Paper Citation Record · LEDGER

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

As of 22 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 1 inbound Pith citation observation for arXiv:2502.08150.

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

pith.paper-citation-record.v1
2502.08150 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

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measured 62 of 62 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:19:45.934862Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T23:19:46.438743Z

Reference resolution

61 of 61 outbound references displayed

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External citation measurements

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Outbound references

Observation e9f96e01-3b8f-4a5a-b841-6f43b9bd9c2a · outbound

This paper cites Wasserstein GAN.

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

Reference 1

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Observation abe44209-402a-4d37-86cf-bd12cf44e3bf · outbound

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

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.855586Z

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Observation 791d6689-4906-4b5f-ba76-25f43aa430a6 · outbound

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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Observation 4e4db1fb-01c6-4c2b-86a7-7df0cc3a0690 · outbound

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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Observation eb76f657-67b6-45ff-9970-12ae899ad648 · outbound

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

Reference 5

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Observation 81e3ff1e-15ca-4a96-8ad8-a79167ea6531 · outbound

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

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T10:20:15.283385Z

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Observation 56358d05-e560-46c8-881b-c12e4bc20d30 · outbound

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

Reference 7

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Observation f5e19cf0-bfaf-408a-8806-cba79c7c5210 · outbound

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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Observation 9f9c1829-9cda-4eee-a86c-d2193d66c148 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.834019Z

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Observation 6c59f864-a325-471c-9800-c930908f977d · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.823126Z

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Observation 29e04608-f74c-4a4d-ab8d-dcf594c4cd69 · outbound

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

Reference 11

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Observation aac03332-ece8-4c21-9c69-fc195b213eba · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.811086Z

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Observation 86688b8b-82f8-40b1-9b55-17b2b21c52a2 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.798794Z

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Observation edec1e4e-88ce-4688-a5ba-423cedf519e7 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.786226Z

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Observation 90c8c7e3-3eaf-48ab-ac5f-7ca7bcf72bbb · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.774046Z

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Observation ad606014-34fc-4b33-9e72-72c995479969 · outbound

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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Observation f7679492-2535-4936-91c6-e46c6747f244 · outbound

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

Reference 17

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Observation 616e3277-f5e7-48f9-936f-66bcc65684d5 · outbound

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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Observation 68c93bd9-5301-4697-af19-190ea7456b27 · outbound

This paper cites Generative adversarial nets.

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

Reference 19

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Observation 3717ff73-f93d-434e-a678-0c7235f08dd2 · outbound

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 20

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Observation f54565df-8e61-429b-96a6-d49e2a99de49 · outbound

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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Observation 9c8f4fc1-dbf1-4682-b39a-93e2c4376111 · outbound

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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Observation c24958bd-68b2-458d-bcfc-b81d05cb6b21 · outbound

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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Observation 680f1e8c-da66-425b-9847-0449f9f620b6 · outbound

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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Observation 1620cf83-ee4b-491e-b85f-b88298b6f711 · outbound

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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Observation 787f103c-74e2-41df-b107-d518f174d434 · outbound

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

Reference 26

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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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Observation d7c26797-9021-4d2b-9ebf-df5a9faf50d6 · outbound

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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Observation 452bb941-a780-4011-bdcf-19c01c95c82a · outbound

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

Reference 29

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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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Observation 85c8fa12-9f90-4f47-b5b1-6076b15fec90 · outbound

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

Reference 31

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This paper cites Auto-Encoding Variational Bayes.

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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This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

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

Reference 34

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This paper cites Exploring the frontiers of softmax: Provable optimization, applications in diffusion model, and beyond.

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

Reference 35

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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

Reference 36

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This paper cites Looped ReLU MLPs May Be All You Need as Practical Programmable Computers.

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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Observation ff9ebee8-c2c2-40b2-88bf-cef2552a20cf · outbound

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

Reference 38

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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

Reference 39

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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

Reference 41

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This paper cites Conditional Generative Adversarial Nets.

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

Reference 42

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.664887Z

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Observation ab82fc22-fc81-4fb8-bb0e-3434a8dec31a · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.651060Z

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Observation 3ec43d36-ffc9-4d18-99ab-4be31b06f2ad · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.628703Z

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Observation 1a502ba6-56de-4cda-a5a2-26db9d7d510e · outbound

This paper cites Aligned diffusion schr \"o dinger bridges.

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

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:20:16.616846Z

Source-reported events for the cited work

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Observation 0f354b20-a612-422a-a6b2-a7ff902e8294 · outbound

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

Source-reported events for the cited work

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Observation 3ea42ac4-0561-4b8c-8c23-e45769098b82 · outbound

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

Source-reported events for the cited work

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

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

Source-reported events for the cited work

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

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

Source-reported events for the cited work

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 30b3f5d9-030c-4d11-bd7f-8c9de14228f9 · inbound

T2VTextBench: A Human Evaluation Benchmark for Textual Control in Video Generation Models cites this paper.

T2VTextBench: A Human Evaluation Benchmark for Textual Control in Video Generation Models Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:19:46.442642Z

Source-reported events for the cited work

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