Paper Citation Record · LEDGER

Numerical Pruning for Efficient Autoregressive Models

As of 19 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 2026-08-18T06:34:40.430872+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

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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

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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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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 15

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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 16

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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 17

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

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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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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

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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

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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 23

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

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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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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 36

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Numerical Pruning for Efficient Autoregressive Models Attention Scheme Inspired Softmax Regression

Reference 37

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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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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 40

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

Reference 44

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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 45

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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 46

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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 47

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

Reference 48

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

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

Reference 49

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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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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 51

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Numerical Pruning for Efficient Autoregressive Models Differentially Private Attention Computation

Reference 52

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

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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 55

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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 56

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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 57

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Numerical Pruning for Efficient Autoregressive Models Unresolved cited work

Reference 58

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

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

Reference 60

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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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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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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.029538Z

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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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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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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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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.043902Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.048436Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.052359Z

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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

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:11:28.542018Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.060164Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.064179Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.067209Z

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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

Resolution: 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 2026-08-18T06:34:40.430872+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.073634Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.077538Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.082012Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.085660Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.089378Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.093188Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.096652Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.100223Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.107742Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.111370Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.114864Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.118676Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.122377Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.126958Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.130508Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.134163Z

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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

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:11:28.389739Z

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.

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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.140984Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.144019Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.147459Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.150759Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.153902Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.157691Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.161894Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:27.166202Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T18:10:53.203470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: 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 2026-08-18T06:34:40.430872+00:00.