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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 2026-08-16T06:30:59.297886+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

No source-named external measurement is stored.

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:d99640a795a27d5787a4f7a064660e0c32d4b1b5e362d7a90d0eaa3b73692636

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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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=sha256:6d1087b5083bbf89ee565111d50bfb1e500eadc19ddb3bc570c7ca6840995fff

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T21:29:16.334559Z digest=sha256:d710c9531af961e07a74b411c8c30d5adf9ce2c7f25906db9d40d6fa18919942

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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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

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source=pdf_text observed=2026-08-15T21:29:16.343616Z digest=sha256:31fee511638ac6598d4ced2d3fa4579b087bee637a5c1089500773e47677c6ce

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:ab5c2fe2b180b211aef08bc4eb6d19015b0248e3201799916fb8e13daaaa65e0

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=pdf_text observed=2026-08-15T21:29:16.352218Z digest=sha256:2116e6ea884a6a6fd5296d8c17f0815ba520396fb11f2ef58147d79a1f851090

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:b291787dcbb84bfeef6db8b968487387760ce05ee32b8a654f925da2cefcdc2e

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=sha256:17c91b913cc8f021fb9751beb67976d916729fce7e0a644c5d4872c008adfbde

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:e5642cc5b6221e4a461ef545f877dca73401d2ebbc2fe75373b6123d5e622e32

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:d7523c7cc41424d6bcf5806001c2f3918c9f64651dbe81cfa77180a2e46cd29a

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:b39d95e55e0b2d7140ba091d1c9f54e2ef7de3a8787e035bb5f7bc5501de9343

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=sha256:831fe7aa275784d659825377f15ab0e3c183ee3b83ca6a84d43551e3a30b1898

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:edd19a2e230bad65c75fe1e3ca96c419140e84641c4219fbd039661a537f7dcb

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:d3ecc170d85345460c6c0fc841b78a53fb6bbae24785c1fc8d5a755e19a380cc

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=sha256:9f785a67ae50eba06a4f1ecc5e86ae23e1990089edfa14d8b879811c727f73a1

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:c89fa4a08d88db51d27092786ff8fcc718592906113e702174140f7dc5ca2a9c

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=sha256:4ca3c7f97c002f5ac8f72922ea32caa25fa0c141b0e48403c13cae394cb0b43e

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=sha256:388c393634d7c5e16f3228a0b45403ede5eff51f6db661f419ed44d10007b9e3

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:f482d014ed5f77ede9e338888b556cf59a63eee2ffd4762dd3a35a4690194ec3

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T21:29:16.414279Z digest=sha256:18c4730f6284be1277218146040dae2f58bcf208220cb866738f6c0e8dab0d67

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=sha256:49d146a042db3819ed79f1c27d6c44b7e97621b132f37df688b8b195fb2e5e6f

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=sha256:10a8efda23125087c53ce85918db01c90bd0283475ef68f5ee0af0c0532f7e14

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=sha256:23f6669e89ab9e3e8debc70f0f0ad800400c9b92cd41f1859bed228cd06b0ba9

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T21:29:16.432783Z digest=sha256:7e76ea42bdf46c01173e6c34a85ed44fa78c5e6cdeb1a1edd824a0cda788f9a5

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=sha256:9400fc93ecf423d870d85259eeba6b6e97b382dbedf4f1d3a95b256eea30a6fd

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:d9f59590418a66fbef3bcc539cab5dc56b2bf4c5897c3d92ab8146a5720381ba

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=sha256:3c3f695c3f0b4b7acc4cc8984ba407e7be651c124df0670f489edf443508b4c3

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:a2e7d1f7f599a556d4a56155e61f3f46f87f8f46e1950930970d7bbc39627cb9

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=sha256:6ee0fd5d9bfe29e3a02ea600343ce1333b9682e2b98a10a5ff0c84d3cfb72136

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:faf00050a62d44246fc1cadcf3b0682f3ecc3e5e67948fecce2f7115f8e69177

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:d64e2a5beb3fe5f3198d4ec48d744291b24b8c5727aeddb57b6c1cae0e08a44d

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T21:29:16.472832Z digest=sha256:fa954c188d8b6483b9cbb9dc4bf6b8f8987fb3b86c6428ef6a8fada95c98e345

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T21:29:16.478035Z digest=sha256:8e6179c5d107bed1d7e59a5874bf8b2a223818e3ee0a472aa462d582e98c03ac

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T21:29:16.482227Z digest=sha256:f36a4a61ff0084e18f2edf4a435320fe7154290ece547c881f25a731bd3d6357

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=sha256:2165f83090106250add32f9885718f8184081893b54b0970e363060038bffef8

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:bac80f65dd7dcba24d0c1c70c1659c18b90177bdc10edcd37195768be6387f36

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T21:29:16.496633Z digest=sha256:623887a6bdd8e570aae8792b62949dcdf3686b4f728a010b1aab3cc14d1d14b0

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T21:29:16.506520Z digest=sha256:c7b02bcd64bd35e4dc29252de587c7c3743f20ed919b71851874f5f7b05325e9

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=sha256:2759ecfd9411b86d6c735e8b57d9a2e575feb9715425cc20fad761481fa43c12

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T21:29:16.511844Z digest=sha256:6df51466c65feaafd2537d18ee103a87fb463745465cde749aec1bcde350bbdc

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T21:29:16.527859Z digest=sha256:fea0b9677e64291ac6723e0f7a467cb0ee8a529a3eec4faf54c5c0d5624b0e66

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:db4bc901a64a76ba3ff29e2129c6438e14dc25cd82d831a6d763be08a1c423c8

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T21:29:16.537377Z digest=sha256:aecd83fb655cfa7cd9fa77f324973ee6d08b1ca37c119edea259646efe9c3cc5

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

Resolution
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=sha256:90b0394ee7f0537b11c1a56db011565e7a3011ac1daf6777c3a0739e9b24feda

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=sha256:056e40482504a949e4cd54a3ade23057149fcee5ef173a542788e6164975bc9c

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=sha256:290fa21922e6c2e56476ad334ab9dec075c4f41b7c9c5cb59c0f89847cf17a9c

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:d3ec7e5c5e2da1520cd9325402fb00b6b4c93d3b726a59e56a92e33c0d72fdf9

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

Resolution
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=sha256:30e7db9f04c9f997db68ed2fe37ef186e49eaafa8ea95a1f96b9d2fc2805ff01

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:fda3e283b14027b772d6a74d27d597ea90b12f98e5bef1d4ddecf81fb788beaf

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T21:29:16.501185Z digest=sha256:d816b11af9de16ae30bd75cd1b593829a546fd9b4ea99936e31b5b2a48cad834

Pith citing papers

No inbound Pith citation observations are available.