Paper Citation Record · LEDGER

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models

As of 8 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2502.09963.

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

pith.paper-citation-record.v1
2502.09963 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T20:00:34.112881Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T15:57:18.027043Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T03:45:55.789850Z

Reference resolution

44 of 44 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c368e677-c750-4c2b-ae17-3216044aa83d · outbound

This paper cites The k-means algorithm: A comprehensive survey and performance evaluation.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models The k-means algorithm: A comprehensive survey and performance evaluation

Reference 1

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Observation 33f523a0-98b8-4133-9064-41e8eba63293 · outbound

This paper cites Self-Consuming Generative Models Go MAD.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Self-Consuming Generative Models Go MAD

Reference 2

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Observation fae22346-2a2f-4042-9264-c0d8b43497b1 · outbound

This paper cites Improving image generation with better captions.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Improving image generation with better captions

Reference 3

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Observation fdb622c3-366a-46c2-9c3c-e3126a680dca · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Training Diffusion Models with Reinforcement Learning

Reference 4

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Observation 82abb510-7a03-47db-9d36-8a10b90e3d40 · outbound

This paper cites Teaching Large Language Models to Self-Debug.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Teaching Large Language Models to Self-Debug

Reference 5

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Observation 3c53cfe9-e6ab-40cf-ba45-03cd371b8b85 · outbound

This paper cites Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Reference 6

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Observation 3a797c1d-fd4f-403d-817f-45bc7a8339ae · outbound

This paper cites Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Reference 7

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Observation ca24fb60-f157-4093-be16-f7031acdc31f · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Imagenet: A large-scale hierarchical image database

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:00:35.131725Z

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Observation fab97c1a-4268-4b55-8dd0-ec03a7c28cd1 · outbound

This paper cites Strong Model Collapse.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Strong Model Collapse

Reference 9

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Observation a5747f36-f9ff-4ad1-bcfc-795c4b1c85e1 · outbound

This paper cites The Llama 3 Herd of Models.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models The Llama 3 Herd of Models

Reference 10

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Observation 04402fad-9878-4a6a-b5b9-1aa2a4306a2f · outbound

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

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:00:35.115484Z

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Observation bfe321a7-c5cb-4171-b8fd-ec9f9ee6dcf5 · outbound

This paper cites Re- inforcement learning for fine-tuning text-to-image diffusion models.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Re- inforcement learning for fine-tuning text-to-image diffusion models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:00:35.097770Z

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Observation d00b0985-d769-42b9-bbaf-1ffb923b7fc0 · outbound

This paper cites CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

Reference 13

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Observation 3341eeea-e802-4517-a37f-ae7a5c758281 · outbound

This paper cites Small language model can self-correct.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Small language model can self-correct

Reference 14

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Observation 9a905807-d0fc-414b-8248-6b21bb7f8cc4 · outbound

This paper cites Denoising diffu- sion probabilistic models.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Denoising diffu- sion probabilistic models

Reference 15

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Observation bbc08813-7a5f-4ccf-b99d-828e004cb705 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 16

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Observation d44190d7-5d02-41b1-b9d8-e9e06c9558f0 · outbound

This paper cites Analyzing and improving the image quality of stylegan.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Analyzing and improving the image quality of stylegan

Reference 17

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Observation 69dd244c-270d-417d-aaad-5e0fc3ce50f1 · outbound

This paper cites Pick-a-pic: An open dataset of user preferences for text-to-image generation.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:00:35.029950Z

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Observation 84927b07-be58-4b58-a5c6-665a2fca304b · outbound

This paper cites Self-refine: Itera- tive refinement with self-feedback.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Self-refine: Itera- tive refinement with self-feedback

Reference 19

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Observation 209c1360-4b4d-42f4-a4bd-288159a192dc · outbound

This paper cites SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning

Reference 20

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Observation f430fa6c-5e07-4d85-96b2-00075f02993f · outbound

This paper cites Bounded Recursive Self-Improvement.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Bounded Recursive Self-Improvement

Reference 21

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Observation fbd6dba3-46d8-4ee4-add1-4418a3308ede · outbound

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

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 22

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Observation e08b497f-039d-4dab-bb82-087f1f91537f · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Learn- ing transferable visual models from natural language super- vision

Reference 23

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Observation afe71fc6-f778-4101-b5cf-a8e8d1769745 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Direct preference optimization: Your language model is secretly a reward model

Reference 24

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Observation f8f06ac4-d3e5-4c68-ba28-d574088ba09a · outbound

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

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 25

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Observation a26c2bec-3aff-4868-9da3-d1f17d0ab5d7 · outbound

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

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models High-resolution image syn- thesis with latent diffusion models

Reference 26

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Observation 86f5cdbc-0da4-4605-9929-fd38fcd4e468 · outbound

This paper cites Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements

Reference 27

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Observation be3085af-5000-42fa-b7f3-420128618f10 · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 28

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Observation 20bab205-a213-4ec6-8214-29cf0601cdae · outbound

This paper cites Ai models collapse when trained on recursively generated data.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Ai models collapse when trained on recursively generated data

Reference 29

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Observation ec805e62-509c-4e59-8366-65cd5365ebfa · outbound

This paper cites Mastering the game of go with deep neu- ral networks and tree search.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Mastering the game of go with deep neu- ral networks and tree search

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:00:34.929138Z

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Observation 6a2610c7-9b86-4d72-86c8-814b22c72fd8 · outbound

This paper cites Growing recursive self-improvers.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Growing recursive self-improvers

Reference 31

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Observation 224a46e9-f344-426c-879a-5aa7a255aaeb · outbound

This paper cites A Survey on Self-Evolution of Large Language Models.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models A Survey on Self-Evolution of Large Language Models

Reference 32

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Observation 1a56e33a-c265-4aa7-83c7-c5c1bd1d1329 · outbound

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Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Diffusion model align- ment using direct preference optimization

Reference 33

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Observation eb70e71b-2b70-4c1a-a07a-2828b3fa0f4c · outbound

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Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Gener- ating sequences by learning to self-correct

Reference 34

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Observation 106a77f2-8e93-49fd-92d4-70a7e6434126 · outbound

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

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 35

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Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Human preference score: Better aligning text-to- image models with human preference

Reference 36

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Observation feb1a760-ffbd-4d27-8028-1c7f4bfa30c3 · outbound

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

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Imagere- ward: Learning and evaluating human preferences for text- to-image generation

Reference 37

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Observation bee6cf07-1a07-4ee0-ac37-3357751fa468 · outbound

This paper cites From Seed AI to Technological Singularity via Recursively Self-Improving Software.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models From Seed AI to Technological Singularity via Recursively Self-Improving Software

Reference 38

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Observation ffa997bb-2adb-4ecd-9567-aac8d1250015 · outbound

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

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Using human feedback to fine-tune diffusion models without any reward model

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:00:34.834158Z

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Observation 841c55ce-4a5c-40b1-9b5e-61b6c7470d7b · outbound

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

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 40

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Observation a5a17897-449f-4a99-9110-628f91f3f409 · outbound

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Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Self-Rewarding Language Models

Reference 41

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This paper cites Star: Bootstrapping reasoning with reasoning.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models Star: Bootstrapping reasoning with reasoning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:00:34.818659Z

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Observation f117fa3a-19a4-4adf-995f-28f17f209554 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models OPT: Open Pre-trained Transformer Language Models

Reference 43

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Observation f8d3e87c-a720-467e-ada1-6e1b6b167896 · outbound

This paper cites The source code can be accessed at https://open upon acceptance.

Generating on Generated: An Approach Towards Self-Evolving Diffusion Models The source code can be accessed at https://open upon acceptance

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:00:34.803594Z

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Pith citing papers

Observation 7a666be3-aca9-4c44-94e7-8e4deeced3a1 · inbound

Epistemic diversity across language models mitigates knowledge collapse cites this paper.

Epistemic diversity across language models mitigates knowledge collapse Generating on Generated: An Approach Towards Self-Evolving Diffusion Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-03T15:57:18.027043Z

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Observation 20fe14f5-d4f9-4d44-b1c7-e53e8f186d85 · inbound

Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops cites this paper.

Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops Generating on Generated: An Approach Towards Self-Evolving Diffusion Models

Reference 156

Resolution: verified exact; local_arxiv, observed 2026-07-09T03:45:55.791440Z

Source-reported events for the cited work

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