Paper Citation Record · LEDGER

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression

As of 20 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2505.18166.

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

pith.paper-citation-record.v1
2505.18166 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:50:41.023247Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

27 of 27 outbound references displayed

verified exact0
verified fuzzy14
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation af31ce55-65e2-443c-ae76-4c9ade11c37d · outbound

This paper cites Large-scale artificial intelligence models.Computer, 55(5):76–80, May 2022.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Large-scale artificial intelligence models.Computer, 55(5):76–80, May 2022

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:50:41.469394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b01d0492-c744-44c8-aab2-edf20b03c3ce · outbound

This paper cites Neural Architecture Search: Insights from 1000 Papers.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Neural Architecture Search: Insights from 1000 Papers

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T21:50:40.886372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90c44106-39dd-4cae-a827-a39f1dc63d89 · outbound

This paper cites Edgeshard: Efficient llm inference via collaborative edge computing.IEEE Internet of Things Journal, pages 1–1, 2024.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Edgeshard: Efficient llm inference via collaborative edge computing.IEEE Internet of Things Journal, pages 1–1, 2024

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:50:41.457392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8d2b6581-e70a-405c-8eea-6e60f48ad64f · outbound

This paper cites On Accelerating Edge AI: Optimizing Resource-Constrained Environments.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression On Accelerating Edge AI: Optimizing Resource-Constrained Environments

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T21:50:40.897187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e93ba845-7a1a-4aae-9648-def7bb4a0851 · outbound

This paper cites Edge AI: A survey.Internet of Things and Cyber-Physical Systems, 2023.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Edge AI: A survey.Internet of Things and Cyber-Physical Systems, 2023

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:50:41.442597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a0d5d358-f6b3-416c-822a-07fb51a544c2 · outbound

This paper cites Distilling the knowledge in a neural network, 2015.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Distilling the knowledge in a neural network, 2015

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T21:50:40.907709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82fd839f-e266-45c0-93ed-1e85306256d5 · outbound

This paper cites EPSD: Early pruning with self-distillation for efficient model compression, 2024.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression EPSD: Early pruning with self-distillation for efficient model compression, 2024

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:50:41.401073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e74b4020-8d93-45c2-bf9d-9a66a429bfc2 · outbound

This paper cites Springer International Publishing, 2023.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Springer International Publishing, 2023

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:50:41.385925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a1a2bd59-576e-4b10-97b0-8a77fd9fd8e0 · outbound

This paper cites Self-data distillation for recovering quality in pruned large language models, 2024.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Self-data distillation for recovering quality in pruned large language models, 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:50:41.372917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eb31a221-690e-44ba-80ad-41ae27beac54 · outbound

This paper cites Llm Pruning and Distillation in Practice: The Minitron Approach.arXiv.org, 2024.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Llm Pruning and Distillation in Practice: The Minitron Approach.arXiv.org, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:50:41.357881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5dd70124-6fa6-4c68-b42c-081b876fa145 · outbound

This paper cites Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks.IEEE International Conference on Decision and Control (CDC), 2018.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks.IEEE International Conference on Decision and Control (CDC), 2018

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:50:41.343025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e76b1024-06ab-4521-a256-8d35f24225eb · outbound

This paper cites Ugur Guney, Yann Dauphin, and Leon Bottou.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Ugur Guney, Yann Dauphin, and Leon Bottou

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:50:41.323267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 917df378-6303-4e66-8774-7db4f4592ba4 · outbound

This paper cites Revisiting self-distillation, 2022.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Revisiting self-distillation, 2022

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T21:50:40.948953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d0cd805-89b0-4eda-80f5-b2872f819299 · outbound

This paper cites Random teachers are good teachers.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Random teachers are good teachers

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:50:41.282302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9fdb6086-16da-4701-83e0-1e4b778988de · outbound

This paper cites Comparing kullback-leibler divergence and mean squared error loss in knowledge distillation.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Comparing kullback-leibler divergence and mean squared error loss in knowledge distillation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:50:41.257362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bcd4a412-3a5d-4c6a-94e8-6b679d90d6f1 · outbound

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

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Self-distillation amplifies regularization in hilbert space

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T21:50:40.962957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70865389-668d-43d8-ac38-8feea5345df5 · outbound

This paper cites Optimal brain damage.Advances in neural information processing systems, 2, 1989.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Optimal brain damage.Advances in neural information processing systems, 2, 1989

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T21:50:40.967618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddb9e323-27b1-4165-9e79-53f46ef09d02 · outbound

This paper cites Gomez, Łukasz Kaiser, and Illia Polosukhin.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Gomez, Łukasz Kaiser, and Illia Polosukhin

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:50:41.217885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 10b69f36-6d4e-435f-9f38-f01ba78bbf92 · outbound

This paper cites Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T21:50:40.975500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af58ced1-7aa3-4c0c-accd-247287b2cbec · outbound

This paper cites GPT-4 Technical Report.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression GPT-4 Technical Report

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T21:50:40.980875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af5f7739-5968-4166-8e44-1e5f6790566c · outbound

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

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression LLaMA: Open and Efficient Foundation Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T21:50:40.984990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 827f0b82-ae47-4829-887d-17b4c810c1c4 · outbound

This paper cites an unresolved cited work.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Unresolved cited work

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T21:50:40.989135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39c2ffe3-554f-4ddb-9272-64d294fea444 · outbound

This paper cites CommonsenseQA: A question answering challenge targeting commonsense knowledge.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression CommonsenseQA: A question answering challenge targeting commonsense knowledge

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T21:50:40.993090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a5aed0d-4a02-4845-8b54-8cce62b05d78 · outbound

This paper cites Language Models are Few-Shot Learners.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression Language Models are Few-Shot Learners

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T21:50:40.998467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8dd1354b-bb01-47d3-8daf-30f5b0f610c2 · outbound

This paper cites ptflops: a flops counting tool for neural networks in pytorch framework, 2024.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression ptflops: a flops counting tool for neural networks in pytorch framework, 2024

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:50:41.181569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b42d6c7f-ef15-4667-8160-2b9e57a8bb29 · outbound

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

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T21:50:41.013629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8539ca2-cc99-46c4-820b-64b19e1d00ca · outbound

This paper cites A survey on uncertainty quantification methods for deep learning, 2025.

Constrained Edge AI Deployment: Fine-Tuning vs Distillation for LLM Compression A survey on uncertainty quantification methods for deep learning, 2025

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:50:41.168587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.