Paper Citation Record · LEDGER

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks

As of 10 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 2 inbound Pith citation observations for arXiv:2505.20132.

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

pith.paper-citation-record.v1
2505.20132 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:01:59.087832Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T06:53:09.576838Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

56 of 56 outbound references displayed

verified exact3
verified fuzzy32
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation a240faaa-cf10-408c-9b29-f7f93f0a91f4 · outbound

This paper cites Scaling Laws for Neural Language Models.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Scaling Laws for Neural Language Models

Reference 1

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Source-reported events for the cited work

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Observation 65bc66aa-2588-4b26-b05f-50b4dba34efb · outbound

This paper cites A survey on model compression for large language models.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks A survey on model compression for large language models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:06.490901Z

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Observation 2bd46294-8486-4f82-84e3-028ed8b06b71 · outbound

This paper cites Can Neural Network Memorization Be Localized?.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Can Neural Network Memorization Be Localized?

Reference 3

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Observation aeecb1f8-9391-45bf-8ee4-d9777a7d98d8 · outbound

This paper cites The Super Weight in Large Language Models.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks The Super Weight in Large Language Models

Reference 4

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Source-reported events for the cited work

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Observation 6fb163a0-9802-4ac6-8abe-6eb6055a7068 · outbound

This paper cites Neuron shapley: Discovering the responsible neurons.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Neuron shapley: Discovering the responsible neurons

Reference 5

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Observation 5c467c15-9e62-4d8e-9a57-116aeb987258 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:54.960046Z

Source-reported events for the cited work

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Observation 1ec8c27e-707a-4e23-b7eb-7342479fff64 · outbound

This paper cites Analysing real world data streams with spatio-temporal correlations: Entropy vs. Pearson correlation.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Analysing real world data streams with spatio-temporal correlations: Entropy vs. Pearson correlation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:06.106531Z

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Observation bb5d8a99-5e38-4ad8-93f2-d14b0803b1d4 · outbound

This paper cites Correlation analysis of invasive disease-free survival and overall survival in a real-world population of patients with HR+/HER2–early breast cancer.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Correlation analysis of invasive disease-free survival and overall survival in a real-world population of patients with HR+/HER2–early breast cancer

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:05.959809Z

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Observation e7060d06-1d63-4b60-8fb1-72c54c75bdf6 · outbound

This paper cites Smoothquant: Accurate and efficient post-training quantization for large language models.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Smoothquant: Accurate and efficient post-training quantization for large language models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:05.811925Z

Source-reported events for the cited work

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Observation 67488c36-47eb-4299-9576-bf38307e1c8c · outbound

This paper cites LLM-QAT: Data-Free Quantization Aware Training for Large Language Models.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 10

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Observation 646c1465-8d30-475a-b0e0-f7d118050729 · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks A Simple and Effective Pruning Approach for Large Language Models

Reference 11

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Observation 08acc299-2950-4f73-97cb-e235770b36ae · outbound

This paper cites Llm-pruner: On the structural pruning of large language models.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Llm-pruner: On the structural pruning of large language models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:05.664772Z

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Observation f8053978-33a3-4564-9be1-d00a882462a1 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Distilling the Knowledge in a Neural Network

Reference 13

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Observation a9821e43-7a56-4c41-9dc7-efe1ec775b32 · outbound

This paper cites Model compression via distillation and quantization.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Model compression via distillation and quantization

Reference 14

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Source-reported events for the cited work

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Observation 228b110e-3ce0-47d1-8849-011b38aa4f65 · outbound

This paper cites Combining weight pruning and knowledge distillation for cnn compression.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Combining weight pruning and knowledge distillation for cnn compression

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:05.507125Z

Source-reported events for the cited work

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Observation e664d3b9-0465-474d-a820-eee27a890e8c · outbound

This paper cites A novel tensor decomposition-based efficient detector for low-altitude aerial objects with knowledge distillation scheme.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks A novel tensor decomposition-based efficient detector for low-altitude aerial objects with knowledge distillation scheme

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:05.361487Z

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Observation 726c7f4a-14ad-45bc-b7d2-be05854c8d90 · outbound

This paper cites Density matrix formulation for quantum renormalization groups.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Density matrix formulation for quantum renormalization groups

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:05.200258Z

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Observation 872e702e-147e-466f-b8f3-38e299e16e8f · outbound

This paper cites Tensor networks for complex quantum systems.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Tensor networks for complex quantum systems

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:05.060313Z

Source-reported events for the cited work

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Observation 3a025ab2-094a-4265-a358-f1389a2a11c7 · outbound

This paper cites Matrix product states and projected entangled pair.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Matrix product states and projected entangled pair

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:04.923121Z

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Observation 5e76f715-d7f2-4778-ab7c-f50da733a36c · outbound

This paper cites Tensor Networks Meet Neural Networks: A Survey and Future Perspectives.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Tensor Networks Meet Neural Networks: A Survey and Future Perspectives

Reference 20

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Observation fd15642a-4cb6-41b7-9c21-eb91b8038243 · outbound

This paper cites Efficient tree tensor network states (TTNS) for quantum chemistry: Generalizations of the density matrix renormalization group algorithm.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Efficient tree tensor network states (TTNS) for quantum chemistry: Generalizations of the density matrix renormalization group algorithm

Reference 21

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Observation d9774113-904c-45e7-924b-67ffa552086d · outbound

This paper cites Tensor network factorizations: Relationships between brain structural connectomes and traits.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Tensor network factorizations: Relationships between brain structural connectomes and traits

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:04.635413Z

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Observation 9370bf7c-fe35-4464-81f8-5beab05bab4f · outbound

This paper cites Era of Big Data Processing: A New Approach via Tensor Networks and Tensor Decompositions.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Era of Big Data Processing: A New Approach via Tensor Networks and Tensor Decompositions

Reference 23

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Observation 17cb0103-86f0-4300-b2a4-87a5eb9362d6 · outbound

This paper cites Tensorizing neural networks.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Tensorizing neural networks

Reference 24

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Observation ab21e62e-f4f6-4d8a-8b38-9a7a339d91c3 · outbound

This paper cites Compressing convolutional neural networks with hierarchical Tucker-2 decomposition.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Compressing convolutional neural networks with hierarchical Tucker-2 decomposition

Reference 25

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Observation c8ed4841-82ae-4303-8bff-5fe5c4af2bb3 · outbound

This paper cites Tensor rank learning in CP decomposition via convolutional neural network.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Tensor rank learning in CP decomposition via convolutional neural network

Reference 26

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Observation b99156cc-df1b-4565-b2d3-0852cfb7540c · outbound

This paper cites Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks

Reference 27

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Observation b90ff0e6-6803-4e5d-8d13-4316a1ce647d · outbound

This paper cites Tensor network compressibility of convolutional models.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Tensor network compressibility of convolutional models

Reference 28

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Observation 3cc77e48-4ff2-4ae9-bfe6-0b28644a3211 · outbound

This paper cites CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks

Reference 29

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This paper cites A tensorized transformer for language modeling.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks A tensorized transformer for language modeling

Reference 30

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Observation fa9c7727-ae89-4c6b-b8dd-f8ed352ebf8f · outbound

This paper cites TensorGPT: Efficient Compression of Large Language Models based on Tensor-Train Decomposition.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks TensorGPT: Efficient Compression of Large Language Models based on Tensor-Train Decomposition

Reference 31

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Observation 83fcaada-f3da-4841-ba5a-17d642049e2e · outbound

This paper cites Improving language understanding by generative pre-training.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Improving language understanding by generative pre-training

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:03.923854Z

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Observation 1cdaf886-f8cd-4db8-80e6-9c9a3c0103a3 · outbound

This paper cites ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models

Reference 33

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This paper cites Quantum Large Language Models via Tensor Network Disentanglers.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Quantum Large Language Models via Tensor Network Disentanglers

Reference 34

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This paper cites Machine learning of inductive bias.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Machine learning of inductive bias

Reference 35

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Observation 1df66ca7-36d0-4dd1-835f-c2c5fbad4669 · outbound

This paper cites Inductive biases for deep learning of higher-level cognition.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Inductive biases for deep learning of higher-level cognition

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:03.610298Z

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Observation 6ddea177-d3ff-4bde-9d65-0e22963e1b5c · outbound

This paper cites Geometric deep learning.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Geometric deep learning

Reference 37

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Observation 55a9fb3d-5ab1-4da2-baae-4451672ecf56 · outbound

This paper cites Learning with invariances in random features and kernel models.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Learning with invariances in random features and kernel models

Reference 38

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Observation ab044810-427c-4397-8240-1fe5fc131199 · outbound

This paper cites Mechanism for feature learning in neural networks and backpropagation-free machine learning models.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Mechanism for feature learning in neural networks and backpropagation-free machine learning models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:02.977169Z

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Observation d9d60939-c98c-4477-9fdb-65500f28bae7 · outbound

This paper cites Incremental learning algorithms and applications.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Incremental learning algorithms and applications

Reference 40

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Observation 1847598e-65a0-4bbc-bcdf-6a5fbb07f201 · outbound

This paper cites Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?

Reference 41

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This paper cites Bayesian tensorized neural networks with automatic rank selection.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Bayesian tensorized neural networks with automatic rank selection

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:02.563692Z

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Observation 9b521949-ce4a-4309-8603-1983c451408b · outbound

This paper cites Lightweight tensorized neural networks for hyperspectral image classification.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Lightweight tensorized neural networks for hyperspectral image classification

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:02.397567Z

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Observation 15be2ab5-bb96-4a3e-864b-28d3fe1f0bbf · outbound

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

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks LLaMA: Open and Efficient Foundation Language Models

Reference 44

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This paper cites Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition

Reference 45

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This paper cites On interpretability of artificial neural networks: A survey.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks On interpretability of artificial neural networks: A survey

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:02.204903Z

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Observation ad2b17fa-ae25-40bd-88af-3950e62a571c · outbound

This paper cites Sparse autoencoder.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Sparse autoencoder

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:02.008020Z

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Observation f30b78e9-58d9-4abc-a11a-051e6c13aed7 · outbound

This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 48

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Observation e2bfe8af-0f1a-428d-8a4f-94873e1cd41d · outbound

This paper cites Quantifying Feature Space Universality Across Large Language Models via Sparse Autoencoders.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Quantifying Feature Space Universality Across Large Language Models via Sparse Autoencoders

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:58.499011Z

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Observation a0037fc2-cd9c-441f-ab5c-fec2676b3c71 · outbound

This paper cites Tensor Networks for Explainable Machine Learning in Cybersecurity.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Tensor Networks for Explainable Machine Learning in Cybersecurity

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:01:59.959870Z

Source-reported events for the cited work

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Observation ae3446c7-c402-4508-b37f-01273a13c971 · outbound

This paper cites FPGA-based component-wise LSTM training accelerator for neural granger causality analysis.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks FPGA-based component-wise LSTM training accelerator for neural granger causality analysis

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:01.749939Z

Source-reported events for the cited work

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Observation bf4179e5-7d69-4b13-a50b-06837246b539 · outbound

This paper cites FlexCNN: An end-to-end framework for composing CNN accelerators on FPGA.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks FlexCNN: An end-to-end framework for composing CNN accelerators on FPGA

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:01.491168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0875ed54-8872-40bf-a494-217a3192eb11 · outbound

This paper cites Neural architecture search survey: A hardware perspective.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Neural architecture search survey: A hardware perspective

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:01.303520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9fd17013-612a-4070-9b03-54b1ba9be2fd · outbound

This paper cites Compression of deep neural networks based on quantized tensor decomposition to implement on reconfigurable hardware platforms.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Compression of deep neural networks based on quantized tensor decomposition to implement on reconfigurable hardware platforms

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:02:01.149976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 361d371c-08df-4af0-b5ea-db1f73312fb2 · outbound

This paper cites Successive randomized compression: A randomized algorithm for the compressed MPO-MPS product.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks Successive randomized compression: A randomized algorithm for the compressed MPO-MPS product

Reference 55

Resolution: verified exact; raw_fallback, observed 2026-08-07T14:01:59.719545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0d480852-d6b2-4596-894e-917c909ffdde · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks KAN: Kolmogorov-Arnold Networks

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:59.087832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 10d71b96-fd12-41ca-830c-fc2ee2cb2158 · inbound

From Mechanistic to Compositional Interpretability cites this paper.

From Mechanistic to Compositional Interpretability Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks

Reference 38

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T02:46:18.549960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ccf39a29-9150-44ee-b6dc-1ebd743daf45 · inbound

Fast Tensorization of Neural Networks via Slice-wise Feature Distillation cites this paper.

Fast Tensorization of Neural Networks via Slice-wise Feature Distillation Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-20T06:53:22.963923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.