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Paper Citation Record · LEDGER

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF

As of 8 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 3 inbound Pith citation observations for arXiv:2506.03234.

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

pith.paper-citation-record.v1
2506.03234 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:18:06.054981Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T16:26:34.918099Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T01:37:30.522097Z

Reference resolution

39 of 39 outbound references displayed

  • verified exact0
  • verified fuzzy13
  • unresolved25
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c232ca51-e49b-4862-ba9d-ffcc20072146 · outbound

This paper cites Best-of-Venom: Attacking RLHF by Injecting Poisoned Preference Data.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Best-of-Venom: Attacking RLHF by Injecting Poisoned Preference Data

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:18:03.450932Z digest=sha256:a7d19a301c3787ae315e40f409dce4d78d3fa919a3fce6615a8b3955e678f3b9

Observation 4c5bc4b2-3cf6-451b-8f0e-fa0f87f8f7ff · outbound

This paper cites Poisoning Attacks against Support Vector Machines.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Poisoning Attacks against Support Vector Machines

Reference 2

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no resolver link, observed 2026-08-07T11:18:03.502246Z

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source=pdf_text observed=2026-08-07T11:18:03.502246Z digest=sha256:bdf77bf07cafb816117ed7d2f4d5ea723934da00d27a0648524221033361cbd8

Observation 9a7d5036-1e5b-40ac-86eb-e677ced3f55f · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Training Diffusion Models with Reinforcement Learning

Reference 3

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no resolver link, observed 2026-08-07T11:18:03.581756Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:18:03.581756Z digest=sha256:a6423a03aa3f3b6fa8e0f0b77cb6f211b2dc1625b28734c492d8d05e9ca15551

Observation cc6c3d91-5ef5-4e50-bae9-873226a676fc · outbound

This paper cites A survey on generative diffusion models.IEEE Transactions on Knowledge and Data Engineering, 2024.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF A survey on generative diffusion models.IEEE Transactions on Knowledge and Data Engineering, 2024

Reference 4

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no resolver link, observed 2026-08-07T11:18:03.657237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:18:03.657237Z digest=sha256:76e0f554abaf4d7e24cff077faf30c0b7f14bf42b94c6522f400e488d9006a18

Observation 08bfd459-da94-4898-be9a-85e1de397f36 · outbound

This paper cites Trojdiff: Trojan attacks on diffusion models with diverse targets.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Trojdiff: Trojan attacks on diffusion models with diverse targets

Reference 5

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no resolver link, observed 2026-08-07T11:18:03.750828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:18:03.750828Z digest=sha256:fd9e5550e209e693e4ae67b7cd54c4ed89f5f4fc070a3de5b962ce626eb6cc18

Observation 161f05ef-a822-4d0d-9935-86d69a731fc9 · outbound

This paper cites Amplifying membership exposure via data poisoning.Advances in Neural Information Processing Systems, 35:29830– 29844, 2022.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Amplifying membership exposure via data poisoning.Advances in Neural Information Processing Systems, 35:29830– 29844, 2022

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-07T11:18:08.561155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:18:03.836456Z digest=sha256:1d1f03375bce236fbb4ffed066bec13cfed01bcfa1967c5ad72333c9dc784389

Observation e758bf7b-1171-43c7-83a9-775a9b3d7d14 · outbound

This paper cites Villandiffusion: A unified backdoor attack framework for diffusion models.Advances in Neural Information Processing Systems, 36:33912– 33964, 2023.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Villandiffusion: A unified backdoor attack framework for diffusion models.Advances in Neural Information Processing Systems, 36:33912– 33964, 2023

Reference 7

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source=pdf_text observed=2026-08-07T11:18:03.929697Z digest=sha256:212170c3c6f2f21d41386aae7bb1b263a8418d6d95d611953093b319985e2dc3

Observation d73902d0-311e-4dd5-b6c3-8fe604230f35 · outbound

This paper cites Diffusion models in vision: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):10850–10869, 2023.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Diffusion models in vision: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):10850–10869, 2023

Reference 8

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:18:03.996111Z digest=sha256:a214cb686fa6b1157ab2988431708496e907a250aa8fd23d47db059dbc499704

Observation 37ae54e1-c492-41d2-9d81-98f72add11b5 · outbound

This paper cites A survey on data poisoning attacks and defenses.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF A survey on data poisoning attacks and defenses

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-07T11:18:08.342924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:18:04.089124Z digest=sha256:a0a0f13af6d631f0dca9893639905fe7362dc4aac57d8f4c2921f5eb502d45bd

Observation 154ec68f-8638-40af-9e34-1f2dc9129a4a · outbound

This paper cites Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models.Advances in Neural Information Processing Systems, 36:79858–79885, 2023.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models.Advances in Neural Information Processing Systems, 36:79858–79885, 2023

Reference 10

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source=pdf_text observed=2026-08-07T11:18:04.152983Z digest=sha256:9d728292403d237f9b83ac7ab7ef02d598ce336697b6c729dffec6b4600b9e30

Observation 8d7a39fa-b174-45d3-9f5e-7a4c4ddc7b8f · outbound

This paper cites Pick-a-pic: An open dataset of user preferences for text-to-image generation.Advances in Neural Information Processing Systems, 36:36652–36663, 2023.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Pick-a-pic: An open dataset of user preferences for text-to-image generation.Advances in Neural Information Processing Systems, 36:36652–36663, 2023

Reference 11

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:18:04.220875Z digest=sha256:a756e9c2bd8935dd41ffa997debc74745a26f981d992e6aeb5e16b54809d5bbc

Observation b37bad84-ff1b-432d-a2b4-af5336bffd74 · outbound

This paper cites Aligning Text-to-Image Models using Human Feedback.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Aligning Text-to-Image Models using Human Feedback

Reference 12

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no resolver link, observed 2026-08-07T11:18:04.275836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:18:04.275836Z digest=sha256:f1556ce898ac505a155f27051ba540564bbc90e24fab33f0120e2c5e8fc258b7

Observation ae670811-d078-4079-a6b9-4f6066a54382 · outbound

This paper cites Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation

Reference 13

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source=pdf_text observed=2026-08-07T11:18:04.336470Z digest=sha256:2e0e48867306e1893e33f42ad8e9ff6858dcb66a4ac8218e140454bd38928bd7

Observation 48f08532-d743-47ae-a892-36c3f5f6222f · outbound

This paper cites Inform: Mitigating reward hacking in rlhf via information-theoretic reward modeling.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Inform: Mitigating reward hacking in rlhf via information-theoretic reward modeling

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-07T11:18:08.117549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:18:04.397499Z digest=sha256:0450b0082eadbcd29a10b4fb314d5b34ec75cd970ebe24d61b522bf60eee3248

Observation e2d3d101-2de6-41d1-ba22-f1a52ee1920b · outbound

This paper cites Backdooring Bias ($B^2$) into Stable Diffusion Models.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Backdooring Bias ($B^2$) into Stable Diffusion Models

Reference 15

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source=pdf_text observed=2026-08-07T11:18:04.459824Z digest=sha256:78f948166316c63b713c877b3ae367b3dda6fd6c7830f5376b9c767730895bd6

Observation 7f949655-be9a-4032-8c9f-09a7e5a7ad61 · outbound

This paper cites From trojan horses to castle walls: Unveiling bilateral data poisoning effects in diffusion models.Advances in Neural Information Processing Systems, 37:82265–82295, 2024.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF From trojan horses to castle walls: Unveiling bilateral data poisoning effects in diffusion models.Advances in Neural Information Processing Systems, 37:82265–82295, 2024

Reference 16

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raw_fallback, observed 2026-08-07T11:18:07.974486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:18:04.508075Z digest=sha256:0f700b1c07f8613547d042bebaf86e12d2d789857944cd8f4f495a44ffcce1c9

Observation 59a7cbec-dd05-4dad-bc13-825bbfec08d6 · outbound

This paper cites Learning transferable visual models from natural language supervision.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Learning transferable visual models from natural language supervision

Reference 17

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:18:04.576132Z digest=sha256:9b45ac00c21a1365db7f97d7ad44fee3b429c25ea20202077561f0fc745e3d6c

Observation b6007439-ac16-4601-bf79-aefa8c707234 · outbound

This paper cites Universal Jailbreak Backdoors from Poisoned Human Feedback.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Universal Jailbreak Backdoors from Poisoned Human Feedback

Reference 18

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source=pdf_text observed=2026-08-07T11:18:04.647947Z digest=sha256:ea06fc848c575596192ecdeb35d0ef97f334b23883a6f859211a9ccdf9852374

Observation 50d6e421-ac7a-49fb-9868-0ca8442a5a76 · outbound

This paper cites Laion- 5b: An open large-scale dataset for training next generation image-text models.Advances in neural information processing systems, 35:25278–25294, 2022.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Laion- 5b: An open large-scale dataset for training next generation image-text models.Advances in neural information processing systems, 35:25278–25294, 2022

Reference 19

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source=pdf_text observed=2026-08-07T11:18:04.739251Z digest=sha256:2f34469de82fed557571052a71a5185738d314d5957a9242988f800b40316245

Observation 5e05d140-3491-48fb-814f-a6e74ae761a1 · outbound

This paper cites Poison frogs! targeted clean-label poisoning attacks on neural networks.Advances in neural information processing systems, 31, 2018.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Poison frogs! targeted clean-label poisoning attacks on neural networks.Advances in neural information processing systems, 31, 2018

Reference 20

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raw_fallback, observed 2026-08-07T11:18:07.823975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:18:04.821469Z digest=sha256:d497dc6a77373f9d0dbcc97719a335336dd560819c2f3d362086bd17977c6448

Observation 54f7eae3-a740-4c7a-ad9f-5aac9f7f5c47 · outbound

This paper cites Nightshade: Prompt-specific poisoning attacks on text-to-image generative models.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Nightshade: Prompt-specific poisoning attacks on text-to-image generative models

Reference 21

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raw_fallback, observed 2026-08-07T11:18:07.666359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:18:04.886395Z digest=sha256:a2c75d34b188ac73f09bf0a64dbcd73ac6c179bd01949339f53cb88c7456566b

Observation 9380d697-33e0-47d7-8aa8-f39f48fe80e4 · outbound

This paper cites Defining and characterizing reward gaming.Advances in Neural Information Processing Systems, 35:9460– 9471, 2022.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Defining and characterizing reward gaming.Advances in Neural Information Processing Systems, 35:9460– 9471, 2022

Reference 22

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source=pdf_text observed=2026-08-07T11:18:04.937273Z digest=sha256:2a1170ca6776ab9a08b89bd315ecb3cd0f9ec0181f94404e9265c179750ca345

Observation ff8d6a03-ecc2-4516-9809-9da4766a8a65 · outbound

This paper cites Attacks and defenses for generative diffusion models: A comprehensive survey.ACM Computing Surveys, 57(8):1–44, 2025.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Attacks and defenses for generative diffusion models: A comprehensive survey.ACM Computing Surveys, 57(8):1–44, 2025

Reference 23

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raw_fallback, observed 2026-08-07T11:18:07.493436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:18:04.989332Z digest=sha256:0d859fed98146c72eab879900caa5ffc14bdd386aa046d69bfd8e8bd5fda5ef5

Observation c35545e0-c05a-4570-9d41-3a9605c58095 · outbound

This paper cites Rlhfpoi- son: Reward poisoning attack for reinforcement learning with human feedback in large language models.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Rlhfpoi- son: Reward poisoning attack for reinforcement learning with human feedback in large language models

Reference 24

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verified fuzzy
raw_fallback, observed 2026-08-07T11:18:07.285820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:18:05.061206Z digest=sha256:bdc64d69b05cfa27e9efff79c3e474797ebd83c361537390b0dd30805b9f4064

Observation d7e337ca-da47-4492-90f3-2a6be172ac8c · outbound

This paper cites Preference Poisoning Attacks on Reward Model Learning.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Preference Poisoning Attacks on Reward Model Learning

Reference 25

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source=pdf_text observed=2026-08-07T11:18:05.136617Z digest=sha256:d0b53eab8c19d6613280dfda01d6cf9010a66e67949ca8d6c40f44d654d1c9d4

Observation 6c07524e-4b2d-4435-b510-47ce1c7e16e8 · outbound

This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 26

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source=pdf_text observed=2026-08-07T11:18:05.209176Z digest=sha256:b07d74ed7fd4537c4037bd6e551257d766481739f2a123bc048b5e29b9afacf5

Observation 5955355f-aeb3-4f18-a634-ce49693845f4 · outbound

This paper cites Human preference score: Better aligning text-to-image models with human preference.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Human preference score: Better aligning text-to-image models with human preference

Reference 27

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source=pdf_text observed=2026-08-07T11:18:05.279581Z digest=sha256:ab6828e7129b67e42bdf4f17555b57fb156b816c99c885a4388a9d07d243359a

Observation 180fd271-a4cc-4700-9b6f-c017fbcedf9d · outbound

This paper cites Adversarial label flips attack on support vector machines.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Adversarial label flips attack on support vector machines

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-07T11:18:07.088382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:18:05.358874Z digest=sha256:3fdd4f00b70947d259a3b7ee95f59a1e1deb0ef3b0e86d4c3d2be5793e0c6245

Observation fd29907a-9f14-49c8-b5f2-e187b32ce0d1 · outbound

This paper cites Imagereward: Learning and evaluating human preferences for text-to-image generation.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Imagereward: Learning and evaluating human preferences for text-to-image generation

Reference 29

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no resolver link, observed 2026-08-07T11:18:05.433869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:18:05.433869Z digest=sha256:02e9631ae65cdf0d40e35aff9041bdc73e72388acf8f2768c4f7e5519ace5dce

Observation 859bd800-3a36-4ae6-9149-195f24f1cdce · outbound

This paper cites Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models

Reference 30

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source=pdf_text observed=2026-08-07T11:18:05.514154Z digest=sha256:62e8557f6e45fec10f7eacaff7b2532e3c2b9edd32296252f316201ff22e8a7b

Observation 3e6ebbea-9cba-46fe-a5b7-8c55c5a571cc · outbound

This paper cites Using human feedback to fine-tune diffusion models without any reward model.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Using human feedback to fine-tune diffusion models without any reward model

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-07T11:18:06.916979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:18:05.550741Z digest=sha256:7301356cf0237d4ec70ad14205c94b27c75642408bfa2de81bcc9d49099f9c47

Observation 65970768-5df4-4d28-8b38-2231aaf7b64e · outbound

This paper cites Diffusion models: A comprehensive survey of methods and applications.ACM Computing Surveys, 56(4):1–39, 2023.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Diffusion models: A comprehensive survey of methods and applications.ACM Computing Surveys, 56(4):1–39, 2023

Reference 32

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no resolver link, observed 2026-08-07T11:18:05.607651Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:18:05.607651Z digest=sha256:9e0715ed03d70934f6a980305508b3bf7838075c4029ee58e2c4663e71c99eea

Observation 3442542c-30c4-4352-af91-c631d8821e31 · outbound

This paper cites Poisonprompt: Backdoor attack on prompt-based large language models.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Poisonprompt: Backdoor attack on prompt-based large language models

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:18:06.732475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:18:05.679279Z digest=sha256:ecf37635ff545805184e6e5a19a82567e3f3335751801a2c20acf78fa4a529ae

Observation 0341263c-6b8a-4661-8ce4-95d8a6a8b82d · outbound

This paper cites Text- to-image diffusion models can be easily backdoored through multimodal data poisoning.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Text- to-image diffusion models can be easily backdoored through multimodal data poisoning

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T11:18:05.729802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:18:05.729802Z digest=sha256:e035cf8b312c1dbcf3fe41300f5c6667ba40799b44e2586e06aef3de48b77f5b

Observation e7e926ed-4c2b-437d-bc93-608708a72727 · outbound

This paper cites Text-to-image Diffusion Models in Generative AI: A Survey.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Text-to-image Diffusion Models in Generative AI: A Survey

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-07T11:18:05.800563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:18:05.800563Z digest=sha256:7b85295f68ee36b9aab0464cdd506105fadc82f78382c7bfbebadc188f822653

Observation 2b07a20c-d990-48b9-a212-dd7b6110fd25 · outbound

This paper cites Aligning few-step diffusion models with dense reward difference learning.arXiv preprint arXiv:2411.11727, 2024.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Aligning few-step diffusion models with dense reward difference learning.arXiv preprint arXiv:2411.11727, 2024

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T11:18:05.875543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:18:05.875543Z digest=sha256:9100830cb6a9783a005fd372dc1fce2c82502a6428d67ae3f8b150de469bf89e

Observation e09bfbe4-338b-43b7-8659-9d26de7da513 · outbound

This paper cites Shielding collaborative learning: Mitigating poisoning attacks through client-side detection.IEEE Transactions on Dependable and Secure Computing, 18(5):2029–2041, 2020.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Shielding collaborative learning: Mitigating poisoning attacks through client-side detection.IEEE Transactions on Dependable and Secure Computing, 18(5):2029–2041, 2020

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:18:06.433447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:18:05.936387Z digest=sha256:e6471bc3b497f48053d9e6ff1baf3bdd3d1c1360b023c43cc1ab84a70bb811fe

Observation e9268e5d-894a-41da-860e-3e09c4e33196 · outbound

This paper cites Diffusion Models for Reinforcement Learning: A Survey.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Diffusion Models for Reinforcement Learning: A Survey

Reference 38

Resolution
malformed identifier
no resolver link, observed 2026-08-07T11:18:05.968907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:18:05.968907Z digest=sha256:7d6f8c699fd3b20b74eca152ebdee38c80aef5e3cd2704371c756cdd27428719

Observation c89c4aad-343c-4484-8241-cfbb1765f46e · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:18:06.292128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:18:06.054981Z digest=sha256:8ae4cef4e3a3866e5476635119c58a34db6653cbb1b7e62e9811d7447e7166cf

Pith citing papers

Observation 4b6439e1-8155-4c28-8686-855b5bee67a9 · inbound

Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges cites this paper.

Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF

Reference 221

Resolution
verified exact
arxiv_id, observed 2026-05-10T14:00:28.455819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T13:58:53.430492Z digest=sha256:799fcc58b191254ce870896683c54a043560a99100779ae2d480a9704d5bc5fd

Observation b58c985f-04cf-476f-ab99-e55e1c7a5c69 · inbound

Power Reinforcement Post-Training of Text-to-Image Models with Super-Linear Advantage Shaping cites this paper.

Power Reinforcement Post-Training of Text-to-Image Models with Super-Linear Advantage Shaping BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:16:29.437150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-12T03:33:40.994346Z digest=sha256:aa6321c2e3bf69f6e31ffa009ae4c385211fb90f5cadabd09ca6661615b8c7b5

Observation 2607aec9-0022-4ee2-95b0-b7697c054fc0 · inbound

Proxy Reward Internalization and Mechanistic Exploitation: A Learned Precursor to Reward Hacking and Its Generalization cites this paper.

Proxy Reward Internalization and Mechanistic Exploitation: A Learned Precursor to Reward Hacking and Its Generalization BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF

Reference 295

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T01:37:30.523466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-27T16:26:34.918099Z digest=sha256:6a170433f00805ecc68ead911937a424f4affe1103104454ae837d4d6301e073