Paper Citation Record · LEDGER

Towards Universal & Efficient Model Compression via Exponential Torque Pruning

As of 8 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2506.22015.

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

pith.paper-citation-record.v1
2506.22015 v3

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:17:44.034506Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

verified exact0
verified fuzzy4
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 36e2f12c-242c-4541-b801-1ff96174f6fb · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning Distilling the Knowledge in a Neural Network

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:43.137762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4826cb9-448e-47c4-afde-b52b91a1cdc2 · outbound

This paper cites Oscar: Object-semantics aligned pre-training for vision-language tasks.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning Oscar: Object-semantics aligned pre-training for vision-language tasks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:44.703237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b41e03eb-b961-4399-9ea8-aaf53bbbcf6f · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:43.478558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59d43b14-6dba-4866-bc5a-48bc6318a5bb · outbound

This paper cites A Signal Propagation Perspective for Pruning Neural Networks at Initialization.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Reference 1989

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:43.313565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18b1d31d-34f3-4d6a-95d7-64a362a98f2f · outbound

This paper cites Fine-tuning vision transformer using lora for image classification.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning Fine-tuning vision transformer using lora for image classification

Reference 1996

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:44.388563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 695b8328-70d5-4e3d-b8a8-da09a4f98ac9 · outbound

This paper cites To compress, or not to compress: Characterizing deep learning model compression for embedded inference.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning To compress, or not to compress: Characterizing deep learning model compression for embedded inference

Reference 2002

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:44.573245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 039d89c2-fbc9-4190-a271-90e129738e76 · outbound

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

Towards Universal & Efficient Model Compression via Exponential Torque Pruning Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:42.841733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 437dd516-34e5-40d3-bcbf-1ae41f47fd27 · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:43.942787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a66c0dd6-5109-40c1-aabc-e80d3bdc2481 · outbound

This paper cites Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:43.047924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7f84e93-1045-4cb8-82f4-a20294aca475 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:42.965857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1128dd22-35eb-492a-8431-72a5bdb54d22 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:43.679822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18d7b047-7c38-4d8a-99c3-22bb2b1b94de · outbound

This paper cites Neural Pruning via Growing Regularization.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning Neural Pruning via Growing Regularization

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:44.034506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 282d4541-9bd4-46a8-b938-768d71b1ce3b · outbound

This paper cites Reducing Transformer Depth on Demand with Structured Dropout.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning Reducing Transformer Depth on Demand with Structured Dropout

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:42.899383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6334bce2-6313-428b-8b4d-b80175c84310 · outbound

This paper cites Learning multiple layers of features from tiny images.(2009),.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning Learning multiple layers of features from tiny images.(2009),

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:44.836457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6c5e1f7b-e094-4397-b37e-0de846005040 · outbound

This paper cites Graph Attention Networks.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning Graph Attention Networks

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:43.861596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.