Paper Citation Record · LEDGER

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning

As of 22 August 2026, this Paper Citation Record lists 90 of 90 outbound references and 7 inbound Pith citation observations for arXiv:2504.16080.

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

pith.paper-citation-record.v1
2504.16080 v1

Coverage vector

measured 90 of 90 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:15:35.148600Z

measured 97 of 97 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:28:55.047871Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:18:57.711113Z

Reference resolution

90 of 90 outbound references displayed

verified exact1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1e8171b2-5cb6-4cd7-9e4d-157fe3e6d959 · outbound

This paper cites ToddlerDiffusion: Interactive Structured Image Generation with Cascaded Schr\"odinger Bridge.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning ToddlerDiffusion: Interactive Structured Image Generation with Cascaded Schr\"odinger Bridge

Reference 1

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Observation 8c7dfb10-a8f8-4c67-ab17-a151acaa46f5 · outbound

This paper cites A Noise is Worth Diffusion Guidance.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning A Noise is Worth Diffusion Guidance

Reference 2

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Observation e480dd82-e190-41b0-9619-532ec1dc1851 · outbound

This paper cites Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection

Reference 3

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Observation 39a844e5-c089-4867-b9f6-b88b6611a3e4 · outbound

This paper cites Qwen2.5-VL Technical Report.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Qwen2.5-VL Technical Report

Reference 4

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Observation fea99285-f968-487e-bac6-952d2a7f60f8 · outbound

This paper cites Improving image generation with better captions.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Improving image generation with better captions

Reference 5

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Observation ee5c38ad-ba63-439d-9688-b14187808446 · outbound

This paper cites Rank analysis of incomplete block designs: I.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Rank analysis of incomplete block designs: I

Reference 6

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Observation 1028c4ad-a919-4bab-8e8a-077e0912be56 · outbound

This paper cites In- structpix2pix: Learning to follow image editing instructions.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning In- structpix2pix: Learning to follow image editing instructions

Reference 7

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Observation 6450bf25-b34b-42e5-bcb0-9b7b6c8e0ee6 · outbound

This paper cites Getting it right: Improving spatial consistency in text-to-image models.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Getting it right: Improving spatial consistency in text-to-image models

Reference 8

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Observation af28a5f8-ad93-47ba-8f24-c20e8d0d9c08 · outbound

This paper cites Pixart- σ: Weak-to-strong training of diffusion transformer for 4k text-to-image generation.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Pixart- σ: Weak-to-strong training of diffusion transformer for 4k text-to-image generation

Reference 9

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Observation c3f6f87f-6583-4965-8069-28b8e40c1f03 · outbound

This paper cites Janus- pro: Unified multimodal understanding and generation with data and model scaling, 2025.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Janus- pro: Unified multimodal understanding and generation with data and model scaling, 2025

Reference 10

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Observation 958c3cda-8141-4bda-bc24-7f3518a74b4b · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 11

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Observation 7b80a61f-ea61-43f7-8f64-cc6c4f00c3f5 · outbound

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

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 12

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Observation 4e2c4003-3996-4161-8e02-189bb2881a20 · outbound

This paper cites Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

Reference 13

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Observation 31320448-b414-4c1e-8a06-c2befd291bc5 · outbound

This paper cites Lumina-t2x: Scalable flow-based large diffusion transformer for flexible resolution generation.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Lumina-t2x: Scalable flow-based large diffusion transformer for flexible resolution generation

Reference 14

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Observation 5ca5d434-c0c1-4b65-b7c5-08c02108994f · outbound

This paper cites Geneval: An object-focused framework for evaluating text- to-image alignment.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Geneval: An object-focused framework for evaluating text- to-image alignment

Reference 15

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Observation 73905dbd-0e28-4e4a-8db7-a7e752140022 · outbound

This paper cites Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step

Reference 16

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Observation 2760463c-02c1-434c-b20e-26b086e68a75 · outbound

This paper cites Glore: When, where, and how to improve llm reasoning via global and local refine- ments.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Glore: When, where, and how to improve llm reasoning via global and local refine- ments

Reference 17

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Observation 39f7410a-b19e-4e2a-9e25-c75307690447 · outbound

This paper cites CLIPScore: A reference-free evaluation metric for image captioning.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning CLIPScore: A reference-free evaluation metric for image captioning

Reference 18

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Observation 750620e5-3682-4549-80f0-af477dd28164 · outbound

This paper cites Denoising diffu- sion probabilistic models.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Denoising diffu- sion probabilistic models

Reference 19

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Observation 48ec63f6-c705-40c6-a197-ff02ac553d90 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Lora: Low-rank adaptation of large language models

Reference 20

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Observation d18c9e60-6f53-4282-b525-ebd66d225bd1 · outbound

This paper cites GPT-4o System Card.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning GPT-4o System Card

Reference 21

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Observation 3947da0a-0442-4803-8e46-2a326fe5134c · outbound

This paper cites Scaling Laws for Neural Language Models.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Scaling Laws for Neural Language Models

Reference 22

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This paper cites Lan- guage models can solve computer tasks.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Lan- guage models can solve computer tasks

Reference 23

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Observation d8419cac-2f43-4784-ae43-f9dda6e30326 · outbound

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

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Pick-a-pic: An open dataset of user preferences for text-to-image genera- tion

Reference 24

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Observation 8054eb65-435e-4076-8f95-7a6cc1ca7f37 · outbound

This paper cites Training Language Models to Self-Correct via Reinforcement Learning.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Training Language Models to Self-Correct via Reinforcement Learning

Reference 25

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Unresolved cited work

Reference 26

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Observation 6ed3e10a-bb8a-44b3-b789-9d65b74e7722 · outbound

This paper cites Reflect-DiT: Inference-Time Scaling for Text-to-Image Diffusion Transformers via In-Context Reflection.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Reflect-DiT: Inference-Time Scaling for Text-to-Image Diffusion Transformers via In-Context Reflection

Reference 27

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Observation 2dea6047-9081-4053-ae64-92d6faf54234 · outbound

This paper cites Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding

Reference 28

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Observation 207ce57a-0286-4f6b-a204-28d2e104b77b · outbound

This paper cites Visual- cloze: A universal image generation framework via visual in-context learning.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Visual- cloze: A universal image generation framework via visual in-context learning

Reference 29

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Observation ff65f525-aea5-4f92-b2a6-9ea680b5f43f · outbound

This paper cites Flow matching for generative modeling.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Flow matching for generative modeling

Reference 30

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Observation 9385474b-f2e9-4667-ad89-a7d4cb141f3b · outbound

This paper cites Playground v3: Im- proving text-to-image alignment with deep-fusion large lan- guage models, 2024.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Playground v3: Im- proving text-to-image alignment with deep-fusion large lan- guage models, 2024

Reference 31

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Observation f1677e3f-0033-4265-aefa-56e5f362f313 · outbound

This paper cites Improving Video Generation with Human Feedback.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Improving Video Generation with Human Feedback

Reference 32

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Observation 8e7383aa-e8cc-44e6-b355-e531016ec79d · outbound

This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 33

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Observation 47e6e764-e11a-4e39-9366-672c46933751 · outbound

This paper cites Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps

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This paper cites Self-refine: It- erative refinement with self-feedback.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Self-refine: It- erative refinement with self-feedback

Reference 35

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Observation 633058a4-b0dd-44dc-a91b-81623c303606 · outbound

This paper cites Prodigy: an expeditiously adaptive parameter-free learner.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Prodigy: an expeditiously adaptive parameter-free learner

Reference 36

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Observation cb8fb747-f7d6-4816-a328-6df4f6c0b507 · outbound

This paper cites Scalable diffusion mod- els with transformers.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Scalable diffusion mod- els with transformers

Reference 37

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Observation 9a155c58-12f9-409a-8b17-8586f108e394 · outbound

This paper cites The fineweb datasets: De- canting the web for the finest text data at scale.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning The fineweb datasets: De- canting the web for the finest text data at scale

Reference 38

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Observation 1a6ab9d6-f696-4254-a2af-50acbc7d519a · outbound

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

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 39

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Observation e0f1626c-c145-48e8-a2e1-6486a6b02757 · outbound

This paper cites Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization

Reference 40

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This paper cites Lumina-Image 2.0: A Unified and Efficient Image Generative Framework.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Lumina-Image 2.0: A Unified and Efficient Image Generative Framework

Reference 41

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Observation ae1c6334-d3e0-45ee-a425-2b3070f9777f · outbound

This paper cites Recursive introspection: Teaching language model agents how to self-improve.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Recursive introspection: Teaching language model agents how to self-improve

Reference 42

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Observation 999ee8ea-18be-4fcd-8389-9719f0b1e621 · outbound

This paper cites Grounded sam: Assembling open-world models for diverse visual tasks,.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Grounded sam: Assembling open-world models for diverse visual tasks,

Reference 43

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

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning High-resolution image syn- thesis with latent diffusion models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.905951Z

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Observation c02d8e11-33f6-4d05-93f1-652e2058b7bb · outbound

This paper cites A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation

Reference 45

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Observation b78bcfe0-5260-4dad-952a-5a81962b2283 · outbound

This paper cites Reflexion: Language agents with verbal reinforcement learning.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Reflexion: Language agents with verbal reinforcement learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.894135Z

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Observation 76192d7d-2fc8-4c65-88e2-fe6c9cb80d69 · outbound

This paper cites A General Framework for Inference-time Scaling and Steering of Diffusion Models.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning A General Framework for Inference-time Scaling and Steering of Diffusion Models

Reference 47

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Observation 9dc81027-1b05-4cce-b864-08f542d534f4 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 48

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Observation d1c14833-13bc-47b9-ac24-62721d7b70c4 · outbound

This paper cites Denois- ing diffusion implicit models.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Denois- ing diffusion implicit models

Reference 49

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Observation e20374c4-1456-40dc-8ff2-49a9d16c82b9 · outbound

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Score-based generative modeling through stochastic differential equa- tions

Reference 50

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Observation 89122bbe-9f3c-4fe8-965d-0aaaaa13b77d · outbound

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning OminiControl: Minimal and Universal Control for Diffusion Transformer

Reference 51

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This paper cites Gemini: A Family of Highly Capable Multimodal Models.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Gemini: A Family of Highly Capable Multimodal Models

Reference 52

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Lumina-image 2.0 : A unified and effi- cient image generative model

Reference 53

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Observation 7799d3d2-621a-4828-b94b-55b1a45271d9 · outbound

This paper cites Diffusion model alignment using direct preference optimization.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Diffusion model alignment using direct preference optimization

Reference 54

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Observation 9f23fb08-1d3e-4399-b7f5-44e63a65c74c · outbound

This paper cites Omniedit: Building image editing generalist models through specialist supervision.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Omniedit: Building image editing generalist models through specialist supervision

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.849485Z

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Observation aa4ae59c-e440-48d8-b2f9-6d77e7d7d1c0 · outbound

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Gener- ating sequences by learning to self-correct

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.839060Z

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Observation f1c884c1-86dd-406a-aaec-7831beeb7733 · outbound

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

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 57

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning OmniGen: Unified Image Generation

Reference 58

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Sana: Efficient high-resolution image synthe- sis with linear diffusion transformers

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.828560Z

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Observation 30764b5b-fbad-42cf-8929-08fac5cc4df3 · outbound

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer

Reference 60

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Observation 64ddf44e-afd0-414b-bca1-3b18fa451a6f · outbound

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Imagere- ward: Learning and evaluating human preferences for text- to-image generation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.818397Z

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Observation 54a54583-78b0-468e-af8c-1744ef16c96c · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Tree of thoughts: Deliberate problem solving with large language models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.806887Z

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Observation 7da805c7-4bf8-408e-a47d-7b7e93166793 · outbound

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T11:15:35.056671Z

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This paper cites Tfg: Unified training-free guidance for diffusion models.Ad- vances in Neural Information Processing Systems (NeurIPS),.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Tfg: Unified training-free guidance for diffusion models.Ad- vances in Neural Information Processing Systems (NeurIPS),

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.794896Z

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Observation ed712b29-9b7c-43a1-b66b-b103dbf04264 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Adding conditional control to text-to-image diffusion models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.783873Z

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Observation 09bb384d-6696-4315-917a-63c50fd10606 · outbound

This paper cites Golden Noise for Diffusion Models: A Learning Framework.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Golden Noise for Diffusion Models: A Learning Framework

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T11:15:35.067538Z

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning BRINKLEY’S

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.772990Z

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning For each score, include a short explanation or justification (1-2 sentences) explaining why that score was given

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.762107Z

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Observation fde29491-ebef-48c4-8337-5ea6f508f828 · outbound

This paper cites This should be a weighted average based on the importance of each aspect to the prompt or an average of all aspects.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning This should be a weighted average based on the importance of each aspect to the prompt or an average of all aspects

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.751573Z

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This paper cites For each score, include a short explanation or justification (1-2 sentences) explaining why that score was given.

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.740597Z

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Observation eded0287-e69e-420d-beba-560a1b307ebd · outbound

This paper cites This should be a weighted average based on the importance of each aspect to the prompt or an average of all aspects.

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.730282Z

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Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.720260Z

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Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.709724Z

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Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.698821Z

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This paper cites This should be a weighted average based on the importance of each aspect to the prompt or an average of all aspects.

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.687752Z

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Observation d870e742-edd1-496e-807a-4764a2a329a6 · outbound

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Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.676863Z

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Observation 9b5034bd-9dda-48c5-974d-073e21956ccf · outbound

This paper cites This should be a weighted average based on the importance of each aspect to the prompt or an average of all aspects.

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.666327Z

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Observation fe20cfca-1d12-4285-a4c5-0dc4397b76f9 · outbound

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Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.655259Z

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This paper cites This should be a weighted average based on the importance of each aspect to the prompt or an average of all aspects.

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.644496Z

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Observation 7f9b252a-4294-4bd9-82b3-b107ee17fedf · outbound

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Unresolved cited work

Reference 80

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Observation 7bcf0a3b-0ca0-48ff-93c1-fbff27c1f2dd · outbound

This paper cites Prompt Following Instructions: Examine the original prompt sentence by sentence.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Prompt Following Instructions: Examine the original prompt sentence by sentence

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.621762Z

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Observation 16c3016a-09c1-4e9d-a32d-0ede4a4d62f0 · outbound

This paper cites Prompt Following:\n-\n Each instruction must start with a hyphen and complete command.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Prompt Following:\n-\n Each instruction must start with a hyphen and complete command

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.609825Z

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Observation 0cb9dfb5-6fdd-42d8-ac8d-a5c34d8205b2 · outbound

This paper cites Prioritize critical errors first.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Prioritize critical errors first

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.595334Z

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Observation 10e2f13e-a400-4895-bef9-bd23624c9103 · outbound

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Unresolved cited work

Reference 84

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Observation 5a26ad6c-a6d2-40db-a90f-008b22c5b3be · outbound

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Unresolved cited work

Reference 85

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Observation 961bb86c-a17b-4908-9908-0c2e6c34ed30 · outbound

This paper cites For example, the images, the assessments, etc.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning For example, the images, the assessments, etc

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.558834Z

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Observation 4bc737ec-bfeb-41a4-b1aa-b0d5f2e0af4f · outbound

This paper cites Figure 19.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Figure 19

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.545961Z

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Observation fcef3cea-b766-4471-8564-a721e50e711f · outbound

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From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning Unresolved cited work

Reference 88

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Observation 278a9a35-58d2-4355-8ff0-fc6ce5e03deb · outbound

This paper cites left" if the left image is better; output.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning left" if the left image is better; output

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:15:35.522210Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dc0bea84-99bb-420d-b859-22473108b847 · outbound

This paper cites the left image.

From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning the left image

Reference 90

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Pith citing papers

Observation 3fb15914-f6c9-4e57-bf4b-bb000546069f · inbound

MINT-CoT: Enabling Interleaved Visual Tokens in Mathematical Chain-of-Thought Reasoning cites this paper.

MINT-CoT: Enabling Interleaved Visual Tokens in Mathematical Chain-of-Thought Reasoning From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning

Reference 85

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Performance Plateaus in Inference-Time Scaling for Text-to-Image Diffusion Without External Models cites this paper.

Performance Plateaus in Inference-Time Scaling for Text-to-Image Diffusion Without External Models From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning

Reference 15

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OmniGen2: Towards Instruction-Aligned Multimodal Generation cites this paper.

OmniGen2: Towards Instruction-Aligned Multimodal Generation From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning

Reference 95

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Observation cbe2b674-3fa5-42d5-8723-e92f31c46a4b · inbound

Interleaving Reasoning for Better Text-to-Image Generation cites this paper.

Interleaving Reasoning for Better Text-to-Image Generation From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning

Reference 31

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Observation 7506a4c0-0637-4a8d-b8d8-297db790e794 · inbound

Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and Generation cites this paper.

Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and Generation From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning

Reference 79

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Unlocking Complex Visual Generation via Closed-Loop Verified Reasoning cites this paper.

Unlocking Complex Visual Generation via Closed-Loop Verified Reasoning From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning

Reference 61

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Observation 515a2e40-fda9-47dc-b049-ea69859a3565 · inbound

MathVis-Fine: Aligning Visual Supervision with Necessity via Progressive Dependency-Guided Training for Multimodal Mathematical Reasoning cites this paper.

MathVis-Fine: Aligning Visual Supervision with Necessity via Progressive Dependency-Guided Training for Multimodal Mathematical Reasoning From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning

Reference 104

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