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Paper Citation Record · LEDGER

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?

As of 13 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 1 inbound Pith citation observation for arXiv:2505.16915.

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

pith.paper-citation-record.v1
2505.16915 v3

Coverage vector

measured 72 of 72 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:55:32.127569Z

measured 73 of 73 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T04:22:19.645747Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T12:01:03.744330Z

Reference resolution

72 of 72 outbound references displayed

  • verified exact1
  • verified fuzzy25
  • unresolved45
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b8deb0b7-dfd9-464d-9da3-a7b1cbd601d2 · outbound

This paper cites write newline.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? write newline

Reference 1

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unresolved
no resolver link, observed 2026-08-07T14:55:25.236529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:25.236529Z digest=sha256:7e4d21a8193cb56434c2004864b238285975b9a86c3fa59183467e55128a3226

Observation 4498b290-d28e-4c13-8b66-d28935da35c7 · outbound

This paper cites GPT-4 Technical Report.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? GPT-4 Technical Report

Reference 3

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unresolved
no resolver link, observed 2026-08-07T14:55:25.342327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:25.342327Z digest=sha256:d06a2c0cbc0b6b5c8aa180ae4172c6addd873a51343046a13bd91ba0b2d39365

Observation f0842ee5-ed83-4c63-b60e-84833b0e07c8 · outbound

This paper cites DeepFloyd IF : a novel state-of-the-art open-source text-to-image model with a high degree of photorealism and language understanding.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? DeepFloyd IF : a novel state-of-the-art open-source text-to-image model with a high degree of photorealism and language understanding

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:38.220244Z

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=arxiv_source observed=2026-08-07T14:55:25.433566Z digest=sha256:cba9a9b0e4b2dd3243053b5752eea05b4a66f92594a3b72ed7f9a891059b5625

Observation a4557e7d-0c77-40f5-bf90-7a8f4f35506c · outbound

This paper cites Hrs-bench: Holistic, reliable and scalable benchmark for text-to-image models.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Hrs-bench: Holistic, reliable and scalable benchmark for text-to-image models

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:38.055478Z

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=arxiv_source observed=2026-08-07T14:55:25.519668Z digest=sha256:08f52b35c724ce0d58f6f2f3d7750fc4a43420d1e0f0e6f51c9b9a53e8310c36

Observation 651e8506-075d-4375-88e3-58bc10c3913d · outbound

This paper cites Improving image generation with better captions.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Improving image generation with better captions

Reference 6

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unresolved
no resolver link, observed 2026-08-07T14:55:25.597506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:25.597506Z digest=sha256:40a6b51471662a7ad9aedbe3f4daf98275777efc902b23c8fb7a24b41a9c7575

Observation d2fa7e38-cfa9-4415-b6cf-853663cb796a · outbound

This paper cites A survey of ai-generated content (aigc).

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? A survey of ai-generated content (aigc)

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:37.868801Z

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=arxiv_source observed=2026-08-07T14:55:25.700744Z digest=sha256:146ccd68f637340929d5bd1e962c925f9cf6d52dcb3b952763ca991f2e866bf0

Observation a83e2d2d-7547-4196-af5c-909f41291762 · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 8

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unresolved
no resolver link, observed 2026-08-07T14:55:25.758652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:25.758652Z digest=sha256:38463cc7d544e30b336f2d371598f79c234bb6b1647560294a3b72eafdb38bef

Observation b4519c43-82da-4928-9b58-f0d06a162b60 · outbound

This paper cites Data-juicer 2.0: Cloud-scale adaptive data processing for and with foundation models.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Data-juicer 2.0: Cloud-scale adaptive data processing for and with foundation models

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:37.690180Z

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=arxiv_source observed=2026-08-07T14:55:25.856431Z digest=sha256:bccc2a4f4b8d648301dd607a85c43ed3092f848e40b6b12e9d7fd574c6d71a47

Observation fd7d9d22-bb62-4c69-9316-d6aa7ee9793e · outbound

This paper cites Data-juicer sandbox: A feedback-driven suite for multimodal data-model co-development.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Data-juicer sandbox: A feedback-driven suite for multimodal data-model co-development

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:37.517849Z

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=arxiv_source observed=2026-08-07T14:55:25.923136Z digest=sha256:341c676749213d557548001ddcfe3624ec2bac7345850f1f4e0dd9a83fe895f2

Observation 13a8bef6-d4c6-4bb7-9e4f-450488504528 · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 11

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unresolved
no resolver link, observed 2026-08-07T14:55:25.989364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:25.989364Z digest=sha256:a0f18fe007fc69c4c1a4ca4717e603a69531a45c4f4916411e846530efc2dfe6

Observation 70c08de5-668e-4f95-856b-e23380c9e598 · outbound

This paper cites Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T14:55:26.052578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:26.052578Z digest=sha256:549138a5dfd8567e7937d1268dabc8667ae0fdba91f1d637e3ff8674ee8ac4ae

Observation 85f93c2a-e2e8-4692-8afb-1728d3548073 · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 13

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unresolved
no resolver link, observed 2026-08-07T14:55:26.176849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:26.176849Z digest=sha256:5528e9ce76c9725019fb44dcc90cf3f96b633bfb4094f566b48e539db970c59c

Observation 94de4e54-9fd9-4681-82ff-b7f38d7b989b · outbound

This paper cites Diffsynth: Latent in-iteration deflickering for realistic video synthesis.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Diffsynth: Latent in-iteration deflickering for realistic video synthesis

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:37.291485Z

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=arxiv_source observed=2026-08-07T14:55:26.238858Z digest=sha256:551569b748f4aee1fbf09a306b5233038d692265103ca65b96732075911c288e

Observation fc602498-e7b0-4e83-a5d0-f65b21eebc1c · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Scaling rectified flow transformers for high-resolution image synthesis

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-07T14:55:26.243518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:26.243518Z digest=sha256:46caf98562615f9e9151f5a96b3a61f627f7d6ec436549cde9523acccba7e0b5

Observation 18f1ba49-3f16-4ae5-af57-72a4be1e6706 · outbound

This paper cites nsfw-image-detection.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? nsfw-image-detection

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:37.134203Z

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=arxiv_source observed=2026-08-07T14:55:26.280885Z digest=sha256:c1fdc1f939b8295ceb059511e8b9073b39aaa61f01dfea4a1f217b270f6176d6

Observation 36d99a75-cc11-4bfa-9750-bf4d522af339 · outbound

This paper cites LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T14:55:26.445655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:26.445655Z digest=sha256:e968797c7965eb4471eb155415e3e7b2005e90e2aac3359f9bf4a6eb8aa4c77e

Observation dcc1c580-9363-4814-a25a-deff32e7e82f · outbound

This paper cites Generative adversarial nets.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Generative adversarial nets

Reference 18

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unresolved
no resolver link, observed 2026-08-07T14:55:26.527836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:26.527836Z digest=sha256:178cb2b856b1ea34702badfcc4bcc6148cc1cf90bc641d94b88226ca1caad0a0

Observation 9663f881-1aad-40f0-98b6-da6612525720 · outbound

This paper cites Gemini 2.0 flash image generation.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Gemini 2.0 flash image generation

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:36.963214Z

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=arxiv_source observed=2026-08-07T14:55:26.698490Z digest=sha256:9d2cfb6a7abcd524f86a8fcf8253271932e87b639caf4c88f2562bdb790b5fe4

Observation 5ddb6e81-0817-4e4c-b02d-47951ebf0898 · outbound

This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 20

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unresolved
no resolver link, observed 2026-08-07T14:55:26.882061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:26.882061Z digest=sha256:60c002af6f0670900f62f6268678f6a58faf09cd9fbcf1f677d38b63e981a4bd

Observation e33d1df4-9838-4c80-8e85-90391aebdd27 · outbound

This paper cites Denoising diffusion probabilistic models.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Denoising diffusion probabilistic models

Reference 21

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unresolved
no resolver link, observed 2026-08-07T14:55:27.012502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:27.012502Z digest=sha256:7596a43ec2742a052636136aaf1cee641bce3982be91a34476af9cb3060b9186

Observation d601b964-d16a-4078-8aef-6441b89316ad · outbound

This paper cites ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-07T14:55:27.234325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:27.234325Z digest=sha256:a38996c3ab240c5fd69cf2cbce8396832164104f627763e9c6c48654eaa5f6d6

Observation 376db6c2-17d9-4866-97e1-94d7b530cce0 · outbound

This paper cites Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:36.778465Z

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=arxiv_source observed=2026-08-07T14:55:27.337676Z digest=sha256:726cb0d17e31eab73605c0589dcf4e2531cd9bb583dc94d6469404c097b95a21

Observation 5745046d-35de-419c-8175-f543fb3fc1a3 · outbound

This paper cites T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T14:55:27.465217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:27.465217Z digest=sha256:6424b16e3119a6d989259aa7fee37d1e56d4ab8b2d7ea1829e9023aecf8bad06

Observation 340e6967-515d-4845-a9c9-69ae3e845280 · outbound

This paper cites Visual storytelling.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Visual storytelling

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:36.569719Z

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=arxiv_source observed=2026-08-07T14:55:27.620511Z digest=sha256:78d61c220a4424458da26d4e21d3781ef0abf440fc22095cebc4a6283feee6a0

Observation fe3c505b-b2cd-46c9-9c04-aa50bc229374 · outbound

This paper cites From Training-Free to Adaptive: Empirical Insights into MLLMs' Understanding of Detection Information.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? From Training-Free to Adaptive: Empirical Insights into MLLMs' Understanding of Detection Information

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T14:55:27.700565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:27.700565Z digest=sha256:a822a2b036dde4fd2b74a257a129762f7741ddefbebd049172e4b302d31c2eae

Observation b3377342-8b73-4474-bc1e-cf5316b19167 · outbound

This paper cites Img-diff: Contrastive data synthesis for multimodal large language models.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Img-diff: Contrastive data synthesis for multimodal large language models

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:36.402490Z

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=arxiv_source observed=2026-08-07T14:55:27.775137Z digest=sha256:68e5319b1aeac6e935487efe44a9fbe480d6562979b0452a11ed3414203dc6a1

Observation 026e90cb-ef80-4780-a940-84c965a49dff · outbound

This paper cites an unresolved cited work.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Unresolved cited work

Reference 28

Resolution
unresolved
raw_fallback, observed 2026-08-07T14:55:36.167255Z

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=arxiv_source observed=2026-08-07T14:55:27.850025Z digest=sha256:478afa0bc703545cd491d93b743e6d6d302a2f86ceb43f6586cf33f6afd8ca10

Observation ace5e3e7-a0bc-432e-99c1-7798f52fe182 · outbound

This paper cites Natural language understanding and inference with mllm in visual question answering: A survey.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Natural language understanding and inference with mllm in visual question answering: A survey

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:35.935257Z

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=arxiv_source observed=2026-08-07T14:55:27.883581Z digest=sha256:4ea37ab1c4e8d93d1a1de0de97d69f1ecbfbac638a2d118906afe41f8f3e770d

Observation a5646d32-438b-4632-8c88-535bea381d69 · outbound

This paper cites an unresolved cited work.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Unresolved cited work

Reference 30

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unresolved
no resolver link, observed 2026-08-07T14:55:27.977541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:27.977541Z digest=sha256:5effce2d81465e4a3cdb46acf48597fa363001d7815f0236dfd335a3117b501e

Observation e98ab4f8-0e88-4dbe-a523-fdcadaff2faf · outbound

This paper cites Genai-bench: A holistic benchmark for compositional text-to-visual generation.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Genai-bench: A holistic benchmark for compositional text-to-visual generation

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:35.735158Z

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=arxiv_source observed=2026-08-07T14:55:28.067672Z digest=sha256:4faf271a416cc5a1a64fb14469210ea0e85847a22fb605a2126d75ad8d8c4ff0

Observation d8d60920-01de-4428-9bb3-3fe32c6d7d0c · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? LLaVA-OneVision: Easy Visual Task Transfer

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T14:55:28.147348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:28.147348Z digest=sha256:2e256855b4b4ee61bbad8555d990a5c7c2b092f541fc31ceccf2ba5e5726f0b0

Observation f4722c6c-9f1a-44ad-9e7b-9df5dcc26f57 · outbound

This paper cites mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-07T14:55:28.217225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:28.217225Z digest=sha256:ae80e8b05ec080ee51b8e97c69de11eaf84d10adc725e830219749d635faa60a

Observation b6258123-ccaa-4d7e-a9ff-7ac5b1aaea0b · outbound

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

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:35.562786Z

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=arxiv_source observed=2026-08-07T14:55:28.364215Z digest=sha256:bf1d3e0bc0a8ec0c788021a0720b374219946dbb3688b35097e73220747534fa

Observation c07fe08f-5bdd-4f1d-af29-d3907d40fbec · outbound

This paper cites Rich human feedback for text-to-image generation.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Rich human feedback for text-to-image generation

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:35.321901Z

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=arxiv_source observed=2026-08-07T14:55:28.435060Z digest=sha256:c6601ac6a3b9b7b80ab0b608bc02b5493576b82e79093342f2d2b4b6e27e9e14

Observation 54a39d1b-4b6e-4480-892a-abc9840a58df · outbound

This paper cites Microsoft coco: Common objects in context.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Microsoft coco: Common objects in context

Reference 36

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unresolved
no resolver link, observed 2026-08-07T14:55:28.521024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:28.521024Z digest=sha256:00f347d65e12b7edea6b6995c8d73e5effc3c61b0e486931a5f1179b62978f07

Observation 31d7a765-4198-4192-84a3-d8865643cad4 · outbound

This paper cites Flow Matching for Generative Modeling.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Flow Matching for Generative Modeling

Reference 37

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source=arxiv_source observed=2026-08-07T14:55:28.590688Z digest=sha256:98ae2e5cc0df8d542274adcbf546a03a7a87cece2294fa67f9a04a171cf93644

Observation a42b0a63-8dd3-4338-9b2b-71c18bb2a129 · outbound

This paper cites Improving Long-Text Alignment for Text-to-Image Diffusion Models.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Improving Long-Text Alignment for Text-to-Image Diffusion Models

Reference 38

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source=arxiv_source observed=2026-08-07T14:55:28.663859Z digest=sha256:4121cea38898c6f14af10648fbc45b8c7d4ba18ceb0d750500130b85bfe86c0b

Observation ec8865cc-1e92-4e2e-b08d-73f4ef025517 · outbound

This paper cites Llm4gen: Leveraging semantic representation of llms for text-to-image generation.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Llm4gen: Leveraging semantic representation of llms for text-to-image generation

Reference 39

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verified fuzzy
raw_fallback, observed 2026-08-07T14:55:35.084854Z

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=arxiv_source observed=2026-08-07T14:55:28.737852Z digest=sha256:f64e92d482b0d8ced626bac931fb1b5b360635f68d2feb90ea16f60f0896f184

Observation a3c041d6-74d3-4ca9-b9be-cd9eaf8daabc · outbound

This paper cites Browsing like human: A multimodal web agent with experiential fast-and-slow thinking.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Browsing like human: A multimodal web agent with experiential fast-and-slow thinking

Reference 40

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verified fuzzy
raw_fallback, observed 2026-08-07T14:55:34.922193Z

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=arxiv_source observed=2026-08-07T14:55:28.822579Z digest=sha256:93977a0a7150051172cecb0d9a585e225eee944824e8b12272fb15bc99a477d7

Observation ebea15ab-0fab-4db2-959a-4de94a0538e8 · outbound

This paper cites Automated flower classification over a large number of classes.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Automated flower classification over a large number of classes

Reference 41

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no resolver link, observed 2026-08-07T14:55:28.916410Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T14:55:28.916410Z digest=sha256:39e0e3660a2030f58012264e5826b694964b94052d404ede004140921d96e29f

Observation 7836a0a7-e510-4929-a79a-eeac3e22b4dd · outbound

This paper cites Docci: Descriptions of connected and contrasting images.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Docci: Descriptions of connected and contrasting images

Reference 42

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verified fuzzy
raw_fallback, observed 2026-08-07T14:55:34.685316Z

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=arxiv_source observed=2026-08-07T14:55:29.015512Z digest=sha256:68c43f7cf452e16a4e905945f335a6ab20e2e8e974bc5b039a73edbbefdd23bd

Observation 799a5789-e1a5-4527-9d4b-59f31b1c951c · outbound

This paper cites Gpt image-1.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Gpt image-1

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:34.472643Z

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=arxiv_source observed=2026-08-07T14:55:29.112322Z digest=sha256:6e1a6cb904205ce58e0a485c44270101df1f491361c2dd7163538dac92c690b7

Observation 847789b4-1643-4243-a9a9-46d16b3f80f6 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 44

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source=arxiv_source observed=2026-08-07T14:55:29.176831Z digest=sha256:d2b474c0c5e369f7321ecd843ee0d785acc10ec780becb16de76b801baeaf95b

Observation bdd8fa48-2e44-4616-a173-271eac2279ed · outbound

This paper cites Connecting vision and language with localized narratives.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Connecting vision and language with localized narratives

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:34.272214Z

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=arxiv_source observed=2026-08-07T14:55:29.303856Z digest=sha256:535196242c47ff2a20f8a5a7b7dcf06014caa9e132b49390b421e40c7ed6331d

Observation 88b6dadb-6586-4801-9d4f-6adab5c7c8d8 · outbound

This paper cites The synergy between data and multi-modal large language models: A survey from co-development perspective.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? The synergy between data and multi-modal large language models: A survey from co-development perspective

Reference 46

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no resolver link, observed 2026-08-07T14:55:29.375398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:29.375398Z digest=sha256:b6d5d263040087cbccfc1b50a62f04d9478ff41e4b1470e76c55c83101c2d6a1

Observation c0a9b799-f4f9-4374-9ce0-686c4e6f1026 · outbound

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

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Learning transferable visual models from natural language supervision

Reference 47

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no resolver link, observed 2026-08-07T14:55:29.453485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:29.453485Z digest=sha256:059dd9dcf42c7868b9a6c604c30917aab5b1cc4268b21ec1775640739a203c25

Observation 0e87bdd9-aa69-4edd-b4ac-a88a34bff960 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 48

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no resolver link, observed 2026-08-07T14:55:29.545971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:29.545971Z digest=sha256:44309278d4ba1b9e317f8d313632d2bc94584b28395ae73716068cbb38bd5d63

Observation 6a18da4a-a3bd-4b14-8136-bef38c15809d · outbound

This paper cites Zero-shot text-to-image generation.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Zero-shot text-to-image generation

Reference 49

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no resolver link, observed 2026-08-07T14:55:29.616410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:29.616410Z digest=sha256:7d4edf169d57ab46aa6193799dc7d8e47b3eca490766623b53cc5dcbcbe92fa5

Observation f33bc5ee-1b2f-4aa3-9137-ff085641cb8d · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 50

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no resolver link, observed 2026-08-07T14:55:29.680330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:29.680330Z digest=sha256:07556e2944e8d86c78ed42c99b55e24f8a3711822c8ee1abdfd756e5df0e0982

Observation 832c8e8c-ba0c-4335-9ec4-f8c2fa9bd9b6 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? High-resolution image synthesis with latent diffusion models

Reference 51

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no resolver link, observed 2026-08-07T14:55:29.766420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:29.766420Z digest=sha256:675286d1d17ebba91f7c94c2b30dbab553d0cac0826d7075e4e7d75d99da7375

Observation c90e7de4-d2c4-40c4-a356-8e9eee85db9d · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Photorealistic text-to-image diffusion models with deep language understanding

Reference 52

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no resolver link, observed 2026-08-07T14:55:29.865585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:29.865585Z digest=sha256:0b1d8bb783ee66b8c67ec1b1b55589b730b2e68bbaee5b94b7f879da4ab6d939

Observation 24284bcc-236a-49c8-ba13-ad16aab445ac · outbound

This paper cites Qwen2.5: A party of foundation models, September 2024.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Qwen2.5: A party of foundation models, September 2024

Reference 53

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no resolver link, observed 2026-08-07T14:55:29.955998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:29.955998Z digest=sha256:e949584000d62bf3e588e9ff6faae4d8d0c58d89ad9d38111bdb2c746edaee08

Observation 20e604a6-2e47-42a5-b3d4-d55e3474f228 · outbound

This paper cites Qwen2.5-vl, January 2025.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Qwen2.5-vl, January 2025

Reference 54

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no resolver link, observed 2026-08-07T14:55:30.108583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:30.108583Z digest=sha256:6178132e13645764c3e84032380471ea491339a10f98871c98bafba10848dce9

Observation bc85ca40-36c9-411c-999e-ef73ef931a32 · outbound

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

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 55

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no resolver link, observed 2026-08-07T14:55:30.161568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:30.161568Z digest=sha256:7da63d9211516f9155d708442f858b13ae25ae8d73b7129ad5bb01f61a69b2d1

Observation 2393ae33-faf9-40f2-810e-b4e652465405 · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? The caltech-ucsd birds-200-2011 dataset

Reference 56

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no resolver link, observed 2026-08-07T14:55:30.205142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:30.205142Z digest=sha256:c4ec399a75070423fd81c69795724c6a0fe2566efc43fbd7197a6d3052252204

Observation 6fdd3748-9ce4-435a-9085-ae2758737043 · outbound

This paper cites Yoloe: Real-time seeing anything.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Yoloe: Real-time seeing anything

Reference 57

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no resolver link, observed 2026-08-07T14:55:30.315937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:30.315937Z digest=sha256:d30f63ae30cbcdffc8ac4e7d9d36bb2bbee38d536f8ec4acaf2c86074ae2c80e

Observation 475f24c2-1a02-417d-9cba-5689a313e5f6 · outbound

This paper cites Revisiting Text-to-Image Evaluation with Gecko: On Metrics, Prompts, and Human Ratings.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Revisiting Text-to-Image Evaluation with Gecko: On Metrics, Prompts, and Human Ratings

Reference 58

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no resolver link, observed 2026-08-07T14:55:30.485554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:30.485554Z digest=sha256:d8a037b00d044bf6a3b232df88c4ef21d8a55c02c9a5f23a034843915c5ca65f

Observation 75a52ecf-47e7-4b2b-a687-21ff57103e09 · outbound

This paper cites Integrating aigc with design: dependence, application, and evolution-a systematic literature review.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Integrating aigc with design: dependence, application, and evolution-a systematic literature review

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:33.955445Z

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=arxiv_source observed=2026-08-07T14:55:30.628688Z digest=sha256:e4f29e4b6586229153166b0ac519f0e320e6d6a3fce7262ae8d74296affe098f

Observation 6c71d2c5-22f8-424b-ac87-c8e8699b7c2f · outbound

This paper cites Paragraph-to-Image Generation with Information-Enriched Diffusion Model.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Paragraph-to-Image Generation with Information-Enriched Diffusion Model

Reference 60

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:55:32.556802Z

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=arxiv_source observed=2026-08-07T14:55:30.742887Z digest=sha256:2ed4980f8be072b06e4b689278c54e70ec8f8cb34685c3d811b7f11a5a32cc37

Observation 7310ce93-a0cd-4ba1-8456-37d18132868f · outbound

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

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 62

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:30.880719Z digest=sha256:d23ee21bd5c99fae97dec461c3fbdecf83666cb36d45efcd67f363b4b9c7d66c

Observation e86b5052-4331-46b5-b4aa-667881abd215 · outbound

This paper cites Ai-generated content for academic visualization and communication in maker education.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Ai-generated content for academic visualization and communication in maker education

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:33.766597Z

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=arxiv_source observed=2026-08-07T14:55:30.977005Z digest=sha256:c525e7884324d54de7118fe95688041d32f927d76877f15091d2de50c35c37b5

Observation 777260f2-95bd-41c6-b8a2-9070e44a3889 · outbound

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

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Imagereward: Learning and evaluating human preferences for text-to-image generation

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:33.557281Z

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=arxiv_source observed=2026-08-07T14:55:31.138577Z digest=sha256:c0bb8b90f47e147549b61615e7a0c769835fc6af3842fe48c3e7ceb36a8b4234

Observation 93500081-3e27-4364-b17d-349786e9f3bb · outbound

This paper cites Mindgym: What matters in question synthesis for thinking-centric fine-tuning? In NeurIPS, 2025.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Mindgym: What matters in question synthesis for thinking-centric fine-tuning? In NeurIPS, 2025

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:33.352502Z

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=arxiv_source observed=2026-08-07T14:55:31.270697Z digest=sha256:2023706822beab437fd31a799bcd09c590e9d533aa5c2db3f405b565fcd732bc

Observation ec8c97eb-28ee-49ab-aea7-b5128f283d87 · outbound

This paper cites From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions

Reference 66

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no resolver link, observed 2026-08-07T14:55:31.424327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:31.424327Z digest=sha256:921e70dfc75f2de2602d7e2d9d915075690ec9d0c39a8a8fba4a0e7ab51dd8c3

Observation 78e39d37-354c-4b90-8b8a-310c159b4d0f · outbound

This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 67

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no resolver link, observed 2026-08-07T14:55:31.570990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:31.570990Z digest=sha256:ff47fc7c39aca49d1a2f1842254c5335ef7c8f1f6d8bd436d0565028e679b064

Observation 0c89e2fb-f098-4e1f-b590-f1e16dab8d27 · outbound

This paper cites Learning multi-dimensional human preference for text-to-image generation.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Learning multi-dimensional human preference for text-to-image generation

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:55:33.160463Z

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=arxiv_source observed=2026-08-07T14:55:31.661280Z digest=sha256:9bd90b3ee2803cc6eddb754842428f85d4c888c1e9fecd66bf103bbbd1ec99cb

Observation 75def250-758c-4cb5-8ddf-c262e8bf8340 · outbound

This paper cites HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized Benchmarks.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized Benchmarks

Reference 69

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no resolver link, observed 2026-08-07T14:55:31.757051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:31.757051Z digest=sha256:ff25e3f98221dadb81fedf584740f34b0b73c29195bb0d907dc9b6c6627d59ca

Observation e0be45fa-a2ce-4835-a6c3-ca0b0df87e13 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 70

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unresolved
no resolver link, observed 2026-08-07T14:55:31.815809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:31.815809Z digest=sha256:dd630abb464f9965424391c64499fa495253f8a988317fd9cf96f876ec78147d

Observation 9886c18f-904d-4ce6-b7fa-422291e00bf2 · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 71

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unresolved
no resolver link, observed 2026-08-07T14:55:31.877954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:31.877954Z digest=sha256:cc65c6abb6e0ddf5accf86832d73443501c375b79742df810f2a951abbcd232d

Observation 30ba720a-8d0a-4e45-a39c-377cd3a76e7b · outbound

This paper cites @esa (Ref.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? @esa (Ref

Reference 72

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unresolved
no resolver link, observed 2026-08-07T14:55:31.966273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:31.966273Z digest=sha256:cd7d7ff3415274ffe49b16238b264dbeeed90cce6b8a0721786d597cf4a11279

Observation 4ae61024-0f7d-451f-b140-e12d019141ad · outbound

This paper cites an unresolved cited work.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Unresolved cited work

Reference 73

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unresolved
no resolver link, observed 2026-08-07T14:55:32.044986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:32.044986Z digest=sha256:a6fc82b7b6f7f0158914beaf034c778b79fb6bb1d07add0c6c9101ca0f531dd1

Observation bcf72c9f-a956-4dad-9e6d-346bb6bc02d4 · outbound

This paper cites an unresolved cited work.

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts? Unresolved cited work

Reference 74

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malformed identifier
no resolver link, observed 2026-08-07T14:55:32.127569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:32.127569Z digest=sha256:8f178c22fef0bb9a880532134ee226e40e0defb55e24f6dea678b371fb01c8ef

Pith citing papers

Observation 195a35c4-a2a8-4831-9f81-b7e23fe20712 · inbound

Long-Text-to-Image Generation via Compositional Prompt Decomposition cites this paper.

Long-Text-to-Image Generation via Compositional Prompt Decomposition DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-06-02T03:03:59.454800Z

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=arxiv_source observed=2026-05-10T04:22:19.645747Z digest=sha256:205b611da3aca50a02a9e393f00dd5662a738a8144b011fb4cf8a01cbd7536f0