Paper Citation Record · LEDGER

VersusDebias: Universal Zero-Shot Debiasing for Text-to-Image Models via SLM-Based Prompt Engineering and Generative Adversary

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.19524.

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

pith.paper-citation-record.v1
2407.19524 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 2026-06-29T09:01:30.275840Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T09:03:15.658878Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c63df41b-99a3-4253-bdd9-a110af1605af · inbound

SynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction cites this paper.

SynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction VersusDebias: Universal Zero-Shot Debiasing for Text-to-Image Models via SLM-Based Prompt Engineering and Generative Adversary

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:36:22.379022Z

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 9575bd38-b29e-47c1-8cb3-b76ac48284f5 · inbound

BiasIG: Benchmarking Multi-dimensional Social Biases in Text-to-Image Models cites this paper.

BiasIG: Benchmarking Multi-dimensional Social Biases in Text-to-Image Models VersusDebias: Universal Zero-Shot Debiasing for Text-to-Image Models via SLM-Based Prompt Engineering and Generative Adversary

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T10:21:00.614747Z

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 2856c078-5032-4eb0-b6d3-b2a1eb4b247d · 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 VersusDebias: Universal Zero-Shot Debiasing for Text-to-Image Models via SLM-Based Prompt Engineering and Generative Adversary

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T11:36:02.344077Z

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 403d618f-7e70-44c4-a8d0-7f6a6b7bb052 · inbound

Embedding Arithmetic: A Lightweight, Tuning-Free Framework for Post-hoc Bias Mitigation in Text-to-Image Models cites this paper.

Embedding Arithmetic: A Lightweight, Tuning-Free Framework for Post-hoc Bias Mitigation in Text-to-Image Models VersusDebias: Universal Zero-Shot Debiasing for Text-to-Image Models via SLM-Based Prompt Engineering and Generative Adversary

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T05:20:54.829028Z

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 fa6112ff-8456-4a69-9e13-2ec60d2481aa · inbound

Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption cites this paper.

Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption VersusDebias: Universal Zero-Shot Debiasing for Text-to-Image Models via SLM-Based Prompt Engineering and Generative Adversary

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T09:03:15.660760Z

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.