The two clocks and the innovation window: When and how generative models learn rules | Paper Citation Record

Observation b8dd2745-de52-4577-a60b-34f33a48d88f · outbound

This paper cites Advances in neural information processing systems , volume=.

The two clocks and the innovation window: When and how generative models learn rules Advances in neural information processing systems , volume=

Reference 1

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Observation de5334fd-7e46-4e2a-8a88-88e3ecf4d716 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

The two clocks and the innovation window: When and how generative models learn rules Advances in Neural Information Processing Systems , volume=

Reference 2

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Observation 9a83b654-719e-469a-af4c-1c31a31023f6 · outbound

This paper cites A Random Matrix Theory Perspective on the Consistency of Diffusion Models.

The two clocks and the innovation window: When and how generative models learn rules A Random Matrix Theory Perspective on the Consistency of Diffusion Models

Reference 3

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This paper cites Vision Transformers Need Registers.

The two clocks and the innovation window: When and how generative models learn rules Vision Transformers Need Registers

Reference 4

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This paper cites Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think.

The two clocks and the innovation window: When and how generative models learn rules Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 5

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Observation 52b9f80f-1712-438a-a099-dd0e0abaf8c1 · outbound

This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.

The two clocks and the innovation window: When and how generative models learn rules Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 6

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Observation 1f5a5ba4-328b-4462-bb5a-123caa767715 · outbound

This paper cites Kearns , title =.

The two clocks and the innovation window: When and how generative models learn rules Kearns , title =

Reference 7

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This paper cites The Thirteenth International Conference on Learning Representations.

The two clocks and the innovation window: When and how generative models learn rules The Thirteenth International Conference on Learning Representations

Reference 8

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This paper cites From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency.

The two clocks and the innovation window: When and how generative models learn rules From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency

Reference 9

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Observation 298e738a-b6be-435f-86d9-f592bcc31193 · outbound

This paper cites Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit.

The two clocks and the innovation window: When and how generative models learn rules Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.120838Z

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This paper cites 2023 , journal =.

The two clocks and the innovation window: When and how generative models learn rules 2023 , journal =

Reference 11

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Observation 864d5f38-6ea0-48c6-979c-ba377a86c070 · outbound

This paper cites Transactions of the Association for Computational Linguistics , volume=.

The two clocks and the innovation window: When and how generative models learn rules Transactions of the Association for Computational Linguistics , volume=

Reference 12

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This paper cites 2024 , journal =.

The two clocks and the innovation window: When and how generative models learn rules 2024 , journal =

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.896584Z

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Observation 81112096-4b4e-429a-8627-12c4a469f16f · outbound

This paper cites Transformers Learn Shortcuts to Automata.

The two clocks and the innovation window: When and how generative models learn rules Transformers Learn Shortcuts to Automata

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.663325Z

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This paper cites 2022 , journal =.

The two clocks and the innovation window: When and how generative models learn rules 2022 , journal =

Reference 15

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Observation b207c980-0564-4767-8168-bf100ea1c3a6 · outbound

This paper cites Self-Attention Networks Can Process Bounded Hierarchical Languages.

The two clocks and the innovation window: When and how generative models learn rules Self-Attention Networks Can Process Bounded Hierarchical Languages

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.595893Z

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This paper cites The Twelfth International Conference on Learning Representations.

The two clocks and the innovation window: When and how generative models learn rules The Twelfth International Conference on Learning Representations

Reference 17

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This paper cites 2025 , journal =.

The two clocks and the innovation window: When and how generative models learn rules 2025 , journal =

Reference 18

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This paper cites Neural Information Processing Systems , year =.

The two clocks and the innovation window: When and how generative models learn rules Neural Information Processing Systems , year =

Reference 19

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Observation 04b57830-f66a-4b40-8eeb-e86808d59166 · outbound

This paper cites T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models.

The two clocks and the innovation window: When and how generative models learn rules T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models

Reference 20

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Observation 4440eec2-80a8-4374-8930-7888f01f4e8a · outbound

This paper cites Tight pair query lower bounds for matching and earth mover’s distance.

The two clocks and the innovation window: When and how generative models learn rules Tight pair query lower bounds for matching and earth mover’s distance

Reference 21

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This paper cites 2025 , journal =.

The two clocks and the innovation window: When and how generative models learn rules 2025 , journal =

Reference 22

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This paper cites 2024 , journal =.

The two clocks and the innovation window: When and how generative models learn rules 2024 , journal =

Reference 23

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Observation 58fa1deb-52f8-45c1-95b3-a2c3783541e8 · outbound

This paper cites SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics.

The two clocks and the innovation window: When and how generative models learn rules SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.051877Z

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This paper cites 2025 , journal =.

The two clocks and the innovation window: When and how generative models learn rules 2025 , journal =

Reference 25

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Observation 303ec49c-3c8d-4385-b4fd-977844d79869 · outbound

This paper cites Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions.

The two clocks and the innovation window: When and how generative models learn rules Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:16:18.027875Z

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Observation cb2d66e0-8033-40fc-ab1d-53b89eab4983 · outbound

This paper cites 2023 , eprint=.

The two clocks and the innovation window: When and how generative models learn rules 2023 , eprint=

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.830221Z

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Observation 18bd9b62-ca67-4b3d-9d4a-030526415e68 · outbound

This paper cites Journal of Machine Learning Research , volume =.

The two clocks and the innovation window: When and how generative models learn rules Journal of Machine Learning Research , volume =

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.836698Z

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This paper cites Learning High-Degree Parities: The Crucial Role of the Initialization , booktitle =.

The two clocks and the innovation window: When and how generative models learn rules Learning High-Degree Parities: The Crucial Role of the Initialization , booktitle =

Reference 29

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Observation f2aede62-52e3-44ab-bbb5-a435336e71e3 · outbound

This paper cites Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

The two clocks and the innovation window: When and how generative models learn rules Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 30

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The two clocks and the innovation window: When and how generative models learn rules 2024 , eprint=

Reference 31

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Observation 9f7a3052-ab13-4012-b2fd-a69aee1f3dfe · outbound

This paper cites Towards a Mechanistic Explanation of Diffusion Model Generalization.

The two clocks and the innovation window: When and how generative models learn rules Towards a Mechanistic Explanation of Diffusion Model Generalization

Reference 32

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This paper cites 2025 , eprint=.

The two clocks and the innovation window: When and how generative models learn rules 2025 , eprint=

Reference 33

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Observation a87c4e15-c37e-4504-a097-bf8e3adc29fd · outbound

This paper cites Align Your Latents.

The two clocks and the innovation window: When and how generative models learn rules Align Your Latents

Reference 34

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The two clocks and the innovation window: When and how generative models learn rules 2024 , eprint =

Reference 35

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This paper cites arXiv preprint arXiv:2602.17846 , year=.

The two clocks and the innovation window: When and how generative models learn rules arXiv preprint arXiv:2602.17846 , year=

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.592940Z

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Observation 8618f3a6-4700-4b18-b40a-c83708e7a669 · outbound

This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

The two clocks and the innovation window: When and how generative models learn rules PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 37

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Observation 9fdad174-3eae-4d0e-97c7-5a4f3fd11a56 · outbound

This paper cites Sampling Is as Easy as Learning the Score: Theory for Diffusion Models with Minimal Data Assumptions , booktitle =.

The two clocks and the innovation window: When and how generative models learn rules Sampling Is as Easy as Learning the Score: Theory for Diffusion Models with Minimal Data Assumptions , booktitle =

Reference 38

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Observation 355ff940-1c40-4809-80dd-856333aeb1e2 · outbound

This paper cites Deconstructing Denoising Diffusion Models for Self-Supervised Learning.

The two clocks and the innovation window: When and how generative models learn rules Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 39

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Observation a639dce5-5c35-4253-b2d5-ac4862adf93d · outbound

This paper cites Stargan v2.

The two clocks and the innovation window: When and how generative models learn rules Stargan v2

Reference 40

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Observation 189a632c-8786-4c1f-b3b5-60eb22324d4c · outbound

This paper cites Testing Relational Understanding in Text-Guided Image Generation.

The two clocks and the innovation window: When and how generative models learn rules Testing Relational Understanding in Text-Guided Image Generation

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.064800Z

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This paper cites 2025 , eprint=.

The two clocks and the innovation window: When and how generative models learn rules 2025 , eprint=

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:51.073520Z

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Observation 1f259e60-f672-481b-90cf-7ac6c5c38a1b · outbound

This paper cites Learning Mixtures of Gaussians Using Diffusion Models.

The two clocks and the innovation window: When and how generative models learn rules Learning Mixtures of Gaussians Using Diffusion Models

Reference 43

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Observation 7b0f3de2-99a8-4474-b455-149cd0bc6483 · outbound

This paper cites 2020 , journal =.

The two clocks and the innovation window: When and how generative models learn rules 2020 , journal =

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:51.034516Z

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Observation b4f0f9c4-fad0-41ac-bd6c-71f1202e2178 · outbound

This paper cites Classifier-Free Diffusion Guidance.

The two clocks and the innovation window: When and how generative models learn rules Classifier-Free Diffusion Guidance

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-05-12T03:16:18.653459Z

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Observation bb81c94a-fcaf-415e-8347-03837e1ef961 · outbound

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The two clocks and the innovation window: When and how generative models learn rules Video Diffusion Models , booktitle =

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:51.030878Z

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The two clocks and the innovation window: When and how generative models learn rules 2005 , month = dec, journal =

Reference 47

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Observation 8a619527-9aec-4fb5-8648-fc47b2f8c06e · outbound

This paper cites Analyzing and Improving the Training Dynamics of Diffusion Models.

The two clocks and the innovation window: When and how generative models learn rules Analyzing and Improving the Training Dynamics of Diffusion Models

Reference 48

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Observation 37f75ce8-8bb8-4c86-a9af-ecfe801c071f · outbound

This paper cites 2024 , eprint=.

The two clocks and the innovation window: When and how generative models learn rules 2024 , eprint=

Reference 49

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Observation d2549214-8fe6-4fdf-aa59-3c099caa73fc · outbound

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The two clocks and the innovation window: When and how generative models learn rules Unresolved cited work

Reference 50

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Observation 386e4a5e-4001-4dd2-adc8-42c0b657bc23 · outbound

This paper cites PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior.

The two clocks and the innovation window: When and how generative models learn rules PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior

Reference 51

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:16:18.628232Z

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Observation 86da8b83-640b-40e8-bb64-d8bc8de1b564 · outbound

This paper cites Pseudo Numerical Methods for Diffusion Models on Manifolds.

The two clocks and the innovation window: When and how generative models learn rules Pseudo Numerical Methods for Diffusion Models on Manifolds

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.660649Z

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Observation d70ad9b8-65e1-478b-a5ea-ddf5def51889 · outbound

This paper cites DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models.

The two clocks and the innovation window: When and how generative models learn rules DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:54:11.877839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation ab17f360-7b20-41e0-8e1d-1ab13e0678c6 · outbound

This paper cites Dpm-Solver.

The two clocks and the innovation window: When and how generative models learn rules Dpm-Solver

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:51.042695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation f897c324-07b3-4bdc-9c55-35ffd910e7a6 · outbound

This paper cites SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers.

The two clocks and the innovation window: When and how generative models learn rules SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 55

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:16:18.047678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 224d6f71-79d6-4745-8169-484b46f2278e · outbound

This paper cites Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task.

The two clocks and the innovation window: When and how generative models learn rules Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.114385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 900b57f8-71b6-410e-bfba-edd437d20b23 · outbound

This paper cites Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space.

The two clocks and the innovation window: When and how generative models learn rules Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.068530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 1b016c92-8e4a-4e05-afac-400d147c1767 · outbound

This paper cites PFGM++: Unlocking the Potential of Physics-Inspired Generative Models.

The two clocks and the innovation window: When and how generative models learn rules PFGM++: Unlocking the Potential of Physics-Inspired Generative Models

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.061098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 885d1348-bb3e-47aa-a1db-1ac97ad3a9dd · outbound

This paper cites Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors.

The two clocks and the innovation window: When and how generative models learn rules Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.622479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 77a601ab-ad39-4f18-bacc-c94637791387 · outbound

This paper cites Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

The two clocks and the innovation window: When and how generative models learn rules Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 61

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T05:37:56.023799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 3031d66b-a68c-400f-963f-67447a046609 · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models , booktitle =.

The two clocks and the innovation window: When and how generative models learn rules High-Resolution Image Synthesis with Latent Diffusion Models , booktitle =

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:51.050692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation b17605ec-3c3a-446b-a1db-419956a8b0e8 · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

The two clocks and the innovation window: When and how generative models learn rules High-Resolution Image Synthesis with Latent Diffusion Models

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-05-12T03:16:18.037624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 4c4711fa-d049-498f-bc17-707002fe9374 · outbound

This paper cites Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding.

The two clocks and the innovation window: When and how generative models learn rules Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Reference 64

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T07:38:54.138183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 35884d23-873f-4d2a-a839-57a7cf5da61a · outbound

This paper cites Closed-Form Diffusion Models.

The two clocks and the innovation window: When and how generative models learn rules Closed-Form Diffusion Models

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.645436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 9c3fd01b-b07d-4946-b321-1640d1409fe1 · outbound

This paper cites 2023 , journal =.

The two clocks and the innovation window: When and how generative models learn rules 2023 , journal =

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:51.007050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 55958599-a9f5-47af-ab36-3b23ec651d41 · outbound

This paper cites Sliced Score Matching: A Scalable Approach to Density and Score Estimation , booktitle =.

The two clocks and the innovation window: When and how generative models learn rules Sliced Score Matching: A Scalable Approach to Density and Score Estimation , booktitle =

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:51.010625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation f2a557da-c355-4d5f-98c0-2b2e617213f0 · outbound

This paper cites Generative Modeling by Estimating Gradients of the Data Distribution , booktitle =.

The two clocks and the innovation window: When and how generative models learn rules Generative Modeling by Estimating Gradients of the Data Distribution , booktitle =

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.999282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation c5b7e07a-0248-4dfa-ac50-7dccaa5d2e26 · outbound

This paper cites Denoising Diffusion Implicit Models.

The two clocks and the innovation window: When and how generative models learn rules Denoising Diffusion Implicit Models

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-05-12T03:16:18.625246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 301e515e-f163-4294-900f-52bb2a34748e · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations , booktitle =.

The two clocks and the innovation window: When and how generative models learn rules Score-Based Generative Modeling through Stochastic Differential Equations , booktitle =

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.995633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 159f8dee-0929-4e47-9eac-f224bf1819e5 · outbound

This paper cites What the DAAM: Interpreting Stable Diffusion Using Cross Attention.

The two clocks and the innovation window: When and how generative models learn rules What the DAAM: Interpreting Stable Diffusion Using Cross Attention

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.598762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 9931a627-857a-4468-beca-3fdbfbd900c0 · outbound

This paper cites 2011 , journal =.

The two clocks and the innovation window: When and how generative models learn rules 2011 , journal =

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:51.002855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 212d5f08-37e9-4920-a98d-1eb0107397e7 · outbound

This paper cites A Geometric Analysis of Deep Generative Image Models and Its Applications , booktitle =.

The two clocks and the innovation window: When and how generative models learn rules A Geometric Analysis of Deep Generative Image Models and Its Applications , booktitle =

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:51.014663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 82968527-4f9a-4b4d-bd8e-04a1579ad575 · outbound

This paper cites Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Later.

The two clocks and the innovation window: When and how generative models learn rules Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Later

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.642183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation cf07aa6c-e728-4321-94f6-a9b68653132a · outbound

This paper cites The Hidden Linear Structure in Score-Based Models and its Application.

The two clocks and the innovation window: When and how generative models learn rules The Hidden Linear Structure in Score-Based Models and its Application

Reference 75

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:16:18.106842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 1a9d791a-3449-43fa-8d65-8ec411986ab6 · outbound

This paper cites Relational Composition in Neural Networks: A Survey and Call to Action.

The two clocks and the innovation window: When and how generative models learn rules Relational Composition in Neural Networks: A Survey and Call to Action

Reference 76

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.022827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 8b40defd-7d8e-4c6e-a7d2-0ae413a78bfd · outbound

This paper cites Score-Based Generative Model Learn Manifold-like Structures with Constrained Mixing , booktitle =.

The two clocks and the innovation window: When and how generative models learn rules Score-Based Generative Model Learn Manifold-like Structures with Constrained Mixing , booktitle =

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.984766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 750e690f-87ba-4c8e-a1b9-54640a9de070 · outbound

This paper cites Score-based generative models learn manifold-like structures with constrained mixing.

The two clocks and the innovation window: When and how generative models learn rules Score-based generative models learn manifold-like structures with constrained mixing

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.631214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation cd30a241-b0b6-40f5-915c-c947e72f0a16 · outbound

This paper cites Making Text-to-Image Diffusion Models Zero-Shot Image-to-Image Editors by Inferring ''random Seeds'' , booktitle =.

The two clocks and the innovation window: When and how generative models learn rules Making Text-to-Image Diffusion Models Zero-Shot Image-to-Image Editors by Inferring ''random Seeds'' , booktitle =

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.981205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 16dd242b-8ce0-49c0-9206-831ddb2909a9 · outbound

This paper cites SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers.

The two clocks and the innovation window: When and how generative models learn rules SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers

Reference 80

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T00:56:50.121098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 68acda0d-9ce9-4bbe-934b-b1f22eaaebca · outbound

This paper cites editor =.

The two clocks and the innovation window: When and how generative models learn rules editor =

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.988447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation fb23f5aa-fb51-4240-af71-8754bc85c3a7 · outbound

This paper cites Diffusion models: A comprehensive survey of methods and applications.

The two clocks and the innovation window: When and how generative models learn rules Diffusion models: A comprehensive survey of methods and applications

Reference 82

Resolution: metadata mismatch; doi, observed 2026-05-12T03:16:18.103561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 25bdafa7-3e45-472b-91bc-f40a78d80c5c · outbound

This paper cites On the Generalization of Diffusion Model.

The two clocks and the innovation window: When and how generative models learn rules On the Generalization of Diffusion Model

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.656856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 1fac1e8e-e5e5-4361-91f5-4ce8b730305f · outbound

This paper cites The Emergence of Reproducibility and Generalizability in Diffusion Models.

The two clocks and the innovation window: When and how generative models learn rules The Emergence of Reproducibility and Generalizability in Diffusion Models

Reference 84

Resolution: verified exact; arxiv_id, observed 2026-06-10T02:11:08.597866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation b1e13ccb-6f5f-4b8f-a305-6a262ea43c02 · outbound

This paper cites an unresolved cited work.

The two clocks and the innovation window: When and how generative models learn rules Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-05-12T20:21:50.973638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 7dd04da5-488b-4207-9a9f-ca821a7b3c61 · outbound

This paper cites Dpm-Solver-v3.

The two clocks and the innovation window: When and how generative models learn rules Dpm-Solver-v3

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:51.027382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 58cbb2ef-094d-4072-98ce-fb99ed7e0bcc · outbound

This paper cites Flow Matching for Generative Modeling.

The two clocks and the innovation window: When and how generative models learn rules Flow Matching for Generative Modeling

Reference 87

Resolution: metadata mismatch; local_arxiv, observed 2026-05-12T03:16:18.072714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation b260b38d-acdc-40c4-8db2-7c3cf7067372 · outbound

This paper cites Prompt-to-prompt image editing with cross attention control , journal =.

The two clocks and the innovation window: When and how generative models learn rules Prompt-to-prompt image editing with cross attention control , journal =

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.970367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation d6d3e46c-b416-421d-8e88-7fe66e663904 · outbound

This paper cites Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing.

The two clocks and the innovation window: When and how generative models learn rules Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing

Reference 89

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:16:18.097450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 2ddf2174-6d03-477b-aafc-2a30708e2a3d · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

The two clocks and the innovation window: When and how generative models learn rules Advances in Neural Information Processing Systems , volume=

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.977102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 8cdadffa-539a-47b3-b7a1-d31b5f92c2a5 · outbound

This paper cites 2025 , number=.

The two clocks and the innovation window: When and how generative models learn rules 2025 , number=

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.992184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 7a369bb9-970f-4290-bf4b-bdc8da5ce2a7 · outbound

This paper cites 2023 , eprint=.

The two clocks and the innovation window: When and how generative models learn rules 2023 , eprint=

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:51.017735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation f48db9d1-ac21-425e-87c5-ec026f527482 · outbound

This paper cites 2023 , eprint=.

The two clocks and the innovation window: When and how generative models learn rules 2023 , eprint=

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:51.046417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation b1a31b3b-71d9-42a2-92d6-0fddd353e756 · outbound

This paper cites 2023 , eprint=.

The two clocks and the innovation window: When and how generative models learn rules 2023 , eprint=

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.966242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation bb29d332-f306-4a1d-aa9d-04380203bc26 · outbound

This paper cites 2024 , eprint=.

The two clocks and the innovation window: When and how generative models learn rules 2024 , eprint=

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.947109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 80ee4bd1-c268-4a5d-aa36-3e8637bbba88 · outbound

This paper cites The Thirteenth International Conference on Learning Representations , year=.

The two clocks and the innovation window: When and how generative models learn rules The Thirteenth International Conference on Learning Representations , year=

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.950447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 89a1113b-e815-43c0-b246-1dba6d44c16e · outbound

This paper cites 2022 , eprint=.

The two clocks and the innovation window: When and how generative models learn rules 2022 , eprint=

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.940349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation a13ebc7a-068d-4f51-8db2-5e483b9556fa · outbound

This paper cites 2023 , eprint=.

The two clocks and the innovation window: When and how generative models learn rules 2023 , eprint=

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.937339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 9f54f79e-3400-4f0e-887c-e5c6da3e4ef3 · outbound

This paper cites Proceedings of the IEEE/CVF international conference on computer vision , pages=.

The two clocks and the innovation window: When and how generative models learn rules Proceedings of the IEEE/CVF international conference on computer vision , pages=

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.943497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation e9b2e5ac-51c8-447e-acf7-7808fd44538a · outbound

This paper cites IEEE Transactions on Visualization and Computer Graphics , volume=.

The two clocks and the innovation window: When and how generative models learn rules IEEE Transactions on Visualization and Computer Graphics , volume=

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.953310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 329535b5-c046-4a14-94ad-7a5fe867bd4e · outbound

This paper cites Advances in neural information processing systems , volume=.

The two clocks and the innovation window: When and how generative models learn rules Advances in neural information processing systems , volume=

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T20:21:50.958552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers