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Paper Citation Record · LEDGER

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs

As of 13 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 5 inbound Pith citation observations for arXiv:2411.13757.

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

pith.paper-citation-record.v1
2411.13757 v4

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:16:01.180601Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:46:16.161695Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T08:29:42.356230Z

Reference resolution

40 of 40 outbound references displayed

  • verified exact0
  • verified fuzzy19
  • unresolved21
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e65e99b6-ceb1-45d5-b4c9-a99a58c1cecf · outbound

This paper cites Improving language understanding by generative pre- training,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Improving language understanding by generative pre- training,

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:16:00.971225Z digest=sha256:66aae2daed783281adfe38ff515feb7ab88bc4237a0513900d60767adca2100c

Observation eebc9b53-a602-4c90-8d99-6c957154e26c · outbound

This paper cites A survey on evaluation of large language models,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs A survey on evaluation of large language models,

Reference 2

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raw_fallback, observed 2026-08-12T16:16:02.746519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T16:16:00.977562Z digest=sha256:e89851db6b2c0c203fae313a99b43f2efc197d59c2fe5f826bb56926ffe823e5

Observation c0c782cc-e23b-4340-be56-060838aeb10d · outbound

This paper cites A Survey of Resource-efficient LLM and Multimodal Foundation Models.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs A Survey of Resource-efficient LLM and Multimodal Foundation Models

Reference 3

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no resolver link, observed 2026-08-12T16:16:00.984041Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T16:16:00.984041Z digest=sha256:40f07315d37f9c1d8924442eb978dfe1a0596c3ffb8d6aeb2667b1b67dec02f4

Observation 28269a3f-2d7c-4caa-9a89-ab4b0f0c0021 · outbound

This paper cites Security and Privacy Challenges of Large Language Models: A Survey.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Security and Privacy Challenges of Large Language Models: A Survey

Reference 4

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T16:16:00.990401Z digest=sha256:0310f73a8cde829078e847adf7237f4e1872cd0a5dcc8d5d8bd8d363696cefc6

Observation 83bd4e99-04db-4403-a93d-008a80949e97 · outbound

This paper cites Bit-flip attack: Crushing neural network with progressive bit search,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Bit-flip attack: Crushing neural network with progressive bit search,

Reference 5

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raw_fallback, observed 2026-08-12T16:16:02.731164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T16:16:00.995540Z digest=sha256:54c1289868ecd5c51804b30432ce30fe0394b89d38cb7338121087895a312909

Observation 3f396951-df42-4d5c-a747-19e43991407c · outbound

This paper cites A survey of bit-flip attacks on deep neural network and corresponding defense methods,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs A survey of bit-flip attacks on deep neural network and corresponding defense methods,

Reference 6

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raw_fallback, observed 2026-08-12T16:16:02.715386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T16:16:01.000444Z digest=sha256:a0b117c48263b94d300b18ae67a868d0eee981b66e4b5c0eb4b3626e99fac404

Observation 5cd48146-66db-4284-b2cf-85e2837bef32 · outbound

This paper cites Bit-by-bit: Investigating the vulnerabilities of binary neural networks to adversarial bit flipping,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Bit-by-bit: Investigating the vulnerabilities of binary neural networks to adversarial bit flipping,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T16:16:01.005499Z digest=sha256:d93686bae6898ee7ff64a978f394416d0523cc9ae0ba17c3e868d4d54e66dbd5

Observation d9a12097-325b-4a01-a303-53fe523871e6 · outbound

This paper cites {DeepHammer}: Depleting the intelligence of deep neural networks through targeted chain of bit flips,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs {DeepHammer}: Depleting the intelligence of deep neural networks through targeted chain of bit flips,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T16:16:01.010741Z digest=sha256:02995ee9a9b91b0b282bb78592df19aa4ca04d30ef8c270d3317b201473a6e4e

Observation 5c9a06b9-107f-4990-a69d-149a0e4437f7 · outbound

This paper cites Non-invasive emi-based fault injection attack against cryp- tographic modules,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Non-invasive emi-based fault injection attack against cryp- tographic modules,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T16:16:01.015691Z digest=sha256:81691a68ec8ed3f96c592f2f6d93cd6a26e8b7f6df7b3a1bcbeb9b8e7017dc9a

Observation 2850ca57-3fc8-44c1-a591-78c5fbed83b4 · outbound

This paper cites A com- prehensive survey on non-invasive fault injection attacks,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs A com- prehensive survey on non-invasive fault injection attacks,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T16:16:01.021119Z digest=sha256:394d4d1ad51bc8050dd9be56f56ac9f34072604f43c43f57c301c7db7162ab39

Observation 8513f571-8f15-4e28-9b0b-97bccd4d250c · outbound

This paper cites Proflip: Targeted trojan attack with progressive bit flips,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Proflip: Targeted trojan attack with progressive bit flips,

Reference 11

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raw_fallback, observed 2026-08-12T16:16:02.620221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T16:16:01.026132Z digest=sha256:6ed35ad71f7c667e20105d2ff1c6018afcc8d06b8b572d6aaeb68d4e058af273

Observation 6d074d0f-ab12-4a8c-be0f-25eab9f86375 · outbound

This paper cites Forget and rewire: Enhancing the resilience of transformer-based models against {Bit-Flip} attacks,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Forget and rewire: Enhancing the resilience of transformer-based models against {Bit-Flip} attacks,

Reference 12

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T16:16:01.031903Z digest=sha256:46cd794062d5689f01e52441e6586208a1920634a7c7be364a8b2cb80cfe5797

Observation 8619ff5b-ba11-4ba8-b7e3-ab5fd89b854c · outbound

This paper cites Are transformers more robust than cnns?.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Are transformers more robust than cnns?

Reference 13

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source=pdf_text observed=2026-08-12T16:16:01.036972Z digest=sha256:4596ea95467a387004a7348850a93fe6fd1833a77a8fb7f9661ebe0ef8d0c3b7

Observation 837446ad-ae9d-4d04-9658-ada8ba00e333 · outbound

This paper cites Genetic algorithm-a literature review,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Genetic algorithm-a literature review,

Reference 14

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:16:01.046855Z digest=sha256:664a4836983d7574b617f7eb0a0c4d41e0fe1a188a5eb3b8a2a1809deca291d6

Observation 0430d258-333c-475b-a140-0753aac3d47a · outbound

This paper cites Attention is all you need.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Attention is all you need

Reference 15

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raw_fallback, observed 2026-08-12T16:16:02.567861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T16:16:01.052704Z digest=sha256:ba7f0607847d6bcefed980326a57e7fb23222bf6356e643631fbd499371b99ef

Observation deff655a-1674-4820-b4f0-8ec6baa1208c · outbound

This paper cites Zhang, Z.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Zhang, Z

Reference 16

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:16:01.057519Z digest=sha256:7a2a11a6953a92df9d02242dda376c78b213c0e2573c426908bce7832083ee51

Observation f55d5eb7-5bef-47a8-8f02-d50822ae31cf · outbound

This paper cites {IMIX}:{In-Process} memory isolation {EXtension},.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs {IMIX}:{In-Process} memory isolation {EXtension},

Reference 17

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raw_fallback, observed 2026-08-12T16:16:02.539735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T16:16:01.061812Z digest=sha256:7ddbab757cfc079735e8ee0b16a86c2c2bc67baf06475689830eb40f57d4be56

Observation 61f8283b-35fe-46d5-b6ef-b68b06c53165 · outbound

This paper cites Flipping bits in memory without accessing them: An experimental study of dram disturbance errors,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Flipping bits in memory without accessing them: An experimental study of dram disturbance errors,

Reference 18

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raw_fallback, observed 2026-08-12T16:16:02.523487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T16:16:01.069690Z digest=sha256:78ec5fc8dc94bfd593088932467af634f7dbcd6e1c5acd4b5c721b63cad23e06

Observation ee1696c9-ae52-4e1a-aa20-2b32ef333dec · outbound

This paper cites Rowhammer: A retrospective,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Rowhammer: A retrospective,

Reference 19

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raw_fallback, observed 2026-08-12T16:16:02.505925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T16:16:01.074504Z digest=sha256:ca3420103978989549110f60f8d87423c875dcb06909cc71557255031949fc27

Observation 9528ffc4-e8f5-4738-a347-6edfbdd909e2 · outbound

This paper cites A closer look at evaluating the bit-flip attack against deep neural networks,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs A closer look at evaluating the bit-flip attack against deep neural networks,

Reference 20

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raw_fallback, observed 2026-08-12T16:16:02.488380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T16:16:01.078582Z digest=sha256:17fc12ceca79ad745288ccef225322bb50a04840cffd163965b9f4a7b58b64f9

Observation eddbecff-2d1c-4d37-86fd-fbc1deb4c94d · outbound

This paper cites The Llama 3 Herd of Models.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs The Llama 3 Herd of Models

Reference 21

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:16:01.083907Z digest=sha256:c29bbac17291e5845b8d2a1577a87b783de0d57bc4040245d0f4fd68db17a3b1

Observation de56739b-4cdf-4012-ab13-d1c4600dc0a9 · outbound

This paper cites LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Reference 22

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source=pdf_text observed=2026-08-12T16:16:01.088608Z digest=sha256:722462779be32c50e32751485702204f00a433e716fe48bcdbab92a6469fd26d

Observation d5bb12e6-511a-43fc-abc0-a41662e8805e · outbound

This paper cites Fault injection attack on deep neural network,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Fault injection attack on deep neural network,

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T16:16:01.092976Z digest=sha256:66bfaa53e6a10b45c435682c487bd1c210ca3d87f6e00c94f0a5baf01f37ddb4

Observation 09251056-0f7c-4b43-a332-c0db0b48fc0c · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs LLaMA: Open and Efficient Foundation Language Models

Reference 24

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:16:01.097010Z digest=sha256:6e91b1344189757da74b29bafa6dd093cef3bc4b7e15e4f17ee0733fb46b1af2

Observation 80fba939-3f07-4635-aac4-72ff4ca03ed8 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 25

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:16:01.101632Z digest=sha256:4ea94e7e2e8a92f25daffc51c0b1c7ed57ebf0adbed81e90ef7380e824e091f9

Observation 98fb6b9a-d3dd-4968-a850-0d48466f0fc7 · outbound

This paper cites The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 26

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source=pdf_text observed=2026-08-12T16:16:01.105849Z digest=sha256:49a6183aa6b31de2d8df20c2c0649ea11021e282e48bea81d36a242668f744da

Observation 894793e3-eb37-4daa-b679-874e8a76af9b · outbound

This paper cites Are We Done with MMLU?.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Are We Done with MMLU?

Reference 27

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source=pdf_text observed=2026-08-12T16:16:01.111395Z digest=sha256:d2a295658ceb32402052d63a07957d510325f96e4e004ccef88301e7b331c4e5

Observation e8e808d2-af52-41d0-bae9-ddba6d59514e · outbound

This paper cites A framework for few-shot language model evaluation,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs A framework for few-shot language model evaluation,

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:16:01.119084Z digest=sha256:fdedea419c30f0525536f0fd41daf722ad0bbb1ff8cf10c2b29db60f18b46fc4

Observation 9dbda892-fbdb-44f9-8693-c15ebf7864e3 · outbound

This paper cites Improved baselines with visual instruction tuning,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Improved baselines with visual instruction tuning,

Reference 29

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source=pdf_text observed=2026-08-12T16:16:01.124827Z digest=sha256:2cc0c1e75c8a45cd53e76e8c8593f4472113f20d3b78c2a12ec0f41dbab006c5

Observation eb83e5b8-9446-4558-802b-6cdafa06a624 · outbound

This paper cites Openvivqa: Task, dataset, and multimodal fusion models for visual question answering in vietnamese,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Openvivqa: Task, dataset, and multimodal fusion models for visual question answering in vietnamese,

Reference 30

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raw_fallback, observed 2026-08-12T16:16:02.448489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T16:16:01.129057Z digest=sha256:ec5149f57b2d63b5abed2218a71ba83775cd2fab384e3e2ffc6a79e74e20e2c3

Observation 16430c2a-8838-4409-b2ec-b554d5ddec66 · outbound

This paper cites Towards vqa models that can read,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Towards vqa models that can read,

Reference 31

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T16:16:01.134543Z digest=sha256:e20ec718a53e822b606709434c4406652814caa69b38b4b0cadbd5683fbd5bb7

Observation 64acd883-d19e-43df-9eed-19ea2a63dc3f · outbound

This paper cites Can Perplexity Reflect Large Language Model's Ability in Long Text Understanding?.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Can Perplexity Reflect Large Language Model's Ability in Long Text Understanding?

Reference 32

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:16:01.139702Z digest=sha256:dda6ae6710b0179ade59bc26791f49ccc46f5a4f19b5eb8ae540d62e6412492a

Observation a9140e4b-5bf7-4874-9d8d-b120f351fdbc · outbound

This paper cites Pointer sentinel mixture models,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Pointer sentinel mixture models,

Reference 33

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source=pdf_text observed=2026-08-12T16:16:01.145254Z digest=sha256:58e020fbd46d3bc271e086228eae797cdea33255c9ee9dbdda115598504d3cc7

Observation e02a791e-bf25-4157-9fa1-512b9e929dbd · outbound

This paper cites Changing Answer Order Can Decrease MMLU Accuracy.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Changing Answer Order Can Decrease MMLU Accuracy

Reference 34

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source=pdf_text observed=2026-08-12T16:16:01.149922Z digest=sha256:8fadfd84befc74f2196fe4a3d5ee6526d174ef591301919149fbee7c27b2580d

Observation 70d476a7-49ee-452e-abf5-51fe3c9491a4 · outbound

This paper cites Generalized cross entropy loss for training deep neural networks with noisy labels,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Generalized cross entropy loss for training deep neural networks with noisy labels,

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation dce2e3fe-6a26-43aa-9f41-038e5a08c849 · outbound

This paper cites an unresolved cited work.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Unresolved cited work

Reference 36

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 1e15d389-880c-45be-986b-7483b8427a98 · outbound

This paper cites A new optimizer using particle swarm theory,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs A new optimizer using particle swarm theory,

Reference 37

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 0e7424f7-78e4-4ed1-93ab-9669369d692a · outbound

This paper cites Muiltiobjective optimization using non- dominated sorting in genetic algorithms,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Muiltiobjective optimization using non- dominated sorting in genetic algorithms,

Reference 38

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation f099b3bb-611f-4cc8-b436-ec4cf086d313 · outbound

This paper cites A fast elitist non-dominated sorting genetic algorithm for multi-objective optimiza- tion: Nsga-ii,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs A fast elitist non-dominated sorting genetic algorithm for multi-objective optimiza- tion: Nsga-ii,

Reference 39

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 3d7df22e-778f-42b7-b519-b4df4a7ec54d · outbound

This paper cites Advances in logic locking: Past, present, and prospects,.

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs Advances in logic locking: Past, present, and prospects,

Reference 40

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Pith citing papers

Observation f3c48745-4f6c-4202-8252-e15559ad48f3 · inbound

LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems cites this paper.

LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs

Reference 68

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a0c8b0b3-e6ef-441f-9f16-d0f9391c54a0 · inbound

CacheTrap: Unveiling a Stealthier Gray-Box Trojan against LLMs cites this paper.

CacheTrap: Unveiling a Stealthier Gray-Box Trojan against LLMs GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs

Reference 4

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 0832b4c8-2fe7-4356-94e0-8bb995c05a57 · inbound

Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis cites this paper.

Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs

Reference 20

Resolution
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arxiv_id, observed 2026-07-02T03:46:32.513144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 8f64111e-8db5-4523-a5f7-0f8b5e84ff41 · inbound

Investigating The Security of Modern AI and Cloud Infrastructure cites this paper.

Investigating The Security of Modern AI and Cloud Infrastructure GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-07-04T08:29:42.357848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 03e4d2d8-c38b-4358-8cad-134c67f47699 · inbound

Decision-Level Hijacking: Injecting Cognitive Bias into Large Language Models via Bit-Flip Attacks cites this paper.

Decision-Level Hijacking: Injecting Cognitive Bias into Large Language Models via Bit-Flip Attacks GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs

Reference 7

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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