:29:16.366235Z digest=sha256:21c6e4a38e943adf137539e1d53b7960c045de63aed2ec9145a9c24d36163b78

Observation 92200545-6874-49d2-9dc1-2d296e23ed14 · outbound

This paper cites Intriguing properties of neural networks.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Intriguing properties of neural networks

Reference 13

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source=pdf_text observed=2026-08-15T21:29:16.370977Z digest=sha256:04da37fd0e70803d078032e63c1ce047d834cb1026efa29510fe62e7a0c8c6c6

Observation d7a49216-f850-413b-9f8d-660bb3b0ae02 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Explaining and Harnessing Adversarial Examples

Reference 14

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source=pdf_text observed=2026-08-15T21:29:16.375661Z digest=sha256:f9d3e969d1ea4b8923f847e37b463388a0bc8efe6e5ed34fe61944f491e12c01

Observation 61eb4008-ecbc-4d42-8f19-1de8b84833a0 · outbound

This paper cites Jailbroken: How does llm safety training fail?.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Jailbroken: How does llm safety training fail?

Reference 15

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source=pdf_text observed=2026-08-15T21:29:16.379883Z digest=sha256:f7b48f74d99ee73df272492e2ae49a22d36528c12b3082e25e88f023a97533c1

Observation a3780aa4-ae9c-4d04-abca-1657f0f21b7c · outbound

This paper cites Prompt engineering in large language models,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Prompt engineering in large language models,

Reference 16

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source=pdf_text observed=2026-08-15T21:29:16.383905Z digest=sha256:ebd36c876113e1da35f0570f6cb80aeac01ef93b4966ff370a3c1db33ae69b1a

Observation 14ce5075-4c6a-4b89-9a50-1b37925fadcc · outbound

This paper cites AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

Reference 17

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source=pdf_text observed=2026-08-15T21:29:16.388004Z digest=sha256:498e1f9417328e8977feac7dca2474015eb1141d2670d96168eb2c40dc28c8c0

Observation 654f7344-1368-4768-aae3-8abf3f74544f · outbound

This paper cites Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery,

Reference 18

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source=pdf_text observed=2026-08-15T21:29:16.392502Z digest=sha256:e3f36eeef3b05a161efab8e15e19dbda29f40701da2cada20365b989203199f9

Observation 06702131-5177-4386-bec7-c3ef6dc06e96 · outbound

This paper cites Are aligned neural networks adversarially aligned?.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Are aligned neural networks adversarially aligned?

Reference 19

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source=pdf_text observed=2026-08-15T21:29:16.396800Z digest=sha256:206849e2fbb11fbebda665cb5b89851d76415c28c25d7bf3087315989864d0cb

Observation 01f0e7e2-5dc8-4218-ab0d-752c9ffe1e46 · outbound

This paper cites Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation

Reference 20

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source=pdf_text observed=2026-08-15T21:29:16.400855Z digest=sha256:9ef8f24250725f13bd141dee34f6e1798a52b9ba46b74341fa76619f82e6730e

Observation bff6df8c-b177-4d6e-9e59-ec97cd3131be · outbound

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

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 21

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source=pdf_text observed=2026-08-15T21:29:16.405196Z digest=sha256:5dd45d4519d68ce6f75ad6595b36273b32bfd69bce121eef3f21acf8fd436529

Observation d4da211b-6020-46bf-817d-97544570e12e · outbound

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

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Fast Adversarial Attacks on Language Models In One GPU Minute

Reference 22

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source=pdf_text observed=2026-08-15T21:29:16.409643Z digest=sha256:49918254013c33c3c45983e09c65e5702ded881a4a9826d8b3469e5d6e3fb1af

Observation 84ad1fc4-de68-4e33-9fb0-16b1bd0f29a7 · outbound

This paper cites Attacking large language models with projected gradient descent,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Attacking large language models with projected gradient descent,

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T21:29:16.414279Z digest=sha256:0660d093d55e671a4ee6c5ccf6fbbc9405207ed45d1c21e0624a9a562b17b76a

Observation 28be6349-f425-497c-ad24-49d4f9380f25 · outbound

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

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space

Reference 24

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source=pdf_text observed=2026-08-15T21:29:16.418332Z digest=sha256:277d61dd657882d8b96332db268e41b07abb11d9f44467bba16ae0039f08875b

Observation 8674ed51-c1f0-4cee-837e-addf1442e3b4 · outbound

This paper cites Jailbreak in pieces: Compositional adversarial attacks on multi-modal language models,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Jailbreak in pieces: Compositional adversarial attacks on multi-modal language models,

Reference 25

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source=pdf_text observed=2026-08-15T21:29:16.423122Z digest=sha256:80737e2c63baf589a0d93edbd56faf7258b69991cbbead9d799b30c13b257087

Observation 45d0c3f6-4a6f-4b91-98f9-a86b9c764ba6 · outbound

This paper cites Assessing Adversarial Robustness of Large Language Models: An Empirical Study.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Assessing Adversarial Robustness of Large Language Models: An Empirical Study

Reference 26

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source=pdf_text observed=2026-08-15T21:29:16.427888Z digest=sha256:0a2582bc07aff9984a1cdc0f7b47c09657bff77d25cc925bb308efd9d3e50ef3

Observation cfe9e1c3-21d3-43a2-a2aa-694cabcdd200 · outbound

This paper cites Geometric analysis and metric learning of instruction embeddings,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Geometric analysis and metric learning of instruction embeddings,

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T21:29:16.432783Z digest=sha256:18474230de7a69b57de570b5a159634ea40a9da82f948aa1b09f973ee1750989

Observation 03fff507-e944-4e25-a0ce-5f076953e413 · outbound

This paper cites Large Language Models as Superpositions of Cultural Perspectives.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Large Language Models as Superpositions of Cultural Perspectives

Reference 28

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:29:16.437346Z digest=sha256:6bbf388486739a6f208780abc63e8b3943bddd68942f829487fdea6345a2a1f3

Observation 6ee496b2-3fb9-4b6c-b248-dafb5a595ebb · outbound

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

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 29

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source=pdf_text observed=2026-08-15T21:29:16.442750Z digest=sha256:e1b3a3bbb17227f3bb18602080ff30f99773312ed175d02e086eeaff4986d3d2

Observation c26ca2eb-4355-42de-acb8-0add32eb973b · outbound

This paper cites Universal Adversarial Triggers for Attacking and Analyzing NLP.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 30

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source=pdf_text observed=2026-08-15T21:29:16.448110Z digest=sha256:0758f5d19b3f69043d4fc0a28fd5b57931267fd5a8cf6d9b6d9ad8e0d3ad7f33

Observation 5ecb55fd-450c-412a-8e07-68c412623829 · outbound

This paper cites HotFlip: White-Box Adversarial Examples for Text Classification.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent HotFlip: White-Box Adversarial Examples for Text Classification

Reference 31

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source=pdf_text observed=2026-08-15T21:29:16.453009Z digest=sha256:9fccd1a4eac43c84e1e324a65590d2323234f0c698238414cc6788a4a62898b2

Observation 031b59af-b7c5-40eb-ad2b-de002ad07537 · outbound

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

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent AmpleGCG: Learning a Universal and Transferable Generative Model of Adversarial Suffixes for Jailbreaking Both Open and Closed LLMs

Reference 32

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source=pdf_text observed=2026-08-15T21:29:16.457905Z digest=sha256:1b643ceb7b7ed2731808743c42957e883eaeffeb76b2b3234c56d3fe47e6233b

Observation d3308ecb-c694-4f5d-8543-3f71de6150ec · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 33

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source=pdf_text observed=2026-08-15T21:29:16.462733Z digest=sha256:a87465914afaeb3e46eb1c91a14713768ab19cdfa647d407e0aadbd16ae323b4

Observation fdb3f88b-be62-4313-9720-d8d3ec6d2e2b · outbound

This paper cites Efficient projections onto the l 1-ball for learning in high dimensions,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Efficient projections onto the l 1-ball for learning in high dimensions,

Reference 34

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source=pdf_text observed=2026-08-15T21:29:16.467755Z digest=sha256:ea02cb00e5dacda686ff27d1e3e991a342ac0998f57f4b7cd0bfcf335aa25a48

Observation 220e539b-b4b8-4f9a-9de6-08aaac1c4c78 · outbound

This paper cites Crafting ad- versarial input sequences for recurrent neural networks,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Crafting ad- versarial input sequences for recurrent neural networks,

Reference 35

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raw_fallback, observed 2026-08-15T21:29:17.259076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T21:29:16.472832Z digest=sha256:44e63e61e7f1896554af057ef27fc2950028ef8383fea2c2a7a52eb4ebaae48f

Observation 51c67002-a060-4470-90a3-029f7b96093b · outbound

This paper cites Exponentiated gradient versus gradient descent for linear predictors,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Exponentiated gradient versus gradient descent for linear predictors,

Reference 36

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raw_fallback, observed 2026-08-15T21:29:17.245280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T21:29:16.478035Z digest=sha256:0b97e11d52f00d42fc8b1f28c85e553a36d67a5c80fa41625032354b8485cc0e

Observation 1f0ac72d-5457-4369-a5b9-348e98340a84 · outbound

This paper cites Exponential gradient with momentum for online portfolio selection,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Exponential gradient with momentum for online portfolio selection,

Reference 37

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raw_fallback, observed 2026-08-15T21:29:17.230310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T21:29:16.482227Z digest=sha256:39f416bdd51e5881cbcbeb34b7b0baec3757148643b5d05f1314b103987536e1

Observation eec37898-d42e-4b23-9078-219ed691a07c · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Adam: A Method for Stochastic Optimization

Reference 38

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no resolver link, observed 2026-08-15T21:29:16.486996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:29:16.486996Z digest=sha256:e17f2af09f4253de251c867f5394359521a36803cbd0d4569be4db6466a6bde5

Observation 610f98c6-7859-47c2-b45b-f20c3ab68553 · outbound

This paper cites The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming,

Reference 39

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unresolved
no resolver link, observed 2026-08-15T21:29:16.491670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:29:16.491670Z digest=sha256:ad7dfc5cc042877799963654c77ab129a7a4a3a421ad9f1fb1bb198e14c6777b

Observation 8e0d4897-07a3-4234-aedf-fdab31654c2a · outbound

This paper cites Iterative bregman projections for regularized transportation problems,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Iterative bregman projections for regularized transportation problems,

Reference 40

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verified fuzzy
raw_fallback, observed 2026-08-15T21:29:17.215871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T21:29:16.496633Z digest=sha256:cbd7d9391fb2d195febd5c89898f3a539fae6d047e06ed2333c124b25177831d

Observation 6565e7be-3bc6-4dab-8688-02cda61b87ee · outbound

This paper cites An inertial forward-backward algorithm for the minimization of the sum of two nonconvex functions,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent An inertial forward-backward algorithm for the minimization of the sum of two nonconvex functions,

Reference 41

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verified fuzzy
raw_fallback, observed 2026-08-15T21:29:17.200720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T21:29:16.506520Z digest=sha256:3f951ba286b7bcd1c6ffd949ef1a49834d62f33bdfd40b92454809e1e48fe214

Observation 6bc92a63-76ee-409f-b546-87659bc84cd3 · outbound

This paper cites Computational Optimal Transport.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Computational Optimal Transport

Reference 42

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unresolved
no resolver link, observed 2026-08-15T21:29:16.517082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:29:16.517082Z digest=sha256:cd446ad3c2909c8e0e7d776fa5ba550f8246b276f525124e4052cbbc0ca832ef

Observation 6d20c213-c18d-423d-b8b9-3aa3f4a10790 · outbound

This paper cites An inertial forward-backward algorithm for the minimization of the sum of two nonconvex functions.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent An inertial forward-backward algorithm for the minimization of the sum of two nonconvex functions

Reference 43

Resolution
metadata mismatch
local_arxiv, observed 2026-08-15T21:29:16.727783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T21:29:16.511844Z digest=sha256:002429287418ba75ef2f6f69a991c78b7f300dc38220db25ab5f5ff54f165c30

Observation de503318-da62-4e26-a0e7-2beb59d07e89 · outbound

This paper cites Introducing mpt-7b: A new standard for open-source, commercially usable llms, 2023,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Introducing mpt-7b: A new standard for open-source, commercially usable llms, 2023,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:29:17.186032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T21:29:16.527859Z digest=sha256:b32cf9955c663f03711bdeaaefaf310f037cc167e78c9d600bafc6e69d6f22ff

Observation d2537125-b55c-4f03-bf93-4b0d1331be5b · outbound

This paper cites The Falcon Series of Open Language Models.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent The Falcon Series of Open Language Models

Reference 45

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no resolver link, observed 2026-08-15T21:29:16.522098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:29:16.522098Z digest=sha256:4ce2c5cdbaef4624b34d87e3e024b7456ba780d3375573a6dccb983368a534d7

Observation 8dd98fed-e5d9-49b9-b7cc-b97fc133e0e5 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena,.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Judging llm-as-a-judge with mt-bench and chatbot arena,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:29:17.171571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T21:29:16.537377Z digest=sha256:09803a5e9fe9d6fd879516badb327ad8376ff3db67f0574439b8ce11eebb0378

Observation 7013ffd1-1a30-4f9b-b00a-6f29aeaddb9b · outbound

This paper cites Mistral 7B.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Mistral 7B

Reference 47

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unresolved
no resolver link, observed 2026-08-15T21:29:16.532209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:29:16.532209Z digest=sha256:9d039c7d1bc6601f4e994268365b9c2da628d5c1942c8503367955fb816c0953

Observation 43df6081-4a84-4b9f-ac0b-2cd2f4fabcf6 · outbound

This paper cites Safe RLHF: Safe Reinforcement Learning from Human Feedback.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Safe RLHF: Safe Reinforcement Learning from Human Feedback

Reference 48

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unresolved
no resolver link, observed 2026-08-15T21:29:16.549212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:29:16.549212Z digest=sha256:439c8b95786d0c41d496cae32d0ae46566505786066ece4401d4e9ea9ba6a8a6

Observation 86bb26c6-1c16-446a-b6d2-76881b89c8b9 · outbound

This paper cites The Llama 3 Herd of Models.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent The Llama 3 Herd of Models

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-15T21:29:16.544087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:29:16.544087Z digest=sha256:396b3179182dd00f6ee53f9876fa87626064f52e6a09eb368cb7380c0cc933e4

Observation af923638-4bb1-4dd4-8a51-57cccc6e8d7f · outbound

This paper cites JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-15T21:29:16.560280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:29:16.560280Z digest=sha256:6f003096339417358330c2c8535d306a400cb9da24ba952c38d1ae905dc000ff

Observation 514f4c1d-22c2-4815-8c9f-08c0818c727f · outbound

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

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal

Reference 51

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unresolved
no resolver link, observed 2026-08-15T21:29:16.555115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:29:16.555115Z digest=sha256:5c0664fc7517f6aaa7cbcac846ba82b10c3fa30080f9a6627eea9102c592a41b

Observation 2717cc57-55ef-4152-8970-3dcd38bde6d9 · outbound

This paper cites Adversarial Attacks on Large Language Models Using Regularized Relaxation.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Adversarial Attacks on Large Language Models Using Regularized Relaxation

Reference 53

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no resolver link, observed 2026-08-15T21:29:16.565174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:29:16.565174Z digest=sha256:39bab4b6f4a8d17a3563a50406f2e13a3d19d46d08a3d42a5465db6ec12075fc

Observation e64fdc34-cb90-4957-ab86-426938272978 · outbound

This paper cites Iterative Bregman Projections for Regularized Transportation Problems.

Adversarial Attack on Large Language Models using Exponentiated Gradient Descent Iterative Bregman Projections for Regularized Transportation Problems

Reference 2014

Resolution
metadata mismatch
local_arxiv, observed 2026-08-15T21:29:16.747727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T21:29:16.501185Z digest=sha256:5082c39992c6917cfe220a9fb38294b50e518830afb49c8d7a544c6c3d34a3ad

Pith citing papers

No inbound Pith citation observations are available.