Paper Citation Record · LEDGER

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models

As of 13 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2501.01973.

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

pith.paper-citation-record.v1
2501.01973 v3

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:48:02.423535Z

measured 28 of 28 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 2026-05-10T13:47:12.605699Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T11:36:02.375165Z

Reference resolution

27 of 27 outbound references displayed

verified exact0
verified fuzzy18
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5d6d3624-5aef-4989-83f9-d5bf9dc1a887 · outbound

This paper cites https://huggingface.co/touchtech/fashion-images- gender-age-vit-large-patch16-224-in21k-v3.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models https://huggingface.co/touchtech/fashion-images- gender-age-vit-large-patch16-224-in21k-v3

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.907664Z

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.

Observation fe3f6b1b-df76-4f87-9872-f95824446f87 · outbound

This paper cites https://huggingface.co/prompthero/openjourney-v4.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models https://huggingface.co/prompthero/openjourney-v4

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.887195Z

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.

Observation b44b5880-6767-4371-b417-0589e602f37c · outbound

This paper cites https://huggingface.co/SG161222/Realistic_ Vision_V5.1_noVAE.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models https://huggingface.co/SG161222/Realistic_ Vision_V5.1_noVAE

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.867574Z

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.

Observation 1ab8cda6-ee15-4bba-9112-62275e96494e · outbound

This paper cites https://huggingface.co/SG161222/Realistic_ Vision_V6.0_B1_noVAE.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models https://huggingface.co/SG161222/Realistic_ Vision_V6.0_B1_noVAE

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.848105Z

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.

Observation 454bbbc2-e37b-47b7-803b-9e5ab8a7f2fb · outbound

This paper cites Ai fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Ai fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.830961Z

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.

Observation c821ad37-4f10-44c8-a1c3-8d18e81b318e · outbound

This paper cites Easily accessible text-to-image generation amplifies demographic stereotypes at large scale.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Easily accessible text-to-image generation amplifies demographic stereotypes at large scale

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.813614Z

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.

Observation e874269d-6cb1-47a6-808e-9551252517d3 · outbound

This paper cites Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.795999Z

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.

Observation ecfdd8d1-70e6-49bb-84bd-fff737539346 · outbound

This paper cites Bias and fairness in large language models: A survey.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Bias and fairness in large language models: A survey

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T23:48:02.313813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8b730c0-eb2b-4ad7-bf6f-e5970280d0d7 · outbound

This paper cites Gen- erative adversarial networks.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Gen- erative adversarial networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.768342Z

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.

Observation 434dfd69-ab49-4f3b-85cc-3ab747b38ea3 · outbound

This paper cites The Pursuit of Fairness in Artificial Intelligence Models: A Survey.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models The Pursuit of Fairness in Artificial Intelligence Models: A Survey

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T23:48:02.324262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 154875a4-3854-4323-810b-0ebca87ab0b0 · outbound

This paper cites Flux.1 [schnell].

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Flux.1 [schnell]

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.751861Z

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.

Observation 4c9e8ca9-42a9-4804-ab64-7753ad340223 · outbound

This paper cites Holistic evaluation of text-to-image models.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Holistic evaluation of text-to-image models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.735323Z

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.

Observation aaf64c9c-8a63-44e6-abab-2941bf075d34 · outbound

This paper cites Holistic Evaluation of Language Models.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Holistic Evaluation of Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T23:48:02.339502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 164064b6-25fa-49c3-9038-76f913d30f81 · outbound

This paper cites SDXL-Lightning: Progressive Adversarial Diffusion Distillation.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models SDXL-Lightning: Progressive Adversarial Diffusion Distillation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T23:48:02.344920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81ce2491-c8db-45ee-abf6-76033b5b2104 · outbound

This paper cites Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T23:48:02.350039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6bae0c3-5fb2-41f6-84af-9a6df7692ebf · outbound

This paper cites A survey on bias and fairness in machine learning.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models A survey on bias and fairness in machine learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T23:48:02.355616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36d2e165-3a1b-470f-a37d-f32d623515ee · outbound

This paper cites The monk skin tone scale.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models The monk skin tone scale

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.709158Z

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.

Observation 9e4c38fb-3558-4e14-8df8-b0195e29b5e4 · outbound

This paper cites Dall-e 3: The latest in text-to-image generation.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Dall-e 3: The latest in text-to-image generation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.693258Z

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.

Observation d224cbfa-d800-49e7-88e0-c9b9fd831ab2 · outbound

This paper cites Disentan- gling and operationalizing ai fairness at linkedin.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Disentan- gling and operationalizing ai fairness at linkedin

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.674864Z

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.

Observation 9fa24961-5587-48b9-a011-89d76ae1e9e2 · outbound

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

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Learning transferable visual models from natural language supervision

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T23:48:02.377605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 212b6261-20bd-4ee4-9d29-e8ac28b58ed7 · outbound

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

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Zero-shot text-to-image generation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.646363Z

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.

Observation 6ccb6f26-bf20-4311-95d4-8e03c1916755 · outbound

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

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models High-resolution image synthesis with latent diffu- sion models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.629501Z

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.

Observation ac6e2c4f-609c-4125-b471-48372f91d627 · outbound

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

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Photorealistic text-to-image diffu- sion models with deep language understanding

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.611746Z

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.

Observation 84f1f33e-90e8-426f-b43f-167b95fa7da8 · outbound

This paper cites Aequitas: A Bias and Fairness Audit Toolkit.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Aequitas: A Bias and Fairness Audit Toolkit

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T23:48:02.405353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84f42c27-4014-44c7-8915-756456583e99 · outbound

This paper cites Attngan: Fine-grained text to im- age generation with attentional generative adversarial networks.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Attngan: Fine-grained text to im- age generation with attentional generative adversarial networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.590826Z

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.

Observation 3b5862f0-521a-48bb-9464-57df2ad73499 · outbound

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

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T23:48:02.416943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a256fa6a-1800-4d14-b935-b9ef2c5cb726 · outbound

This paper cites Stackgan: Text to photo- realistic image synthesis with stacked generative adversarial networks.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models Stackgan: Text to photo- realistic image synthesis with stacked generative adversarial networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:48:02.570806Z

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.

Pith citing papers

Observation 7c82a621-d024-4441-baf7-972a26e4abbc · inbound

Operationalizing Fairness in Text-to-Image Models: A Survey of Bias, Fairness Audits and Mitigation Strategies cites this paper.

Operationalizing Fairness in Text-to-Image Models: A Survey of Bias, Fairness Audits and Mitigation Strategies INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:36:02.382761Z

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.