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Paper Citation Record · LEDGER

Numerical Pruning for Efficient Autoregressive Models

As of 15 August 2026, this Paper Citation Record lists 100 of 168 outbound references and 2 inbound Pith citation observations for arXiv:2412.12441.

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

pith.paper-citation-record.v1
2412.12441 v1

Coverage vector

measured 100 of 168 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:11:27.166202Z

measured 102 of 102 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:10:53.203470Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T15:11:03.194661Z

Reference resolution

100 of 168 outbound references displayed

  • verified exact8
  • verified fuzzy0
  • unresolved91
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1c23f162-dc2b-402f-87ba-36070731d438 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Numerical Pruning for Efficient Autoregressive Models , " * write output.state after.block = add.period write newline

Reference 1

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Observation 9867d48b-c5af-4d32-9978-a760d6412e54 · outbound

This paper cites write newline.

Numerical Pruning for Efficient Autoregressive Models write newline

Reference 2

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Observation f7c1f08e-4822-4e2e-b138-9eac169e15da · outbound

This paper cites GPT-4 Technical Report.

Numerical Pruning for Efficient Autoregressive Models GPT-4 Technical Report

Reference 3

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Observation d441e4ee-9e1d-4605-b450-4fdbc32f4000 · outbound

This paper cites Bypass Exponential Time Preprocessing: Fast Neural Network Training via Weight-Data Correlation Preprocessing.

Numerical Pruning for Efficient Autoregressive Models Bypass Exponential Time Preprocessing: Fast Neural Network Training via Weight-Data Correlation Preprocessing

Reference 4

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Observation 49dbb188-5b05-4540-95db-9c4bc837f5c1 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 5

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Observation 7c8c9773-de10-4c8c-883e-21969a75b7e0 · outbound

This paper cites A.; and Wainwright, M.

Numerical Pruning for Efficient Autoregressive Models A.; and Wainwright, M

Reference 6

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Observation 7d1058f3-af77-41d9-85c9-33629ac93b02 · outbound

This paper cites Fluctuation-based Adaptive Structured Pruning for Large Language Models.

Numerical Pruning for Efficient Autoregressive Models Fluctuation-based Adaptive Structured Pruning for Large Language Models

Reference 7

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Observation 21cc0c12-1b5a-46ba-b0b5-0af1392f9d5f · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 8

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Observation ce97fc70-0199-4b3d-a3ea-76b397085eaa · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 9

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Observation e35ddc68-18e1-4c8f-b730-70cca16e0432 · outbound

This paper cites S.; Hu, W.; Li, Z.; Salakhutdinov, R.

Numerical Pruning for Efficient Autoregressive Models S.; Hu, W.; Li, Z.; Salakhutdinov, R

Reference 10

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Observation 58de5e41-59a0-4600-a5d5-6ceac5e1ad53 · outbound

This paper cites SliceGPT: Compress Large Language Models by Deleting Rows and Columns.

Numerical Pruning for Efficient Autoregressive Models SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 11

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Observation 46f5240d-fd02-4318-8667-6169f1971cc8 · outbound

This paper cites L.; Bousquet, O.; and Mendelson, S.

Numerical Pruning for Efficient Autoregressive Models L.; Bousquet, O.; and Mendelson, S

Reference 12

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Observation 0da6b6ff-c866-4153-8de3-638138dfa54e · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 13

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Observation fdfffeab-2388-48d7-8555-e24ab680b3e1 · outbound

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

Numerical Pruning for Efficient Autoregressive Models Federated Empirical Risk Minimization via Second-Order Method

Reference 14

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Observation a445d9bd-9b7a-41ea-b7dd-59f586a39c63 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 15

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Observation 5de42c2f-db0c-4413-bf5f-ab42ec2d3151 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 16

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Observation 09756181-5b5b-417b-917b-9b6da7b42583 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 17

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Observation 6ebe249e-fe25-4edd-b4a9-2a31a5d5eb6a · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 18

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Observation 08bc0baf-dca6-4868-9ac4-253f328120ff · outbound

This paper cites Training (Overparametrized) Neural Networks in Near-Linear Time.

Numerical Pruning for Efficient Autoregressive Models Training (Overparametrized) Neural Networks in Near-Linear Time

Reference 19

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Source-reported events for the cited work

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Observation b8fc83ed-91f9-4e27-a64d-17256dae9b29 · outbound

This paper cites Algorithm and Hardness for Dynamic Attention Maintenance in Large Language Models.

Numerical Pruning for Efficient Autoregressive Models Algorithm and Hardness for Dynamic Attention Maintenance in Large Language Models

Reference 20

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Observation 6b062739-8a2f-411c-8218-78c40bf001a7 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 21

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Observation ac556722-e26a-4cda-8be8-12655d14079c · outbound

This paper cites Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems.

Numerical Pruning for Efficient Autoregressive Models Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems

Reference 22

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Observation 4a0d4c79-dacc-486a-968e-d23cb87147ee · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 23

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Observation 1194b743-1b9a-403c-b6db-400078b521c8 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 24

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Observation d530ae13-cf75-4a9a-87da-ede2e826a662 · outbound

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

Numerical Pruning for Efficient Autoregressive Models Circuit Complexity Bounds for RoPE-based Transformer Architecture

Reference 25

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Observation 6064b54f-7ea6-4ec2-8904-08c8d1b2efad · outbound

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

Numerical Pruning for Efficient Autoregressive Models Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent

Reference 26

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Observation 9c5b33e9-0c68-4216-8a3b-9939d8131743 · outbound

This paper cites HSR-Enhanced Sparse Attention Acceleration.

Numerical Pruning for Efficient Autoregressive Models HSR-Enhanced Sparse Attention Acceleration

Reference 27

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Observation a96f381c-b648-4054-a971-81b9e961b4d6 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 28

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Observation 48eae7ba-db4a-4e2b-b2af-523d127d01df · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Numerical Pruning for Efficient Autoregressive Models BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 29

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Observation c5a76d25-a374-4606-9d34-f201e49d7a93 · outbound

This paper cites What Does BERT Look At? An Analysis of BERT's Attention.

Numerical Pruning for Efficient Autoregressive Models What Does BERT Look At? An Analysis of BERT's Attention

Reference 30

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Observation f375386f-cfcc-48fc-b126-8ba0a028baff · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Numerical Pruning for Efficient Autoregressive Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 31

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Observation cea08c23-92ef-4227-86b0-4ddbe433c5aa · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Numerical Pruning for Efficient Autoregressive Models Training Verifiers to Solve Math Word Problems

Reference 32

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Observation 19cc58de-6738-4308-bbe0-e9bb98311704 · outbound

This paper cites B.; Lee, Y.

Numerical Pruning for Efficient Autoregressive Models B.; Lee, Y

Reference 33

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Observation 777120cf-a64d-486f-9810-d07010b0a9e4 · outbound

This paper cites A direct formulation for sparse PCA using semidefinite programming.

Numerical Pruning for Efficient Autoregressive Models A direct formulation for sparse PCA using semidefinite programming

Reference 34

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 8c083fec-d2bb-40a1-9c07-6a0a22b2fa65 · outbound

This paper cites Constant Step Size Least-Mean-Square: Bias-Variance Trade-offs and Optimal Sampling Distributions.

Numerical Pruning for Efficient Autoregressive Models Constant Step Size Least-Mean-Square: Bias-Variance Trade-offs and Optimal Sampling Distributions

Reference 35

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local_arxiv, observed 2026-08-11T14:11:28.932715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 24b36cad-59aa-45d3-8bfd-16910e63cc79 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 36

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source=arxiv_source observed=2026-08-11T14:11:26.924819Z digest=sha256:611225c8e3f8083f357352117a6baa886764fbf7ff10bf1a6765c5dfa5b9d332

Observation b131ed28-bd46-42cc-9cf4-0d4f5a577b2a · outbound

This paper cites Attention Scheme Inspired Softmax Regression.

Numerical Pruning for Efficient Autoregressive Models Attention Scheme Inspired Softmax Regression

Reference 37

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source=arxiv_source observed=2026-08-11T14:11:26.927872Z digest=sha256:40703790c08d76a5ba49bc270a3211ef5bc20ffb21b8b6cdc993e93937facf8c

Observation 8fd3a957-14a6-4972-b27a-fcbade682099 · outbound

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

Numerical Pruning for Efficient Autoregressive Models Randomized and Deterministic Attention Sparsification Algorithms for Over-parameterized Feature Dimension

Reference 38

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Observation 7438ad60-30a7-4d3d-a21b-8e8a19428365 · outbound

This paper cites Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights.

Numerical Pruning for Efficient Autoregressive Models Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights

Reference 39

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Observation fc6b1261-7d9c-4b72-b14c-6aebe12227f9 · outbound

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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 40

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Observation 212cc88d-cd7c-4d35-807e-4199eb597910 · outbound

This paper cites Robust Estimators in High Dimensions without the Computational Intractability.

Numerical Pruning for Efficient Autoregressive Models Robust Estimators in High Dimensions without the Computational Intractability

Reference 41

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source=arxiv_source observed=2026-08-11T14:11:26.943120Z digest=sha256:cbeb359f63a1dd1dd92cfadfdeacf7d19440f278af58eae0922dfcfcf8bbb17a

Observation c8477f99-cdc1-4e37-bb4e-cafb3f1deb9e · outbound

This paper cites A Nearly-Linear Time Algorithm for Linear Programs with Small Treewidth: A Multiscale Representation of Robust Central Path.

Numerical Pruning for Efficient Autoregressive Models A Nearly-Linear Time Algorithm for Linear Programs with Small Treewidth: A Multiscale Representation of Robust Central Path

Reference 42

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source=arxiv_source observed=2026-08-11T14:11:26.946955Z digest=sha256:8b7fb550145eadb3b24714e11f895b832a0653f89c2794fb438ec2c81928f14e

Observation f1553984-7751-4c8d-94b3-f459c030ef37 · outbound

This paper cites Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection.

Numerical Pruning for Efficient Autoregressive Models Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection

Reference 43

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source=arxiv_source observed=2026-08-11T14:11:26.951701Z digest=sha256:4431d36c500fc8370d8ad67731d6560e0c76f282bf16f77c202a7fddb4e61d40

Observation 51784a4e-2868-4f30-ab38-b78aca0448ec · outbound

This paper cites Gradient Descent Provably Optimizes Over-parameterized Neural Networks.

Numerical Pruning for Efficient Autoregressive Models Gradient Descent Provably Optimizes Over-parameterized Neural Networks

Reference 44

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source=arxiv_source observed=2026-08-11T14:11:26.956531Z digest=sha256:13a20981c94569fe9ddfd07b4469683fca69f7c2579dfa2024eb18fdde29ee45

Observation 7a5bade8-72f0-4c80-85e9-302210e1bca6 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 45

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source=arxiv_source observed=2026-08-11T14:11:26.961024Z digest=sha256:e55dad740c7b19044cb555072bfe97ddb6f8105a8a869bfa52ece0e4c090fdaf

Observation fe44e09b-2a00-4e3a-9e7b-07071814b7fc · outbound

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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 46

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source=arxiv_source observed=2026-08-11T14:11:26.965318Z digest=sha256:ad3320f37f42c565b41c85063330e58f119c657d5d3fbb0f7138ebb4239bdaed

Observation 02a558c5-7db1-4f49-a781-f70e1dd258f6 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 47

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source=arxiv_source observed=2026-08-11T14:11:26.969862Z digest=sha256:bbed2f8dd149f3ace70bb04dad7f634cd85fabba66291b0429e474c42b13e56c

Observation 7fa1f912-2f50-4c6d-99d7-f187ef8faa5f · outbound

This paper cites M.; and Sidford, A.

Numerical Pruning for Efficient Autoregressive Models M.; and Sidford, A

Reference 48

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source=arxiv_source observed=2026-08-11T14:11:26.974417Z digest=sha256:52e68a25fbbe1d0a322bcbc016ec0d2ba5dae102415e7938b4e0a1bf7ea3138c

Observation 1a1a41e1-2011-46eb-a0c6-6ba73e354c58 · outbound

This paper cites An Over-parameterized Exponential Regression.

Numerical Pruning for Efficient Autoregressive Models An Over-parameterized Exponential Regression

Reference 49

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source=arxiv_source observed=2026-08-11T14:11:26.978076Z digest=sha256:222cd6ed16fb72973df158ffde456822ed2af2eea27239fb7c39bddfc063215b

Observation 0b480c9a-a69f-4621-bb48-366cece5ede9 · 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.

Numerical Pruning for Efficient Autoregressive 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 50

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source=arxiv_source observed=2026-08-11T14:11:26.982073Z digest=sha256:b31ee497e30cd37c950f537a508682e0f9aa998e5d1f5871f5c2c8bfa1afebc1

Observation 7babcac9-2796-4618-802d-cc9055ef9976 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 51

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source=arxiv_source observed=2026-08-11T14:11:26.985810Z digest=sha256:db3bd23f1b9f4c74ec6088410bb6a79ed461f48668d151fb96b28c04fccfca16

Observation 44bd6e46-d7bd-48f7-ae2d-a9aa36ee6715 · outbound

This paper cites Differentially Private Attention Computation.

Numerical Pruning for Efficient Autoregressive Models Differentially Private Attention Computation

Reference 52

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source=arxiv_source observed=2026-08-11T14:11:26.989227Z digest=sha256:d9c228e63a51e2fd81337ef30229371790e82e1ed97e07ee623eea97f63d9667

Observation 15c4b4c2-ae01-462c-9ce5-3a2bb670db9e · outbound

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

Numerical Pruning for Efficient Autoregressive Models An Iterative Algorithm for Rescaled Hyperbolic Functions Regression

Reference 53

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source=arxiv_source observed=2026-08-11T14:11:26.992847Z digest=sha256:7cd64de756f72b678599c6f41b54f95a7177f8e7f7dd7a523301cafc48c868c4

Observation 8fadf5cc-b654-4162-a2b9-bf47c094fc60 · outbound

This paper cites Quantum Speedup for Spectral Approximation of Kronecker Products.

Numerical Pruning for Efficient Autoregressive Models Quantum Speedup for Spectral Approximation of Kronecker Products

Reference 54

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local_arxiv, observed 2026-08-11T14:11:28.698545Z

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source=arxiv_source observed=2026-08-11T14:11:26.996611Z digest=sha256:de450c45e764befafe5a492536fc5445a7fd4f3a2cbf84594f455d969b2284ce

Observation d1bc78c2-6544-48ca-b80d-7e09f2d2d71c · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 55

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source=arxiv_source observed=2026-08-11T14:11:27.000117Z digest=sha256:0234ad04f84df6402e193f3a9e3e658b9bf90ea71dea917fcff17e3dd7da9dfa

Observation 7f22d504-5b98-4299-b9a5-25cdbb770463 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 56

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source=arxiv_source observed=2026-08-11T14:11:27.003567Z digest=sha256:50a546632ffa379c5d62ac6acda131489438ec1020f8c6cfc3eb5109edcd1572

Observation 2d412280-ed29-40a7-b4d4-3d6ab196d620 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 57

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source=arxiv_source observed=2026-08-11T14:11:27.007074Z digest=sha256:21b33b4f1a52a00b460a92dcbf7319919bf8c3fe26ca4c5175af4635c2caef3a

Observation 0157253d-87e2-4fd3-b81d-c89efd72386e · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 58

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source=arxiv_source observed=2026-08-11T14:11:27.010505Z digest=sha256:6a850442d62bbfcde08932a6b08149fc3a830d0ad2363b57ec18c40d85903282

Observation 475f8bbe-670a-4978-9d92-64691aaeddc1 · outbound

This paper cites Fourier Circuits in Neural Networks and Transformers: A Case Study of Modular Arithmetic with Multiple Inputs.

Numerical Pruning for Efficient Autoregressive Models Fourier Circuits in Neural Networks and Transformers: A Case Study of Modular Arithmetic with Multiple Inputs

Reference 59

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source=arxiv_source observed=2026-08-11T14:11:27.013958Z digest=sha256:713f09e92bb28d106debf46a2cb225e248f8114db8baaa18322410c7eadc5a66

Observation ecc92257-f3c7-4402-8db4-d7a245855701 · outbound

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

Numerical Pruning for Efficient Autoregressive Models Differential Privacy Mechanisms in Neural Tangent Kernel Regression

Reference 60

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source=arxiv_source observed=2026-08-11T14:11:27.017827Z digest=sha256:3846a30dd6026b6aca1ba090832c05af2dcafa1050a3ca035b7709341ffe4ae7

Observation d20432d1-1325-4723-940c-14bb35596db3 · outbound

This paper cites A Faster Small Treewidth SDP Solver.

Numerical Pruning for Efficient Autoregressive Models A Faster Small Treewidth SDP Solver

Reference 61

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source=arxiv_source observed=2026-08-11T14:11:27.021565Z digest=sha256:c40e877f5f4ae6cd8bfbdec481c1e1983d9a892a5876af8468628b99bf27f99e

Observation 1c476d48-7b8a-4adf-b59a-9e80117109dd · outbound

This paper cites Faster Algorithms for Structured Linear and Kernel Support Vector Machines.

Numerical Pruning for Efficient Autoregressive Models Faster Algorithms for Structured Linear and Kernel Support Vector Machines

Reference 62

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source=arxiv_source observed=2026-08-11T14:11:27.025518Z digest=sha256:4af994603146aa1991619466bb016cf90d61992a89513bf5bca848465971c6db

Observation 18ca8f61-e376-4aa8-bb7d-dd1efc03a104 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 63

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source=arxiv_source observed=2026-08-11T14:11:27.029538Z digest=sha256:b480acebb6c0f06affc53beb4a52da8b2fd6a84c29a80017ba26f798970b36f7

Observation e504df6c-cba4-4dd7-94c2-d829e65358a6 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 64

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source=arxiv_source observed=2026-08-11T14:11:27.032870Z digest=sha256:7459215e4ca2586758a1cf650b6649ebbc0d672dd5e74713cdba47ef8f7a2b60

Observation 95beac8a-c205-46b5-adfd-c84133eec324 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 65

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source=arxiv_source observed=2026-08-11T14:11:27.036443Z digest=sha256:fb15fcb78e0c023c8bcbd0e79ee8f8b77ce4dcdbd6a4878559ab2026b90f1ce2

Observation 828ae020-bb8f-45d8-871f-128c63cf223f · outbound

This paper cites Designing and Interpreting Probes with Control Tasks.

Numerical Pruning for Efficient Autoregressive Models Designing and Interpreting Probes with Control Tasks

Reference 66

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source=arxiv_source observed=2026-08-11T14:11:27.040097Z digest=sha256:d5c78a88b609b4d5ce8766c218ec57b473c83af00cd215cd95b4a05c5c11ad5f

Observation 2505b93b-5a0e-43a3-a906-9b3f46a2a4c8 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 67

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source=arxiv_source observed=2026-08-11T14:11:27.043902Z digest=sha256:cdf032d48250e02df6f0fdcfbb7ee60d462aae498a5b4c09555721db72115aa0

Observation c2f461c1-7ae9-4f76-bae0-ac2de985b86c · outbound

This paper cites Solving SDP Faster: A Robust IPM Framework and Efficient Implementation.

Numerical Pruning for Efficient Autoregressive Models Solving SDP Faster: A Robust IPM Framework and Efficient Implementation

Reference 68

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source=arxiv_source observed=2026-08-11T14:11:27.048436Z digest=sha256:8125cd4d447f190e99869b3c94825670ba8559357455a8f351bd6670ff27729c

Observation 1915dddc-6256-4d49-baa8-3c166e69a392 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 69

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source=arxiv_source observed=2026-08-11T14:11:27.052359Z digest=sha256:4dfd70c5ea1b63a8f59f384e226ee8293feaf44ac6d9c34f066df3d597638d34

Observation dba28890-791a-405d-8d9c-2c70eb4420c4 · outbound

This paper cites Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing.

Numerical Pruning for Efficient Autoregressive Models Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing

Reference 70

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local_arxiv, observed 2026-08-11T14:11:28.542018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T14:11:27.055800Z digest=sha256:fe0acb1662343dca115b42f0f008482a44d0e0e21d68f1301d214277e2f492ca

Observation 99fe9eb9-6565-4438-bce1-f46539cc7e64 · outbound

This paper cites Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks.

Numerical Pruning for Efficient Autoregressive Models Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks

Reference 71

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source=arxiv_source observed=2026-08-11T14:11:27.060164Z digest=sha256:00e4054e8a169603d042c680a13208bf45351eb8941604928f63b7009786b1b5

Observation 6a53e5dc-36dc-4bbd-9ba9-b15471cd222b · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 72

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source=arxiv_source observed=2026-08-11T14:11:27.064179Z digest=sha256:729756bc7155b6370c195c1bcef9f175473eae5dd08d7f1c6c71a368a16c3fa3

Observation 78df230b-76a0-4745-96d0-fb7fb6ef3045 · outbound

This paper cites T.; Padmanabhan, S.; and Song, Z.

Numerical Pruning for Efficient Autoregressive Models T.; Padmanabhan, S.; and Song, Z

Reference 73

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source=arxiv_source observed=2026-08-11T14:11:27.067209Z digest=sha256:cb4348ccf6be93e618a8184ed331ff7af59522edfe9d50d1f5d5bea6671ad0db

Observation 3b552aee-6ac6-4d06-b9ac-9e8177ec3dfe · outbound

This paper cites An Improved Cutting Plane Method for Convex Optimization, Convex-Concave Games and its Applications.

Numerical Pruning for Efficient Autoregressive Models An Improved Cutting Plane Method for Convex Optimization, Convex-Concave Games and its Applications

Reference 74

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metadata mismatch
local_arxiv, observed 2026-08-11T14:11:28.516994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T14:11:27.070032Z digest=sha256:f49570674c75c1fa957b2457689e0bd7fa2c52a6b761127709be8d99186d479b

Observation 0d15e73a-ef83-44b4-9916-904cb421e935 · outbound

This paper cites Faster Dynamic Matrix Inverse for Faster LPs.

Numerical Pruning for Efficient Autoregressive Models Faster Dynamic Matrix Inverse for Faster LPs

Reference 75

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source=arxiv_source observed=2026-08-11T14:11:27.073634Z digest=sha256:7a7ea6f077442bed3d92d5a5ae723ed7ed0559b1368cd7eb34421480e07d25c4

Observation 8c5093a4-6f79-4e5e-875f-add31d058cc0 · outbound

This paper cites T.; Ge, R.; and Jordan, M.

Numerical Pruning for Efficient Autoregressive Models T.; Ge, R.; and Jordan, M

Reference 76

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source=arxiv_source observed=2026-08-11T14:11:27.077538Z digest=sha256:36767b23ac433e523af3974189fdcc7fd4476c37db872afda7bcc7ee220f659f

Observation 0ad4040e-fd55-4d77-9fbf-a6f1be5149fb · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 77

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source=arxiv_source observed=2026-08-11T14:11:27.082012Z digest=sha256:d700ca3c96b1f8ab0104083146db1bbe221528572b76ba33cdee5cb8fb3b2663

Observation 23eeeb72-3dc4-4403-8ab9-e2b88e0fd9a7 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 78

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source=arxiv_source observed=2026-08-11T14:11:27.085660Z digest=sha256:34ca6911fc13666f07d809f46076ba343c95dc56686200db4f6260d8b1fe62de

Observation 794e7b36-7598-46de-96ec-2714a59993f4 · outbound

This paper cites PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels.

Numerical Pruning for Efficient Autoregressive Models PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels

Reference 79

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source=arxiv_source observed=2026-08-11T14:11:27.089378Z digest=sha256:e812aa46a63e256dfae4f307e81ad12e13573001eaee7a397759cee01518fc7a

Observation abcdd665-4b31-423b-a596-6e25f0a502d2 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 80

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Observation c3c46917-17d8-49c2-b438-6fe08ecc52c0 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 81

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Observation 8d65b6a8-0cda-4401-a0b1-05aad14059df · outbound

This paper cites Improved Precision and Recall Metric for Assessing Generative Models.

Numerical Pruning for Efficient Autoregressive Models Improved Precision and Recall Metric for Assessing Generative Models

Reference 82

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Observation 187a4ebf-82fb-4dd0-b934-69a23554666b · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 83

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Observation a41ab0d6-8604-472e-a737-b73c3e393b23 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 84

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Observation aa434a82-1710-45af-a880-3d7ece08fcd1 · outbound

This paper cites D.; Shen, R.; Song, Z.; Wang, M.; et al.

Numerical Pruning for Efficient Autoregressive Models D.; Shen, R.; Song, Z.; Wang, M.; et al

Reference 85

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Observation 5b629ed6-1b2c-4ee5-b7e5-225f66aee5a1 · outbound

This paper cites T.; Song, Z.; and Zhang, Q.

Numerical Pruning for Efficient Autoregressive Models T.; Song, Z.; and Zhang, Q

Reference 86

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Observation 2d3b33ce-246d-4155-b71c-1f7202d2b3f6 · outbound

This paper cites The Closeness of In-Context Learning and Weight Shifting for Softmax Regression.

Numerical Pruning for Efficient Autoregressive Models The Closeness of In-Context Learning and Weight Shifting for Softmax Regression

Reference 87

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Observation d6ee1b4f-92cf-45d0-aeed-0fa96f17a403 · outbound

This paper cites Autoregressive Image Generation without Vector Quantization.

Numerical Pruning for Efficient Autoregressive Models Autoregressive Image Generation without Vector Quantization

Reference 88

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Observation 65435117-4830-4d30-838a-85c955e42d73 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 89

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Observation e9637e20-5374-4bd1-99d5-df4ffcff7b4f · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 90

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Observation 44205a40-36be-4fbc-bca2-7ade82eb4df9 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 91

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source=arxiv_source observed=2026-08-11T14:11:27.134163Z digest=sha256:9dbadbf0271509e484caf3f80bee020d11c4ae67707ec366303b46bb313aa963

Observation 82c8a359-0168-4af6-b815-45a67f823c4a · outbound

This paper cites Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models.

Numerical Pruning for Efficient Autoregressive Models Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models

Reference 92

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Source-reported events for the cited work

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Observation 84e6a0eb-192c-4965-9a48-dc2f86e8c0a0 · outbound

This paper cites an unresolved cited work.

Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 93

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Observation a9cb1ce0-5bbc-40ea-a341-291be236df55 · outbound

This paper cites Less is More: Data Pruning for Faster Adversarial Training.

Numerical Pruning for Efficient Autoregressive Models Less is More: Data Pruning for Faster Adversarial Training

Reference 94

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source=arxiv_source observed=2026-08-11T14:11:27.144019Z digest=sha256:7dfecb3416ed80ff540e19caf32c552f27342f3e78af17a0777793747c566c04

Observation d6a01068-2488-45f7-aa7a-158b9df60c25 · outbound

This paper cites Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization.

Numerical Pruning for Efficient Autoregressive Models Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization

Reference 95

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source=arxiv_source observed=2026-08-11T14:11:27.147459Z digest=sha256:3e400f73f82032fb309ef15966115d918099b43adee65851863614bb12bbed8e

Observation af064a60-a981-46e0-937e-24c0d47333b2 · outbound

This paper cites Local Convergence of Approximate Newton Method for Two Layer Nonlinear Regression.

Numerical Pruning for Efficient Autoregressive Models Local Convergence of Approximate Newton Method for Two Layer Nonlinear Regression

Reference 96

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Observation a4f34a1b-6875-4ff1-a355-516dd27c99c0 · outbound

This paper cites Solving Regularized Exp, Cosh and Sinh Regression Problems.

Numerical Pruning for Efficient Autoregressive Models Solving Regularized Exp, Cosh and Sinh Regression Problems

Reference 97

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source=arxiv_source observed=2026-08-11T14:11:27.153902Z digest=sha256:371d7be46b28b0cc3c194a9a4fe538f185ad08a850c7f84085ba1f29a0d27474

Observation 827c5401-b6c8-4dea-8623-d3494d0ea2dc · outbound

This paper cites Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix.

Numerical Pruning for Efficient Autoregressive Models Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix

Reference 98

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source=arxiv_source observed=2026-08-11T14:11:27.157691Z digest=sha256:277fbfe6e86b714e8548c5da0d981a16aa55f337e6d1c1ae26b10c8f08385885

Observation d47fb572-975d-4a5f-bf92-711ff7fed5ce · outbound

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

Numerical Pruning for Efficient Autoregressive Models Looped ReLU MLPs May Be All You Need as Practical Programmable Computers

Reference 99

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source=arxiv_source observed=2026-08-11T14:11:27.161894Z digest=sha256:90ee5b5882a845f65e06dc4bd66bb5cc630782543d0ef553e49d226f474e3508

Observation b47a329f-5930-4b9f-815a-e636e020f842 · outbound

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

Numerical Pruning for Efficient Autoregressive Models Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time

Reference 100

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source=arxiv_source observed=2026-08-11T14:11:27.166202Z digest=sha256:9890fb894beadcfc568c49fcf5dfa97773c1bdb41b3bc992b30fd1ed476d9050

Pith citing papers

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

High-Order Matching for One-Step Shortcut Diffusion Models cites this paper.

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=sha256:5667032ca335f2a2ed5ba5a09e1ec602f500146f036d726b11c82c6f62e5266e

Observation 2ab26d5d-2cb1-4703-8da2-06c387b504c4 · 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 Numerical Pruning for Efficient Autoregressive Models

Reference 57

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verified exact
local_arxiv, observed 2026-08-07T15:11:03.228296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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