Paper Citation Record · LEDGER

On Accelerating Edge AI: Optimizing Resource-Constrained Environments

As of 10 August 2026, this Paper Citation Record lists 100 of 140 outbound references and 3 inbound Pith citation observations for arXiv:2501.15014.

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

pith.paper-citation-record.v1
2501.15014 v2

Coverage vector

measured 100 of 140 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:46:38.527851Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:53:27.672710Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-09T05:45:21.036588Z

Reference resolution

100 of 140 outbound references displayed

verified exact9
verified fuzzy0
unresolved89
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7bbca4b2-ce35-431e-914b-dd0bb73b2712 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.133501Z

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Observation 59eab8df-dad0-45df-95d0-3f4113c80d7a · outbound

This paper cites EfficientQAT: Efficient Quantization-Aware Training for Large Language Models.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments EfficientQAT: Efficient Quantization-Aware Training for Large Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.137485Z

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Observation 94b55ef8-4416-47ec-9d53-d4f6c7a7b80f · outbound

This paper cites Art and Science of Quantizing Large-Scale Models: A Comprehensive Overview.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Art and Science of Quantizing Large-Scale Models: A Comprehensive Overview

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.141662Z

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Observation c4a76823-a8ad-49d0-8412-33019aad2618 · outbound

This paper cites Foundations of Large Language Model Compression -- Part 1: Weight Quantization.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Foundations of Large Language Model Compression -- Part 1: Weight Quantization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.145982Z

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Observation 26a0b5a8-ce62-47e0-8b3e-d17450655d66 · outbound

This paper cites AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.150913Z

Source-reported events for the cited work

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Observation 5f13e718-ef1b-42d3-8c4a-1734987aa677 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments QLoRA: Efficient Finetuning of Quantized LLMs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.155470Z

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Observation 18d71ae2-03b1-4932-acb5-bcf93f78f6d4 · outbound

This paper cites Integer or floating point? new outlooks for low-bit quantization on large language models.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Integer or floating point? new outlooks for low-bit quantization on large language models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.159769Z

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Observation 80b729b7-d83c-4bc4-8222-c2ed17484916 · outbound

This paper cites RPTQ: Reorder-based Post-training Quantization for Large Language Models.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments RPTQ: Reorder-based Post-training Quantization for Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.163507Z

Source-reported events for the cited work

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Observation fa6a0ec5-f35c-433f-9f9d-556e71043bd8 · outbound

This paper cites Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.167424Z

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Observation 93aca12d-60e6-4136-9d0c-9496ee375057 · outbound

This paper cites Mixed Precision Training.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Mixed Precision Training

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.171419Z

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Observation d0727b2a-f5e2-4b55-b92d-0578d713031b · outbound

This paper cites Layered mixed-precision training: A new training method for large-scale ai models.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Layered mixed-precision training: A new training method for large-scale ai models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.175466Z

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Observation d686842b-2e37-4842-82ba-f54a96f683fc · outbound

This paper cites Channel-Wise Mixed-Precision Quantization for Large Language Models.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Channel-Wise Mixed-Precision Quantization for Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.179221Z

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Observation 38cf8457-1374-4fb7-8a4b-9efe2af78aec · outbound

This paper cites Mixed precision low-bit quantization of neural network language models for speech recognition.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Mixed precision low-bit quantization of neural network language models for speech recognition

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.183118Z

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Observation 747d88b1-aaa9-4680-bfe6-d403761b12af · outbound

This paper cites Efficient deep learning: A survey on making deep learning models smaller, faster, and better.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Efficient deep learning: A survey on making deep learning models smaller, faster, and better

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.186941Z

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Observation 16b502ab-f930-4661-8d07-dfe224e487f0 · outbound

This paper cites Model optimization - google ai.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Model optimization - google ai

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.190754Z

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Observation 3cffbc8a-a4b6-4ab5-85e2-2b093843da26 · outbound

This paper cites A Comprehensive Study on Quantization Techniques for Large Language Models.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments A Comprehensive Study on Quantization Techniques for Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.194253Z

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Observation a61b0b69-a24a-4fff-91e9-d9ce53f1efe0 · outbound

This paper cites The Loss Surfaces of Multilayer Networks.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments The Loss Surfaces of Multilayer Networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.198416Z

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Observation 5c57f2bc-fad7-4afc-a5de-965e9f6fb765 · outbound

This paper cites A survey on deep neural network pruning-taxonomy, comparison.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments A survey on deep neural network pruning-taxonomy, comparison

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.204876Z

Source-reported events for the cited work

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Observation 443aa724-f24e-4470-80d9-5491790c87a9 · outbound

This paper cites SNIP: Single-shot Network Pruning based on Connection Sensitivity.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.209487Z

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Observation beebf16b-87b8-4200-a0c7-603fd51e5c8a · outbound

This paper cites Picking Winning Tickets Before Training by Preserving Gradient Flow.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.213775Z

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Observation 5b15a04d-e505-442b-b71e-680e2c8fbe4a · outbound

This paper cites SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.218245Z

Source-reported events for the cited work

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Observation f70e91db-3bbc-4b75-a990-e665cb576c18 · outbound

This paper cites Channel Pruning for Accelerating Very Deep Neural Networks.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Channel Pruning for Accelerating Very Deep Neural Networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.222315Z

Source-reported events for the cited work

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Observation 191d9205-9600-4bde-b6a1-396456cfffe1 · outbound

This paper cites Gate Decorator : Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Gate Decorator : Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.226147Z

Source-reported events for the cited work

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Observation 56d90dca-8913-41bf-9116-2118e204c944 · outbound

This paper cites Layer-wise pruning of transformer attention heads for efficient language modeling.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Layer-wise pruning of transformer attention heads for efficient language modeling

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.229807Z

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Observation feba9842-ca0a-4c1d-a2ef-4d1780cd6721 · outbound

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

On Accelerating Edge AI: Optimizing Resource-Constrained Environments SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.233502Z

Source-reported events for the cited work

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Observation 6369a1ce-10bc-455d-94f1-a1f8788fa130 · outbound

This paper cites ShortGPT: Layers in Large Language Models are More Redundant Than You Expect.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.237471Z

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Observation b1946240-4b6c-431b-82a3-0dbe2c06793c · outbound

This paper cites LLM-Pruner: On the Structural Pruning of Large Language Models.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments LLM-Pruner: On the Structural Pruning of Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.241369Z

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Observation c0f32fd0-4f4a-40ab-b7bb-cdce41df1f39 · outbound

This paper cites LPViT: Low-Power Semi-structured Pruning for Vision Transformers.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments LPViT: Low-Power Semi-structured Pruning for Vision Transformers

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.245641Z

Source-reported events for the cited work

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Observation cf6a1cf8-c980-480e-820e-53e5852adbd1 · outbound

This paper cites An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.249668Z

Source-reported events for the cited work

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Observation d5efe6fe-0e48-4f77-a7e6-cfdb0d3b0dda · outbound

This paper cites Pruning Filter in Filter.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Pruning Filter in Filter

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.254153Z

Source-reported events for the cited work

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Observation 523a67ce-6df9-47b0-97b8-b3158409dce0 · outbound

This paper cites Pruning neural networks without any data by iteratively conserving synaptic flow.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.258240Z

Source-reported events for the cited work

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Observation a782c686-6983-488e-9877-effc0e2409f3 · outbound

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

On Accelerating Edge AI: Optimizing Resource-Constrained Environments A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.262450Z

Source-reported events for the cited work

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Observation f6c03526-9886-4a35-bb5e-1db5eca44b56 · outbound

This paper cites Rigging the Lottery: Making All Tickets Winners.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Rigging the Lottery: Making All Tickets Winners

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.265887Z

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Observation ddbc4549-3f8b-4b3b-b23e-74e8adbd0388 · outbound

This paper cites Learning Structured Sparsity in Deep Neural Networks.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Learning Structured Sparsity in Deep Neural Networks

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.269353Z

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Observation 3473d1cf-5371-47de-a326-918a24857c7f · outbound

This paper cites Learning Efficient Convolutional Networks through Network Slimming.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Learning Efficient Convolutional Networks through Network Slimming

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.272982Z

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Observation 4e3e87d2-475f-47a7-a184-c7e2a4e9ea2c · outbound

This paper cites DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.275991Z

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Observation f40a7400-6074-49b0-940d-2655e9359a8a · outbound

This paper cites APQ: Joint Search for Network Architecture, Pruning and Quantization Policy.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments APQ: Joint Search for Network Architecture, Pruning and Quantization Policy

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.279869Z

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Observation b29ab30e-1ef3-43e1-8f81-b5f9563a56de · outbound

This paper cites A Fast Post-Training Pruning Framework for Transformers.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments A Fast Post-Training Pruning Framework for Transformers

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.283999Z

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Observation 123729c9-0cec-4f2b-8e62-afa6312fd0be · outbound

This paper cites Group Fisher Pruning for Practical Network Compression.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Group Fisher Pruning for Practical Network Compression

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.287873Z

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Observation fcb84075-9dc4-4cae-849b-e9fb4e8cef5f · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.291799Z

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Observation 8a97f40b-3c3c-4a2f-a286-338fd637e44a · outbound

This paper cites Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.295741Z

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Observation 1b2e5457-8591-4209-8b31-cbd0ef804789 · outbound

This paper cites EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.299488Z

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Observation 666369c5-bec5-42fb-884e-591232599155 · outbound

This paper cites Runtime network routing for efficient image classification.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Runtime network routing for efficient image classification

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.303261Z

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Observation ab5d072c-cd1e-4ba4-9e4e-f5bc6fcc5e49 · outbound

This paper cites Manifold Regularized Dynamic Network Pruning.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Manifold Regularized Dynamic Network Pruning

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.306767Z

Source-reported events for the cited work

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Observation b982a158-7a00-47fd-9057-7633e7c82ce2 · outbound

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

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.310472Z

Source-reported events for the cited work

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Observation 0bf525a5-15d4-4c88-a901-c28f5c146044 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Pruning Filters for Efficient ConvNets

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.314470Z

Source-reported events for the cited work

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Observation 60596ee3-31bd-4cbb-9b4c-81586e0ed9f4 · outbound

This paper cites Variational convolutional neural network pruning.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Variational convolutional neural network pruning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.318412Z

Source-reported events for the cited work

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Observation 44967318-9bc6-4813-a3a1-b68448dcf17e · outbound

This paper cites What Matters In The Structured Pruning of Generative Language Models?.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments What Matters In The Structured Pruning of Generative Language Models?

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.321953Z

Source-reported events for the cited work

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Observation d50e0991-34e6-4626-9637-d8a62f853256 · outbound

This paper cites Everybody prune now: Structured pruning of llms with only forward passes, 2024.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Everybody prune now: Structured pruning of llms with only forward passes, 2024

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.325740Z

Source-reported events for the cited work

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Observation 5f6880e4-f459-4038-9d12-f66c0d144854 · outbound

This paper cites Graph Pruning for Model Compression.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Graph Pruning for Model Compression

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:46:40.088510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c59b3724-9f2b-455f-aa4b-b0351463d2f8 · outbound

This paper cites AMC: AutoML for Model Compression and Acceleration on Mobile Devices, page 815–832.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments AMC: AutoML for Model Compression and Acceleration on Mobile Devices, page 815–832

Reference 51

Resolution: verified exact; doi, observed 2026-08-10T14:46:38.766538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d5dc9d93-59ec-4dd8-b381-500a3d4970e0 · outbound

This paper cites Efficient neural network pruning using model-based reinforcement learning.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Efficient neural network pruning using model-based reinforcement learning

Reference 52

Resolution: verified exact; raw_fallback, observed 2026-08-10T14:46:40.072065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f857d809-885a-4881-99e2-ceb1b3f182d6 · outbound

This paper cites Model compression for deep neural networks: A survey.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Model compression for deep neural networks: A survey

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.341038Z

Source-reported events for the cited work

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Observation 9814f67d-a2b4-4741-aa7a-c1ddf0e7596c · outbound

This paper cites Compressing pre-trained language models using progressive low rank decomposition.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Compressing pre-trained language models using progressive low rank decomposition

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.345209Z

Source-reported events for the cited work

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Observation 359ac8eb-a30c-4602-91a7-c8aad07a4551 · outbound

This paper cites Compressing Large Language Models using Low Rank and Low Precision Decomposition.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Compressing Large Language Models using Low Rank and Low Precision Decomposition

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.348825Z

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Observation e1e9f2b9-9de5-4277-9b27-9b68444496f1 · outbound

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

On Accelerating Edge AI: Optimizing Resource-Constrained Environments TensorGPT: Efficient Compression of Large Language Models based on Tensor-Train Decomposition

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.352564Z

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Observation 2c2a43da-05c0-49d0-9840-6848390f7f15 · outbound

This paper cites MoDeGPT: Modular Decomposition for Large Language Model Compression.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments MoDeGPT: Modular Decomposition for Large Language Model Compression

Reference 57

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Observation 4b7f9528-3948-4a0e-bc7e-4ff332147daf · outbound

This paper cites Distilling the Knowledge in a Neural Network.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Distilling the Knowledge in a Neural Network

Reference 58

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Observation 27f9a6c7-33a0-40ee-895d-45cf6922df1b · outbound

This paper cites A Survey on Knowledge Distillation of Large Language Models.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments A Survey on Knowledge Distillation of Large Language Models

Reference 59

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Observation 4ef193f9-044f-4a89-9ab1-f7f28844b3fe · outbound

This paper cites Adversarially robust distillation.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Adversarially robust distillation

Reference 60

Resolution: verified exact; doi, observed 2026-08-10T14:46:38.747255Z

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Observation 44499107-cc33-48ee-bf55-2e577ee9a03f · outbound

This paper cites Generative Adversarial Networks.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Generative Adversarial Networks

Reference 61

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Observation e445bd47-b258-42b2-9818-f6c67062570f · outbound

This paper cites PeerAiD: Improving Adversarial Distillation from a Specialized Peer Tutor.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments PeerAiD: Improving Adversarial Distillation from a Specialized Peer Tutor

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:46:39.919091Z

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Observation 459bca02-04cd-4aa3-87cf-b0e1017ecbe0 · outbound

This paper cites Data-Free Learning of Student Networks.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Data-Free Learning of Student Networks

Reference 63

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Observation 7edb43d7-645b-4710-abbb-b4e6da56ec95 · outbound

This paper cites Deep Mutual Learning.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Deep Mutual Learning

Reference 64

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Observation 0ddb3cc6-b87d-4d9a-b63e-a83ccc9850aa · outbound

This paper cites Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks

Reference 65

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T14:46:39.879307Z

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Observation fdec11e2-cd7f-45e0-b3bb-c5a8c1b5c872 · outbound

This paper cites Cross Modal Distillation for Supervision Transfer.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Cross Modal Distillation for Supervision Transfer

Reference 66

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Observation 90f05cdc-01fc-411a-ad3f-d7435de089b6 · outbound

This paper cites Robust cross-modal representation learning with progressive self-distillation.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Robust cross-modal representation learning with progressive self-distillation

Reference 67

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Observation baf55b35-1b96-4541-b7bb-21d4a69f3430 · outbound

This paper cites C2kd: Bridging the modality gap for cross-modal knowledge distillation.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments C2kd: Bridging the modality gap for cross-modal knowledge distillation

Reference 68

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Observation a2ee16a7-393b-4e57-a53c-1b97400db641 · outbound

This paper cites Graph-based Knowledge Distillation: A survey and experimental evaluation.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Graph-based Knowledge Distillation: A survey and experimental evaluation

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:46:39.852850Z

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Observation 67052e37-d4c0-4de0-8edd-56074ebe3ac4 · outbound

This paper cites Attention Is All You Need.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Attention Is All You Need

Reference 70

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Observation 0259a69a-7921-403f-9107-a477e2760329 · outbound

This paper cites Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers

Reference 71

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Observation 4719ef4e-edea-42c6-aa68-ebe7e77fe262 · outbound

This paper cites Attention Distillation: self-supervised vision transformer students need more guidance.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Attention Distillation: self-supervised vision transformer students need more guidance

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:46:39.825681Z

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Observation edca2eac-0d0d-4ead-a2d5-1cbaf0cd0ad2 · outbound

This paper cites Moonshine: Distilling with Cheap Convolutions.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Moonshine: Distilling with Cheap Convolutions

Reference 73

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T14:46:39.808016Z

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Observation 2731f557-8dec-497a-85ed-2369f8468f34 · outbound

This paper cites Maybank, and Dacheng Tao.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Maybank, and Dacheng Tao

Reference 74

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Observation 468819d4-6d7a-4e78-8d07-6b45b49b9334 · outbound

This paper cites Online Knowledge Distillation with Diverse Peers.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Online Knowledge Distillation with Diverse Peers

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:46:39.791986Z

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Observation 9f5b5f6f-92e9-4fa2-aa68-a8d0bcbecc46 · outbound

This paper cites Self-distillation amplifies regularization in hilbert space.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Self-distillation amplifies regularization in hilbert space

Reference 76

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Observation 1fc37460-86d9-485c-86c8-f22229ca148e · outbound

This paper cites Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:46:39.775868Z

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Observation 56b94f70-741d-46fc-8897-63e05400c2d7 · outbound

This paper cites Self-instruct: Aligning language model with self generated instructions, 12 2022 b.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Self-instruct: Aligning language model with self generated instructions, 12 2022 b

Reference 78

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Observation 0b569ab5-cd21-4632-86da-c53ff8297f26 · outbound

This paper cites Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning

Reference 79

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Observation 3b3b0f38-4e87-4436-b127-e8e02820b26d · outbound

This paper cites Orca: Progressive Learning from Complex Explanation Traces of GPT-4.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Orca: Progressive Learning from Complex Explanation Traces of GPT-4

Reference 80

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Observation e071c7a6-b453-4912-94f9-2523c12c413f · outbound

This paper cites Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data

Reference 81

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Observation 4be93db7-09a0-42f6-abb2-9e433ee160e5 · outbound

This paper cites Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks

Reference 82

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Observation d39edbd0-f453-4e0f-a07c-df529381f9ca · outbound

This paper cites Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Reference 83

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Observation da7ae879-0506-49c9-95a3-b82ed00f337f · outbound

This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 84

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Observation 4549be5c-e0bc-409e-a853-09b99bba609b · outbound

This paper cites Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Reference 85

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Observation 75778f95-59f2-482c-8fbc-39936c186e85 · outbound

This paper cites Zephyr: Direct Distillation of LM Alignment.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Zephyr: Direct Distillation of LM Alignment

Reference 86

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Observation 4b133633-013b-43e0-b7d7-8cf1b70e1238 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Constitutional AI: Harmlessness from AI Feedback

Reference 87

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Observation 1854f0dc-5f92-49e9-9c87-a49b01d31b45 · outbound

This paper cites Sub-goal Distillation: A Method to Improve Small Language Agents.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Sub-goal Distillation: A Method to Improve Small Language Agents

Reference 88

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Observation 90a12a1b-eacf-4eff-aac0-28087351075c · outbound

This paper cites Agent Lumos: Unified and Modular Training for Open-Source Language Agents.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Agent Lumos: Unified and Modular Training for Open-Source Language Agents

Reference 89

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Observation 8e0aa012-322f-4fc4-8919-a50c581c0df7 · outbound

This paper cites FireAct: Toward Language Agent Fine-tuning.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments FireAct: Toward Language Agent Fine-tuning

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.488034Z

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Observation 3ef2dfbb-3358-4533-94b7-bb6e975613d8 · outbound

This paper cites Gorilla: Large Language Model Connected with Massive APIs.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Gorilla: Large Language Model Connected with Massive APIs

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.492092Z

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Observation 12d2a092-d5a9-4e02-adc9-19c8f1e84559 · outbound

This paper cites MiniLLM: On-Policy Distillation of Large Language Models.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments MiniLLM: On-Policy Distillation of Large Language Models

Reference 92

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Observation 994244e9-e0b4-4f92-95d3-d3765efae91b · outbound

This paper cites DistiLLM: Towards Streamlined Distillation for Large Language Models.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments DistiLLM: Towards Streamlined Distillation for Large Language Models

Reference 93

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Observation 2bc579fd-2346-4d6b-a940-b99e3e99e227 · outbound

This paper cites Impossible distillation: from low-quality model to high-quality dataset and model for summarization and paraphrasing, 05 2023.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Impossible distillation: from low-quality model to high-quality dataset and model for summarization and paraphrasing, 05 2023

Reference 94

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Observation d2baf331-6968-4f1c-853f-d26a388be083 · outbound

This paper cites QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation

Reference 95

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Observation 8160cbd4-2d44-470a-9275-a6ca9f1fe555 · outbound

This paper cites Visual Instruction Tuning.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Visual Instruction Tuning

Reference 96

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Observation d30c24d8-bc1b-46e8-9270-fae6bbfed8e2 · outbound

This paper cites Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration

Reference 97

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Observation 96753af1-11ac-4e2e-8ace-5ea10de95cc5 · outbound

This paper cites Junk DNA hypothesis: Pruning small pre-trained weights \ textit\ Irreversibly\ \ and \ textit\ Monotonically\ \ impairs ``difficult'' downstream tasks in LLM s.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Junk DNA hypothesis: Pruning small pre-trained weights \ textit\ Irreversibly\ \ and \ textit\ Monotonically\ \ impairs ``difficult'' downstream tasks in LLM s

Reference 98

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Observation 62cc5dd7-ad19-46af-9724-1d3ace7c1b88 · outbound

This paper cites AutoML : A survey of the state-of-the-art.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments AutoML : A survey of the state-of-the-art

Reference 99

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Observation de3c73e5-2d83-4974-9747-4e2df88c8771 · outbound

This paper cites Neural Architecture Search with Reinforcement Learning.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Neural Architecture Search with Reinforcement Learning

Reference 100

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Pith citing papers

Observation 1e3af81f-46f6-4f84-bf14-2b32bb6ec881 · inbound

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Token Compression Meets Compact Vision Transformers: A Survey and Comparative Evaluation for Edge AI On Accelerating Edge AI: Optimizing Resource-Constrained Environments

Reference 26

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Observation b7b67d51-e4b1-43fe-9383-5a493b71e479 · inbound

Performance Isolation for Inference Processes in Edge GPU Systems cites this paper.

Performance Isolation for Inference Processes in Edge GPU Systems On Accelerating Edge AI: Optimizing Resource-Constrained Environments

Reference 14

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Observation 35e2445b-1cc9-4d7d-9574-c9fe4e169eb8 · inbound

Joint Architecture-Token-Bitwidth Multi-Axis Optimization of Vision Transformers for Semiconductor IC Packaging cites this paper.

Joint Architecture-Token-Bitwidth Multi-Axis Optimization of Vision Transformers for Semiconductor IC Packaging On Accelerating Edge AI: Optimizing Resource-Constrained Environments

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:45:21.041683Z

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