Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling | Paper Citation Record

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This paper cites Wasserstein GAN.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Wasserstein GAN

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This paper cites Language models are few-shot learners.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Language models are few-shot learners

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This paper cites RichSpace: Enriching Text-to-Video Prompt Space via Text Embedding Interpolation.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling RichSpace: Enriching Text-to-Video Prompt Space via Text Embedding Interpolation

Reference 3

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This paper cites Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies

Reference 4

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This paper cites Universal Approximation of Visual Autoregressive Transformers.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Universal Approximation of Visual Autoregressive Transformers

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This paper cites HSR-Enhanced Sparse Attention Acceleration.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling HSR-Enhanced Sparse Attention Acceleration

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This paper cites Neural ordinary differential equations.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Neural ordinary differential equations

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This paper cites Video Latent Flow Matching: Optimal Polynomial Projections for Video Interpolation and Extrapolation.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Video Latent Flow Matching: Optimal Polynomial Projections for Video Interpolation and Extrapolation

Reference 8

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This paper cites Treequestion: Assessing conceptual learning outcomes with llm-generated multiple-choice questions.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Treequestion: Assessing conceptual learning outcomes with llm-generated multiple-choice questions

Reference 9

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This paper cites Flownet: Learning optical flow with convolutional networks.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Flownet: Learning optical flow with convolutional networks

Reference 10

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This paper cites Variational Schr\"odinger Diffusion Models.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Variational Schr\"odinger Diffusion Models

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This paper cites Diffusion models beat gans on image synthesis.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Diffusion models beat gans on image synthesis

Reference 12

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This paper cites Efficient video prediction via sparsely conditioned flow matching.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Efficient video prediction via sparsely conditioned flow matching

Reference 13

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This paper cites Zur elektrodynamik bewegter k \"o rper.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Zur elektrodynamik bewegter k \"o rper

Reference 14

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

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Scaling rectified flow transformers for high-resolution image synthesis

Reference 15

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This paper cites How Far Are We From AGI: Are LLMs All We Need?.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling How Far Are We From AGI: Are LLMs All We Need?

Reference 16

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Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Unresolved cited work

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This paper cites Layer compression of deep networks with straight flows.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Layer compression of deep networks with straight flows

Reference 18

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This paper cites Generative adversarial nets.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Generative adversarial nets

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This paper cites Denoising diffusion probabilistic models.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Denoising diffusion probabilistic models

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This paper cites Denoising diffusion probabilistic models.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Denoising diffusion probabilistic models

Reference 21

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This paper cites On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality

Reference 22

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This paper cites On statistical rates and provably efficient criteria of latent diffusion transformers (dits).

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling On statistical rates and provably efficient criteria of latent diffusion transformers (dits)

Reference 23

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This paper cites FlowNet2 : Evolution of optical flow estimation with deep networks.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling FlowNet2 : Evolution of optical flow estimation with deep networks

Reference 24

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This paper cites Pyramidal flow matching for efficient video generative modeling.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Pyramidal flow matching for efficient video generative modeling

Reference 25

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This paper cites Elucidating the design space of diffusion-based generative models.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Elucidating the design space of diffusion-based generative models

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This paper cites Analyzing and improving the training dynamics of diffusion models.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Analyzing and improving the training dynamics of diffusion models

Reference 27

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This paper cites On the translocation of masses.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling On the translocation of masses

Reference 28

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This paper cites On Computational Limits and Provably Efficient Criteria of Visual Autoregressive Models: A Fine-Grained Complexity Analysis.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling On Computational Limits and Provably Efficient Criteria of Visual Autoregressive Models: A Fine-Grained Complexity Analysis

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This paper cites Circuit Complexity Bounds for Visual Autoregressive Model.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Circuit Complexity Bounds for Visual Autoregressive Model

Reference 30

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This paper cites Dpbloomfilter: Securing bloom filters with differential privacy.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Dpbloomfilter: Securing bloom filters with differential privacy

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Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Auto-Encoding Variational Bayes

Reference 32

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This paper cites Flow Matching for Generative Modeling.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Flow Matching for Generative Modeling

Reference 33

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Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

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Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Exploring the frontiers of softmax: Provable optimization, applications in diffusion model, and beyond

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This paper cites Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models

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Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Looped ReLU MLPs May Be All You Need as Practical Programmable Computers

Reference 37

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This paper cites Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time

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This paper cites Differential Privacy Mechanisms in Neural Tangent Kernel Regression.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Differential Privacy Mechanisms in Neural Tangent Kernel Regression

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This paper cites Unraveling the Smoothness Properties of Diffusion Models: A Gaussian Mixture Perspective.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Unraveling the Smoothness Properties of Diffusion Models: A Gaussian Mixture Perspective

Reference 40

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This paper cites Score-based Generative Diffusion Models for Social Recommendations.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Score-based Generative Diffusion Models for Social Recommendations

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Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Conditional Generative Adversarial Nets

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This paper cites Memoire sur la theorie des deblais et des remblais.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Memoire sur la theorie des deblais et des remblais

Reference 43

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This paper cites Pixel recurrent neural networks.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Pixel recurrent neural networks

Reference 44

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

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling High-resolution image synthesis with latent diffusion models

Reference 45

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This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Deep unsupervised learning using nonequilibrium thermodynamics

Reference 46

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Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Aligned diffusion schr \"o dinger bridges

Reference 47

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This paper cites Score-based generative modeling through stochastic differential equations.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Score-based generative modeling through stochastic differential equations

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.606463Z

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Observation 2078c985-0c2a-47df-8a64-9c3e3864ae11 · outbound

This paper cites Lazydit: Lazy learning for the acceleration of diffusion transformers.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Lazydit: Lazy learning for the acceleration of diffusion transformers

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.596555Z

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This paper cites Rossi, Hao Tan, Tong Yu, Xiang Chen, Yufan Zhou, Tong Sun, Pu Zhao, Yanzhi Wang, and Jiuxiang Gu.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Rossi, Hao Tan, Tong Yu, Xiang Chen, Yufan Zhou, Tong Sun, Pu Zhao, Yanzhi Wang, and Jiuxiang Gu

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.586829Z

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Observation 4271b209-2f32-4f66-839d-3f71ec112ab4 · outbound

This paper cites Visual autoregressive modeling: Scalable image generation via next-scale prediction.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Visual autoregressive modeling: Scalable image generation via next-scale prediction

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.576247Z

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Observation 9e9fa287-7dc5-4cd6-b86c-2a696f43bb1a · outbound

This paper cites Optimal transport: old and new.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Optimal transport: old and new

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.561277Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2e924e16-6bb3-4c3c-a7bc-bcd284838de3 · outbound

This paper cites Attention is all you need.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Attention is all you need

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.548319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 37241137-7460-4e08-b6a9-8b3f2ed0352a · outbound

This paper cites Modeling the trade-off of privacy preservation and activity recognition on low-resolution images.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Modeling the trade-off of privacy preservation and activity recognition on low-resolution images

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.534204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 00723785-2991-4007-9275-8843d1c19b9d · outbound

This paper cites Dolfin: Diffusion Layout Transformers without Autoencoder.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Dolfin: Diffusion Layout Transformers without Autoencoder

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-08T10:20:15.568119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 039cb978-9632-415d-be9e-86bde0b73c6e · outbound

This paper cites Omnicontrolnet: Dual-stage integration for conditional image generation.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Omnicontrolnet: Dual-stage integration for conditional image generation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.520497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7ac55bb1-9854-422f-b717-c7724e86cf64 · outbound

This paper cites Bayesian diffusion models for 3d shape reconstruction.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Bayesian diffusion models for 3d shape reconstruction

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.504861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1a134a4f-2b35-4678-bf8c-d6da55fc52d9 · outbound

This paper cites PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-08T10:20:15.494670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5813a63b-13cd-4c8e-858a-340080a11949 · outbound

This paper cites Uni-3d: A universal model for panoptic 3d scene reconstruction.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Uni-3d: A universal model for panoptic 3d scene reconstruction

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.489652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 22d9cf54-bcfa-499c-bcf4-ba6f9c7266fb · outbound

This paper cites Improved techniques for maximum likelihood estimation for diffusion odes.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Improved techniques for maximum likelihood estimation for diffusion odes

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.473079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d4373439-8682-40ad-bae8-44a42e5af121 · outbound

This paper cites Denoising Diffusion Bridge Models.

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Denoising Diffusion Bridge Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-08T10:20:15.506161Z

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