Paper Citation Record · LEDGER

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion

As of 19 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 1 inbound Pith citation observation for arXiv:2505.16425.

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

pith.paper-citation-record.v1
2505.16425 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:03:43.408265Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:38:37.497428Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T04:38:39.693957Z

Reference resolution

61 of 61 outbound references displayed

verified exact8
verified fuzzy1
unresolved51
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9bc41830-0ba5-4bc4-a162-293bc1e8b35c · outbound

This paper cites an unresolved cited work.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Unresolved cited work

Reference 1

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Observation d3a93cff-b7ce-4f0b-b53a-36e1d7befe0e · outbound

This paper cites Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval

Reference 2

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Observation a4276df0-c917-4904-8060-67d8944eb415 · outbound

This paper cites LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 3

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Observation 76120329-8559-416c-9ba0-0a9d28055e15 · outbound

This paper cites https://api.semanticscholar.org/CorpusID:264403242 Improving image generation with better captions.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion https://api.semanticscholar.org/CorpusID:264403242 Improving image generation with better captions

Reference 4

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Observation 55672839-4837-46ca-952e-2ce7a32bfd7e · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Training Diffusion Models with Reinforcement Learning

Reference 5

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Observation ff16a6bd-03ce-4f03-aa78-5b30b366f4a6 · outbound

This paper cites On the Design Fundamentals of Diffusion Models: A Survey.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion On the Design Fundamentals of Diffusion Models: A Survey

Reference 6

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Source-reported events for the cited work

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Observation b34f1779-4abc-4d04-83ea-91e003946f69 · outbound

This paper cites MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark

Reference 7

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Source-reported events for the cited work

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Observation 54cb629c-fbd1-44d4-98b1-aac0a7fb722b · outbound

This paper cites Evaluating Text-to-Image Generative Models: An Empirical Study on Human Image Synthesis.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Evaluating Text-to-Image Generative Models: An Empirical Study on Human Image Synthesis

Reference 8

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This paper cites X-IQE: eXplainable Image Quality Evaluation for Text-to-Image Generation with Visual Large Language Models.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion X-IQE: eXplainable Image Quality Evaluation for Text-to-Image Generation with Visual Large Language Models

Reference 9

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Observation 382f8147-2089-4b7a-b9ed-18b03b123820 · outbound

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Unresolved cited work

Reference 10

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Observation 0edff757-7773-4372-b5d3-9a975d96818b · outbound

This paper cites Directly Fine-Tuning Diffusion Models on Differentiable Rewards.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Directly Fine-Tuning Diffusion Models on Differentiable Rewards

Reference 11

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Observation 429222e7-86b9-484c-bfba-d965c4e25d92 · outbound

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

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Reference 12

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Observation 7e163a25-9377-4b98-83b3-5fe7ae6f4acd · outbound

This paper cites RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment

Reference 13

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Observation dda87e0e-175e-4c16-89a3-0cd24a0247a1 · outbound

This paper cites DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models

Reference 14

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Observation 8fc889f0-a173-4fad-8d21-1e5a948403a1 · outbound

This paper cites Learning to Segment Actions from Observation and Narration.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Learning to Segment Actions from Observation and Narration

Reference 15

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This paper cites GPTScore: Evaluate as You Desire.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion GPTScore: Evaluate as You Desire

Reference 16

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Observation a9a1f61f-aa72-46a9-823b-aadd3484262d · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 17

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Observation 66908ad7-fc0f-4d95-9147-fc4620f8c7f4 · outbound

This paper cites COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning

Reference 18

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Observation 75349684-e6b9-4c77-8b2e-337290b9b556 · outbound

This paper cites Temporal Alignment Networks for Long-term Video.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Temporal Alignment Networks for Long-term Video

Reference 19

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Optimizing Prompts for Text-to-Image Generation

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 21

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Denoising Diffusion Probabilistic Models

Reference 22

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Observation 9b378390-b238-43e1-88f4-8300d51e0654 · outbound

This paper cites Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Unresolved cited work

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Aligning Text-to-Image Models using Human Feedback

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Unresolved cited work

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Aligning Diffusion Models by Optimizing Human Utility

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Learning To Recognize Procedural Activities with Distant Supervision

Reference 28

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Observation 4436ae88-6107-41d9-8836-d8f293434a2e · outbound

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion VideoDPO: Omni-Preference Alignment for Video Diffusion Generation

Reference 29

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection

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Observation f51cdcfd-4231-4f1c-a9df-49631214729d · outbound

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Solving New Tasks by Adapting Internet Video Knowledge

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Generating Illustrated Instructions

Reference 32

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion End-to-End Learning of Visual Representations from Uncurated Instructional Videos

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion CaptainCook4D: A Dataset for Understanding Errors in Procedural Activities

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Aligning Text-to-Image Diffusion Models with Reward Backpropagation

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Leveraging Procedural Knowledge and Task Hierarchies for Efficient Instructional Video Pre-training

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Diffusion Model Alignment Using Direct Preference Optimization

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Is ChatGPT a Good NLG Evaluator? A Preliminary Study

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion A Unified Agentic Framework for Evaluating Conditional Image Generation

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Large Language Models are not Fair Evaluators

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion InstructionBench: An Instructional Video Understanding Benchmark

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Preference Alignment on Diffusion Model: A Comprehensive Survey for Image Generation and Editing

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals

Reference 49

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 50

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation

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Reference 53

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$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:03:43.937656Z

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Observation 3bbbec6c-3c3c-4d8f-bc36-205b1ddf5267 · outbound

This paper cites an unresolved cited work.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Unresolved cited work

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:42.818795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 398b40fe-c1b4-4450-bb19-7ecc2e1db96a · outbound

This paper cites Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:42.921285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cf6cb1f-b73d-4ca2-81c3-6b1521989a1a · outbound

This paper cites an unresolved cited work.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Unresolved cited work

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:42.997502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f38af04-436b-4d55-9f49-64e217bb05fa · outbound

This paper cites Visual Goal-Step Inference using wikiHow.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Visual Goal-Step Inference using wikiHow

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:43.097879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8eeac2db-e9ca-433c-a022-eb5f4997b90a · outbound

This paper cites Cross-task weakly supervised learning from instructional videos.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion Cross-task weakly supervised learning from instructional videos

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:03:43.631263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 81dd66e2-cc78-4e95-a2c1-1c620547de33 · outbound

This paper cites online" 'onlinestring :=.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion online" 'onlinestring :=

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:43.313545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0e48172-576e-493d-863d-9155ee43bf20 · outbound

This paper cites write newline.

$I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion write newline

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:43.408265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 4ddd4061-9f18-4a7b-a125-b98aa83718fb · inbound

InstructionCrafter: Generating Consistent and High-Fidelity Visual Instructions cites this paper.

InstructionCrafter: Generating Consistent and High-Fidelity Visual Instructions $I^2G$: Generating Instructional Illustrations via Text-Conditioned Diffusion

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-14T04:38:39.703595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.