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Paper Citation Record · LEDGER

High-Order Matching for One-Step Shortcut Diffusion Models

As of 18 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 5 inbound Pith citation observations for arXiv:2502.00688.

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

pith.paper-citation-record.v1
2502.00688 v1

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:10:53.252922Z

measured 69 of 69 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:19:45.943262Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T14:43:31.760566Z

Reference resolution

64 of 64 outbound references displayed

  • verified exact7
  • verified fuzzy6
  • unresolved50
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

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Outbound references

Observation a8a116ab-4b6d-4c32-85e6-b3f95a953620 · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

High-Order Matching for One-Step Shortcut Diffusion Models Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 1

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Observation a2ad1cf4-1db9-4825-bd38-259f5395da61 · outbound

This paper cites Federated Empirical Risk Minimization via Second-Order Method.

High-Order Matching for One-Step Shortcut Diffusion Models Federated Empirical Risk Minimization via Second-Order Method

Reference 3

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source=pdf_text observed=2026-08-09T18:10:52.956380Z digest=sha256:f1355644dd89bed617184542fa3378444c2b14dcd2cb7ae7cd82893f81228674

Observation f6270de9-581b-4ab9-a31f-77be2ebd15ce · outbound

This paper cites Scaling Instruction-Finetuned Language Models.

High-Order Matching for One-Step Shortcut Diffusion Models Scaling Instruction-Finetuned Language Models

Reference 5

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Observation 6dfc83c9-49b8-42a4-bd67-5003348c49e6 · outbound

This paper cites Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent.

High-Order Matching for One-Step Shortcut Diffusion Models Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent

Reference 8

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source=pdf_text observed=2026-08-09T18:10:52.981593Z digest=sha256:3b44b8eba5b77dd8eda66d0e49bb44a36490da4615eacf02dde7bb65d32fd448

Observation 740a45d7-fa27-4226-b724-3e5037942898 · outbound

This paper cites The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity.

High-Order Matching for One-Step Shortcut Diffusion Models The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity

Reference 9

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source=pdf_text observed=2026-08-09T18:10:52.986333Z digest=sha256:de24b5e884c3355412230ecadbbb0e3b36b9cebc198f414833366037f680fc3c

Observation 6bb98ee1-d26e-4508-9481-e915e52aaafd · outbound

This paper cites HSR-Enhanced Sparse Attention Acceleration.

High-Order Matching for One-Step Shortcut Diffusion Models HSR-Enhanced Sparse Attention Acceleration

Reference 11

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source=pdf_text observed=2026-08-09T18:10:52.995341Z digest=sha256:dcc109fa9df4f689085e6b483934196a5ca5541b0fcc538315b61f07987fd139

Observation 5dcdfac2-9044-4406-ab0a-da765aed72dd · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

High-Order Matching for One-Step Shortcut Diffusion Models PaLM: Scaling Language Modeling with Pathways

Reference 12

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source=pdf_text observed=2026-08-09T18:10:52.999696Z digest=sha256:45721e592a288f5c7b580d9a9053db8e9ce30d81c15b3d5c62d9c667fff9b617

Observation 54ea150b-7a9d-4f39-8df3-8f5eced66676 · outbound

This paper cites BERT: Pre- training of deep bidirectional transformers for language understanding.

High-Order Matching for One-Step Shortcut Diffusion Models BERT: Pre- training of deep bidirectional transformers for language understanding

Reference 13

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Observation 93dda562-b80f-4817-8585-f1ff822c44bf · outbound

This paper cites Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs.

High-Order Matching for One-Step Shortcut Diffusion Models Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs

Reference 15

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source=pdf_text observed=2026-08-09T18:10:53.013527Z digest=sha256:dc75ae68cf9eaa0c6dcc1875c4c19e2258121ce9b9aa294e8ff0dc0628a77fd3

Observation 70de0130-5f35-4f9f-92aa-82ecb8c709d6 · outbound

This paper cites A Nearly Optimal Size Coreset Algorithm with Nearly Linear Time.

High-Order Matching for One-Step Shortcut Diffusion Models A Nearly Optimal Size Coreset Algorithm with Nearly Linear Time

Reference 16

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Observation 5454d6c3-f6d8-484b-bf30-e1fe2bc9413e · outbound

This paper cites Flow Matching for Scalable Simulation-Based Inference.

High-Order Matching for One-Step Shortcut Diffusion Models Flow Matching for Scalable Simulation-Based Inference

Reference 17

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source=pdf_text observed=2026-08-09T18:10:53.023097Z digest=sha256:d1d47a04f4c7eabc165aeabda36b518f5a9bc5213ec7c18b4576063881731c2e

Observation f9d84a16-42e1-4594-9e4b-de03e294fb8b · outbound

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

High-Order Matching for One-Step Shortcut Diffusion Models How Far Are We From AGI: Are LLMs All We Need?

Reference 18

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Observation f975aa1e-2fc7-432b-b479-f6d5de375a0a · outbound

This paper cites Flow matching achieves almost minimax optimal convergence.

High-Order Matching for One-Step Shortcut Diffusion Models Flow matching achieves almost minimax optimal convergence

Reference 19

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Observation c9b0672f-8d32-4daa-a928-889bddea2141 · outbound

This paper cites FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models.

High-Order Matching for One-Step Shortcut Diffusion Models FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

Reference 20

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Observation 5d49fe6f-c0b7-44c6-b924-cc0538447dc9 · outbound

This paper cites An Over-parameterized Exponential Regression.

High-Order Matching for One-Step Shortcut Diffusion Models An Over-parameterized Exponential Regression

Reference 22

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Observation f13f7b0b-9201-41b4-ad34-b47e7d401213 · outbound

This paper cites GradientCoin: A Peer-to-Peer Decentralized Large Language Models.

High-Order Matching for One-Step Shortcut Diffusion Models GradientCoin: A Peer-to-Peer Decentralized Large Language Models

Reference 24

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Observation 6c2d43a2-fb5e-4afe-8332-8b0d5823dd2c · outbound

This paper cites An Iterative Algorithm for Rescaled Hyperbolic Functions Regression.

High-Order Matching for One-Step Shortcut Diffusion Models An Iterative Algorithm for Rescaled Hyperbolic Functions Regression

Reference 25

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Observation b2c73810-c689-4461-9678-8eb147a6c4a4 · outbound

This paper cites Improved Noise Schedule for Diffusion Training.

High-Order Matching for One-Step Shortcut Diffusion Models Improved Noise Schedule for Diffusion Training

Reference 26

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Observation 2711a5c6-4232-449a-b24c-059d00dcfd1e · outbound

This paper cites Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency.

High-Order Matching for One-Step Shortcut Diffusion Models Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency

Reference 27

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Observation 9af00e30-069c-40a9-9c35-0261aed91a5e · outbound

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

High-Order Matching for One-Step Shortcut Diffusion Models On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality

Reference 28

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source=pdf_text observed=2026-08-09T18:10:53.074307Z digest=sha256:13db5c801389b37158a4f669b66e71482880c9dd9232117e06185dd65f7b6d81

Observation 9f05d5d9-c265-4244-8d32-b6cd10fab35c · outbound

This paper cites On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs).

High-Order Matching for One-Step Shortcut Diffusion Models On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)

Reference 29

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Observation 91020246-b202-4ad5-9386-d298df3d377b · outbound

This paper cites Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Study.

High-Order Matching for One-Step Shortcut Diffusion Models Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Study

Reference 30

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Observation aa912837-7d5e-44c9-a4ad-189f99102a26 · outbound

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

High-Order Matching for One-Step Shortcut Diffusion Models On Computational Limits and Provably Efficient Criteria of Visual Autoregressive Models: A Fine-Grained Complexity Analysis

Reference 31

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Observation df3a235e-fd85-4c05-b0a5-cd95c1c89113 · outbound

This paper cites Circuit Complexity Bounds for Visual Autoregressive Model.

High-Order Matching for One-Step Shortcut Diffusion Models Circuit Complexity Bounds for Visual Autoregressive Model

Reference 32

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source=pdf_text observed=2026-08-09T18:10:53.093678Z digest=sha256:681800708fdf7b296501b59d6e9cf0c7232bab0c4e6c75f15be0e8f8f809ac8d

Observation aba94119-76b4-4db7-b5eb-3e364891764e · outbound

This paper cites Flow Matching for Generative Modeling.

High-Order Matching for One-Step Shortcut Diffusion Models Flow Matching for Generative Modeling

Reference 34

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Observation 2f0a2627-89fe-4d3a-b0c7-be70ffd950f2 · outbound

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

High-Order Matching for One-Step Shortcut Diffusion Models Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 36

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Observation c64bf636-e496-4847-b28e-d2f40cf20fa1 · outbound

This paper cites Fine-grained at- tention i/o complexity: Comprehensive analysis for backward passes.

High-Order Matching for One-Step Shortcut Diffusion Models Fine-grained at- tention i/o complexity: Comprehensive analysis for backward passes

Reference 37

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Observation 1605ebe5-d8cc-44af-9dc0-e709c90d5226 · outbound

This paper cites On the Computational Capability of Graph Neural Networks: A Circuit Complexity Bound Perspective.

High-Order Matching for One-Step Shortcut Diffusion Models On the Computational Capability of Graph Neural Networks: A Circuit Complexity Bound Perspective

Reference 38

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Observation 6488a6c2-b58a-4d47-8e2a-48a279687712 · outbound

This paper cites A Tighter Complexity Analysis of SparseGPT.

High-Order Matching for One-Step Shortcut Diffusion Models A Tighter Complexity Analysis of SparseGPT

Reference 39

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Observation e0b3202c-653d-4ba1-9a46-7b79b072c294 · outbound

This paper cites Fast second-order method for neural network under small treewidth setting.

High-Order Matching for One-Step Shortcut Diffusion Models Fast second-order method for neural network under small treewidth setting

Reference 40

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

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Observation c4185a59-c14c-47f1-ac1a-796dd1a041dd · outbound

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

High-Order Matching for One-Step Shortcut Diffusion Models Looped ReLU MLPs May Be All You Need as Practical Programmable Computers

Reference 41

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Observation 45c05b6c-2f9c-4757-a6eb-baa9b8f1fa92 · outbound

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

High-Order Matching for One-Step Shortcut Diffusion Models Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time

Reference 42

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Observation 231dd84d-b200-43b0-97a1-ab45170db7ee · outbound

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

High-Order Matching for One-Step Shortcut Diffusion Models Differential Privacy Mechanisms in Neural Tangent Kernel Regression

Reference 43

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source=pdf_text observed=2026-08-09T18:10:53.144828Z digest=sha256:a6d95bf0f86c15c56e167e50568da619752e291d3498bdb38e5c035c50c7e226

Observation f660f7e7-b0a1-4579-ba86-858c682b41af · outbound

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

High-Order Matching for One-Step Shortcut Diffusion Models Unraveling the Smoothness Properties of Diffusion Models: A Gaussian Mixture Perspective

Reference 44

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Observation c29ee117-9dae-4337-b787-28359cb0a1b5 · outbound

This paper cites Quantum Speedups for Approximating the John Ellipsoid.

High-Order Matching for One-Step Shortcut Diffusion Models Quantum Speedups for Approximating the John Ellipsoid

Reference 45

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

source=pdf_text observed=2026-08-09T18:10:53.153731Z digest=sha256:7372c4844afd5aaaa18ea7c0f5487daa044f7a691c5bf531caca8c548fadcab5

Observation 379cf744-4675-492a-bce7-16932306c21a · outbound

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

High-Order Matching for One-Step Shortcut Diffusion Models Score-based Generative Diffusion Models for Social Recommendations

Reference 46

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Observation c35e8525-8903-4c7e-986f-d43d99e98599 · outbound

This paper cites Show Your Work: Scratchpads for Intermediate Computation with Language Models.

High-Order Matching for One-Step Shortcut Diffusion Models Show Your Work: Scratchpads for Intermediate Computation with Language Models

Reference 47

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source=pdf_text observed=2026-08-09T18:10:53.162588Z digest=sha256:cd5e22fd73344b28e7b90d311ed529e69d9c64f3e511526f96d6a1fe8ecb93eb

Observation daff3232-7e78-4790-8242-af4e83dc2848 · outbound

This paper cites GPT-4 Technical Report.

High-Order Matching for One-Step Shortcut Diffusion Models GPT-4 Technical Report

Reference 48

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source=pdf_text observed=2026-08-09T18:10:53.167020Z digest=sha256:2ca206e71850a8ac93c3b11837e5428ff66e718df4360417b60913c7e1d6406c

Observation c432bab2-776b-4655-9fd2-b8e88a929928 · outbound

This paper cites Multisample Flow Matching: Straightening Flows with Minibatch Couplings.

High-Order Matching for One-Step Shortcut Diffusion Models Multisample Flow Matching: Straightening Flows with Minibatch Couplings

Reference 49

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source=pdf_text observed=2026-08-09T18:10:53.171729Z digest=sha256:64a46e06211d44983fdcf27a427d461358060b3d61762454da823a2d2151b954

Observation 352a31af-7092-41ea-acf0-61db3848f2b3 · outbound

This paper cites Is Solving Graph Neural Tangent Kernel Equivalent to Training Graph Neural Network?.

High-Order Matching for One-Step Shortcut Diffusion Models Is Solving Graph Neural Tangent Kernel Equivalent to Training Graph Neural Network?

Reference 50

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local_arxiv, observed 2026-08-09T18:10:53.451584Z

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.

source=pdf_text observed=2026-08-09T18:10:53.177424Z digest=sha256:4a2a43a1f0418cfd6f964b971b741a8d11ff16d07db4dd15863a6e4ec7929785

Observation c80f1561-9502-4547-843e-92bf7c1be88b · outbound

This paper cites Denoising Diffusion Implicit Models.

High-Order Matching for One-Step Shortcut Diffusion Models Denoising Diffusion Implicit Models

Reference 51

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source=pdf_text observed=2026-08-09T18:10:53.182448Z digest=sha256:81a57305d5e12e0897787af32d5069d414a5b778b6ac1c5f205903cd3d66cac0

Observation 03a95559-2b39-45c4-a563-e0aa47942ed0 · outbound

This paper cites A Theoretical Analysis Of Nearest Neighbor Search On Approximate Near Neighbor Graph.

High-Order Matching for One-Step Shortcut Diffusion Models A Theoretical Analysis Of Nearest Neighbor Search On Approximate Near Neighbor Graph

Reference 52

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local_arxiv, observed 2026-08-09T18:10:53.416965Z

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.

source=pdf_text observed=2026-08-09T18:10:53.187135Z digest=sha256:8bc3c3ba54725721d495c44f6a37700d41343ac2fbd985ecc9cd33645a2ce5dc

Observation 0545fbc4-67d9-4dcd-8593-69bae2b3c86e · outbound

This paper cites LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers.

High-Order Matching for One-Step Shortcut Diffusion Models LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers

Reference 54

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source=pdf_text observed=2026-08-09T18:10:53.196082Z digest=sha256:282ea23dffd2fb30e3e4a23d012f1712dbc0ab7768121d3313448d0a7d07c9c5

Observation 788d3fa3-001e-4dba-b718-7b1c1ed03a10 · outbound

This paper cites Numerical Pruning for Efficient Autoregressive Models.

High-Order Matching for One-Step Shortcut Diffusion Models Numerical Pruning for Efficient Autoregressive Models

Reference 56

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source=pdf_text observed=2026-08-09T18:10:53.203470Z digest=sha256:8aa0df2737c7babf7db555fe453402fe0b24e3891693324c51b96ceafab91519

Observation 4bf0e8a4-07c3-4271-a0fe-6187a0a5dfe7 · outbound

This paper cites A Unified Scheme of ResNet and Softmax.

High-Order Matching for One-Step Shortcut Diffusion Models A Unified Scheme of ResNet and Softmax

Reference 57

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source=pdf_text observed=2026-08-09T18:10:53.207540Z digest=sha256:6fbb05d66c9db027d35573c0cd970f40c5b5bb0c64db0849c26e3ca889b91305

Observation 2d7b70b0-7026-4ae3-bdb7-3aa533bf9c7c · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

High-Order Matching for One-Step Shortcut Diffusion Models LLaMA: Open and Efficient Foundation Language Models

Reference 58

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source=pdf_text observed=2026-08-09T18:10:53.211376Z digest=sha256:cb922ac57eb7d6ca54c0e2f374ae54ecec75ddc6cfff370e15b65864cb796e58

Observation edd26f6b-04a2-4bcd-917b-86d90ebaa421 · outbound

This paper cites Dolfin: Diffusion Layout Transformers without Autoencoder.

High-Order Matching for One-Step Shortcut Diffusion Models Dolfin: Diffusion Layout Transformers without Autoencoder

Reference 60

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source=pdf_text observed=2026-08-09T18:10:53.219758Z digest=sha256:359168edca650040a60c4354a0aef222478efd27dce049785ed40b593022d6df

Observation 25cf9e42-d40c-4181-b1e1-daab68565502 · outbound

This paper cites An Investigation of Noise Robustness for Flow-Matching-Based Zero-Shot TTS.

High-Order Matching for One-Step Shortcut Diffusion Models An Investigation of Noise Robustness for Flow-Matching-Based Zero-Shot TTS

Reference 61

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source=pdf_text observed=2026-08-09T18:10:53.224329Z digest=sha256:db906e84ee04abb2859273f2b9ef85f4db393f0f42f4cbd5c58e085168eedeb1

Observation 09ca0546-2698-44cf-adf1-0e3ab8c7b1ac · outbound

This paper cites Symbol tun- ing improves in-context learning in language models.

High-Order Matching for One-Step Shortcut Diffusion Models Symbol tun- ing improves in-context learning in language models

Reference 62

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verified fuzzy
raw_fallback, observed 2026-08-09T18:10:54.672713Z

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.

source=pdf_text observed=2026-08-09T18:10:53.228879Z digest=sha256:d525d2f885e4aca2277def4bd94aa48a4bf8382aa469cbd571785c0cf6e36cc6

Observation 756aca09-de10-43ab-ba78-3159028a399c · outbound

This paper cites TokenCompose: Text-to-Image Diffusion with Token-level Supervision.

High-Order Matching for One-Step Shortcut Diffusion Models TokenCompose: Text-to-Image Diffusion with Token-level Supervision

Reference 63

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local_arxiv, observed 2026-08-09T18:10:53.311838Z

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.

source=pdf_text observed=2026-08-09T18:10:53.233876Z digest=sha256:228f62ae485c58bb4ecdb3ecff6e2eaaecb5e2d656c5b18dfdfc1d8cc21e9ae6

Observation 2aec2ab8-e63a-4fa6-8e5f-4842330f2ec2 · outbound

This paper cites Improving founda- tion models for few-shot learning via multitask finetuning.

High-Order Matching for One-Step Shortcut Diffusion Models Improving founda- tion models for few-shot learning via multitask finetuning

Reference 64

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raw_fallback, observed 2026-08-09T18:10:54.657039Z

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.

source=pdf_text observed=2026-08-09T18:10:53.238525Z digest=sha256:af3d8d0a926cfb430de89f0e408deb3b79a9891d526c6d23782d017851eb7bf8

Observation 3b39557c-6310-4825-aea7-55a736b91d84 · outbound

This paper cites LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention.

High-Order Matching for One-Step Shortcut Diffusion Models LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

Reference 65

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source=pdf_text observed=2026-08-09T18:10:53.243123Z digest=sha256:d1a8125d44eb50eef84796aea8f419e48e8a9eb5cb7a27f63c2a8f1901383137

Observation 9de7ec62-a42e-4f29-b880-771fc62bb7b5 · outbound

This paper cites an unresolved cited work.

High-Order Matching for One-Step Shortcut Diffusion Models Unresolved cited work

Reference 66

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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.

source=pdf_text observed=2026-08-09T18:10:53.247538Z digest=sha256:cd5d6bc8e19759247806bcee51ecac72fec4e9f67c767a5f6121c55ab365674a

Observation ae611811-2162-4851-b020-d95d3700d699 · outbound

This paper cites an unresolved cited work.

High-Order Matching for One-Step Shortcut Diffusion Models Unresolved cited work

Reference 67

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raw_fallback, observed 2026-08-09T18:10:54.630401Z

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.

source=pdf_text observed=2026-08-09T18:10:53.252922Z digest=sha256:190ea2e1e35beec41a52fc92e8ec14be50b9ed6ae966fb6f1aff42e187043ffa

Observation 02e41a7a-4510-4234-bd15-13dc8a6cc742 · outbound

This paper cites The power of scale for parameter- efficient prompt tuning.

High-Order Matching for One-Step Shortcut Diffusion Models The power of scale for parameter- efficient prompt tuning

Reference 1992

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raw_fallback, observed 2026-08-09T18:10:54.719410Z

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.

source=pdf_text observed=2026-08-09T18:10:53.098390Z digest=sha256:138ec86bac9bb656bcf50b2b30fb58784a1fc5a8d9aff3f92983e25c44b03d12

Observation e859a5d1-ce54-47da-96af-7ad55d02d452 · outbound

This paper cites On the Importance of Noise Scheduling for Diffusion Models.

High-Order Matching for One-Step Shortcut Diffusion Models On the Importance of Noise Scheduling for Diffusion Models

Reference 1994

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source=pdf_text observed=2026-08-09T18:10:52.961488Z digest=sha256:29d8f3ad013061a94a526db90c7a99cba8de41eba08f63e896b62d05c4b48c3f

Observation 9fc34ebd-3520-4c75-9248-204fe52be610 · outbound

This paper cites A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time.

High-Order Matching for One-Step Shortcut Diffusion Models A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time

Reference 2014

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source=pdf_text observed=2026-08-09T18:10:53.050576Z digest=sha256:edd4647f594c102dc455e5705e3a75aebccc12b8dc8cdbda321d998f65b507a8

Observation 025b564e-064d-4787-a829-b61c1ba3903c · outbound

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

High-Order Matching for One-Step Shortcut Diffusion Models Modeling the trade-off of privacy preservation and activity recognition on low-resolution images

Reference 2017

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verified fuzzy
raw_fallback, observed 2026-08-09T18:10:54.687266Z

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.

source=pdf_text observed=2026-08-09T18:10:53.215132Z digest=sha256:019a72c8cc847dbab8a6bed4d698eba11871752a191b6a601d2f896d90379c42

Observation a864b27a-be2f-45f5-9602-736bd5c3503b · outbound

This paper cites Randomized and Deterministic Attention Sparsification Algorithms for Over-parameterized Feature Dimension.

High-Order Matching for One-Step Shortcut Diffusion Models Randomized and Deterministic Attention Sparsification Algorithms for Over-parameterized Feature Dimension

Reference 2019

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:10:53.008960Z digest=sha256:b39a02564cbc5f67524d0ff2bcbfc7a9fba442e005d7e5e0001074d057601e8d

Observation a72b2817-6108-4683-96e2-52a4543b019c · outbound

This paper cites LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model.

High-Order Matching for One-Step Shortcut Diffusion Models LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

Reference 2021

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no resolver link, observed 2026-08-09T18:10:53.041680Z

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source=pdf_text observed=2026-08-09T18:10:53.041680Z digest=sha256:026c4c4eeaabc904e30a82efeeaeb8c0f9a33530447b72fa6f36d6c95ed02127

Observation 9a98c84f-5748-4c35-80c8-1b32bd89c8f2 · outbound

This paper cites Fast gradient computation for rope attention in almost linear time.

High-Order Matching for One-Step Shortcut Diffusion Models Fast gradient computation for rope attention in almost linear time

Reference 2022

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no resolver link, observed 2026-08-09T18:10:52.971123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:10:52.971123Z digest=sha256:ab99ff2bbf11dd6a995a7e7bebbb86163e19bd09d529976a01c8c3858e907ca7

Observation 3fd53dbb-23cf-4f90-9ea2-428bc8ce0678 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

High-Order Matching for One-Step Shortcut Diffusion Models On the Opportunities and Risks of Foundation Models

Reference 2023

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no resolver link, observed 2026-08-09T18:10:52.951616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:10:52.951616Z digest=sha256:4826965241ac90311a3a158c22dcbd28d719c102f9ab6aa0a442e7a8734fbe3a

Observation d50ca951-3574-4f63-9b86-0bb08052c43e · outbound

This paper cites Circuit Complexity Bounds for RoPE-based Transformer Architecture.

High-Order Matching for One-Step Shortcut Diffusion Models Circuit Complexity Bounds for RoPE-based Transformer Architecture

Reference 2024

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no resolver link, observed 2026-08-09T18:10:52.976926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:10:52.976926Z digest=sha256:c1036dc562f16d277a533be3ff38c9e7600b442b52dc9df3ade49f0377973b9c

Observation 32a24b45-d249-44d7-9b00-a45a07b65086 · outbound

This paper cites Grams: Gradient Descent with Adaptive Momentum Scaling.

High-Order Matching for One-Step Shortcut Diffusion Models Grams: Gradient Descent with Adaptive Momentum Scaling

Reference 2025

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verified exact
local_arxiv, observed 2026-08-09T18:10:54.314524Z

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.

source=pdf_text observed=2026-08-09T18:10:52.990784Z digest=sha256:ac87272a285f9ca695279585718936e712cb3f0ed709f6af470f7b7b893a129e

Pith citing papers

Observation 87802fc2-e708-444b-bf09-461ce0a1ea86 · inbound

Universal Approximation of Visual Autoregressive Transformers cites this paper.

Universal Approximation of Visual Autoregressive Transformers High-Order Matching for One-Step Shortcut Diffusion Models

Reference 2025

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no resolver link, observed 2026-08-08T16:39:14.107007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T16:39:14.107007Z digest=sha256:4a20979af93c07c308841f47af79a02ab465627051078e3e40bac35e5037ce1c

Observation bb76a354-c470-4734-9513-319da0526a52 · 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 High-Order Matching for One-Step Shortcut Diffusion Models

Reference 5

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no resolver link, observed 2026-08-15T23:19:45.943262Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:19:45.943262Z digest=sha256:d9a6725bc2154f07423385a5093fc931f8a0960b0e2f198bcae32534f31bf226

Observation 69144943-d0d6-4943-ac1d-0af11df1a0e7 · inbound

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform cites this paper.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform High-Order Matching for One-Step Shortcut Diffusion Models

Reference 25

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no resolver link, observed 2026-08-15T20:56:33.855710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:56:33.855710Z digest=sha256:d1b08114a2260fcd7298908851a52c905a5417b676a6c2b4c673b27dd753ebad

Observation 397d7ef6-e300-4ac1-bb06-9320443501db · inbound

Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse cites this paper.

Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse High-Order Matching for One-Step Shortcut Diffusion Models

Reference 9

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no resolver link, observed 2026-08-07T15:10:57.772656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:10:57.772656Z digest=sha256:0fbfd13486ce3c482fcc285c26758e71d3377e91b1aeb951ca8268cb8af18e30

Observation 92c1b818-85ba-4dc2-b5df-3d432c487fe1 · inbound

T2VWorldBench: A Benchmark for Evaluating World Knowledge in Text-to-Video Generation cites this paper.

T2VWorldBench: A Benchmark for Evaluating World Knowledge in Text-to-Video Generation High-Order Matching for One-Step Shortcut Diffusion Models

Reference 3

Resolution
verified exact
local_arxiv, observed 2026-08-06T14:43:31.834179Z

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.

source=pdf_text observed=2026-08-06T14:43:25.915523Z digest=sha256:62f56eb556e8a45047bb8dc03f24599f2267637a69a8574b57a3153e8495936b