Paper Citation Record · LEDGER

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques

As of 18 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 3 inbound Pith citation observations for arXiv:2505.02309.

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

pith.paper-citation-record.v1
2505.02309 v2

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T01:00:02.155950Z

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:07:48.814065Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T10:38:35.586521Z

Reference resolution

62 of 62 outbound references displayed

verified exact0
verified fuzzy21
unresolved41
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bb503ec8-ccfc-44b7-88bb-dece89451bae · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Gemini: A Family of Highly Capable Multimodal Models

Reference 1

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Observation f7f15967-a0d5-4bf8-861b-ee2de2c66f4f · outbound

This paper cites Language models are few -shot learners.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Language models are few -shot learners

Reference 2

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Observation 7175a426-9346-4a7c-a739-6bfe3b9c4700 · outbound

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

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques LLaMA: Open and Efficient Foundation Language Models

Reference 3

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Observation aaaba901-5fb6-4b28-b466-a99c890c91b0 · outbound

This paper cites The Llama 3 Herd of Models.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques The Llama 3 Herd of Models

Reference 4

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Observation ec3a304c-fd6d-4af3-b215-c0d2c7e7f4f7 · outbound

This paper cites Hierarchical Neural Story Generation.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Hierarchical Neural Story Generation

Reference 5

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Observation 91d5e6d9-ba58-4e6e-9c51-7ca1b51981d9 · outbound

This paper cites LaMDA: Language Models for Dialog Applications.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques LaMDA: Language Models for Dialog Applications

Reference 6

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Observation 1c84929a-4841-4d12-a28e-a500ddd3cb27 · outbound

This paper cites A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage,

Reference 7

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Observation dee029e6-9d60-4474-b269-f087bc3f8ef0 · outbound

This paper cites Highly accurate protein structure prediction with AlphaFold,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Highly accurate protein structure prediction with AlphaFold,

Reference 8

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Observation cf67736f-7047-4bd7-81f4-fe8540b18806 · outbound

This paper cites A Survey on Model Compression for Large Language Models,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques A Survey on Model Compression for Large Language Models,

Reference 9

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Observation 1b020adb-c068-4cc9-b8e6-b7cae46f8820 · outbound

This paper cites A Survey on Transformer Compression.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques A Survey on Transformer Compression

Reference 10

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Observation 42e96f55-67f9-4ae3-8d89-f8de583f8e81 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Distilling the Knowledge in a Neural Network

Reference 11

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Observation dd7f1576-0d1b-4586-9f29-e9e261438601 · outbound

This paper cites Model compression,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Model compression,

Reference 12

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Observation 844a8cb6-08e1-42d7-bef7-5d4af0dff5b7 · outbound

This paper cites Knowledge Distillation: A Survey,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Knowledge Distillation: A Survey,

Reference 13

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Observation fb98f24b-a989-41b7-895f-16aea4601ed2 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques OPT: Open Pre-trained Transformer Language Models

Reference 14

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Observation 69004620-d6e5-4019-a45b-f3d12edbf6a6 · outbound

This paper cites FitNets: Hints for Thin Deep Nets.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques FitNets: Hints for Thin Deep Nets

Reference 15

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Observation b8dd35fd-44d0-4394-8529-a3066d8cc569 · outbound

This paper cites Relational knowledge distillation.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Relational knowledge distillation

Reference 16

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Observation 5f266641-ebae-49e1-92a7-0b56e2499026 · outbound

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

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation,

Reference 17

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Observation d820ce6a-8b9c-4c8f-b744-ce432aa76d47 · outbound

This paper cites Born again neural networks.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Born again neural networks

Reference 18

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Observation be8b8f18-4f33-4c77-8fbf-e1a9f506f740 · outbound

This paper cites Learning from Multiple Teacher Networks,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Learning from Multiple Teacher Networks,

Reference 19

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Observation 589eb7fd-4b2d-4d42-81f6-0cb833e1c400 · outbound

This paper cites On-policy distillation of language models: Learning from self -generated mistakes,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques On-policy distillation of language models: Learning from self -generated mistakes,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:00:03.225804Z

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Observation db837453-4376-4b5e-b5c6-7eeddaef7ef1 · outbound

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

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques MiniLLM: On-Policy Distillation of Large Language Models

Reference 21

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Observation 97676631-4632-4726-90e7-37cafba6c385 · outbound

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

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques DistiLLM: Towards Streamlined Distillation for Large Language Models

Reference 22

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Observation a882c0f5-7d80-4939-9b53-5cf440cce337 · outbound

This paper cites GPT3.int8(): 8- bit Matrix Multiplication for Transformers at Scale,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques GPT3.int8(): 8- bit Matrix Multiplication for Transformers at Scale,

Reference 23

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Observation 7511ea32-2687-4ae0-95f6-2391e05805ab · outbound

This paper cites BitNet: Scaling 1-bit Transformers for Large Language Models.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques BitNet: Scaling 1-bit Transformers for Large Language Models

Reference 24

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Observation 95fd0ef2-6af6-435f-838c-5e78d964e3b6 · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 25

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This paper cites Towards Accurate Post-training Network Quantization via Bit-Split and Stitching,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Towards Accurate Post-training Network Quantization via Bit-Split and Stitching,

Reference 26

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Observation ef9811d0-8dac-4098-9a07-6df3f40d97a6 · outbound

This paper cites Up or down? Adaptive rounding for post -training quantization.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Up or down? Adaptive rounding for post -training quantization

Reference 27

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Observation 2d4b8286-09ee-43ba-ab8d-066667137d67 · outbound

This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Reference 28

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This paper cites Quantization and Training of Neural Networks for Efficient Integer -Arithmetic-Only Inference,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Quantization and Training of Neural Networks for Efficient Integer -Arithmetic-Only Inference,

Reference 29

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This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 30

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Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 31

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Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques A Survey of Quantization Methods for Efficient Neural Network Inference,

Reference 32

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This paper cites Understanding and Overcoming the Challenges of Efficient Transformer Quantization.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Understanding and Overcoming the Challenges of Efficient Transformer Quantization

Reference 33

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This paper cites ZeroQuant: Efficient and Affordable Post -Training Quantization for Large-Scale Transformers,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques ZeroQuant: Efficient and Affordable Post -Training Quantization for Large-Scale Transformers,

Reference 34

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Observation d1769770-8c9e-48d6-8058-7c9884264287 · outbound

This paper cites HAQ: Hardware -Aware Automated Quantization With Mixed Precision,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques HAQ: Hardware -Aware Automated Quantization With Mixed Precision,

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This paper cites Binaryconnect: Training deep neural networks with binary weights during propagations ,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Binaryconnect: Training deep neural networks with binary weights during propagations ,

Reference 36

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Observation 531a8252-53ac-47f7-8be5-437b7e5b2065 · outbound

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Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Ternary Weight Networks,

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Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Do Deep Nets Really Need to be Deep?,

Reference 38

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Observation c0f32e67-cf26-4a67-aba3-52e0ee3a428f · outbound

This paper cites Model compression via distillation and quantization.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Model compression via distillation and quantization

Reference 39

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Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques SmoothQuant: Accurate and Efficient Post -Training Quantization for Large Language Models,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:00:03.150589Z

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Observation f40fa136-bbbf-48b3-8d36-1fd779a4d639 · outbound

This paper cites ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 41

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Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration,

Reference 42

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

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 43

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Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Optimal brain damage. Advances

Reference 44

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Observation cee344da-f6c1-40a9-95b3-8633093b6992 · outbound

This paper cites Optimal Brain Surgeon and general network pruning,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Optimal Brain Surgeon and general network pruning,

Reference 45

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This paper cites Learning both weights and connections for efficient neural network.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Learning both weights and connections for efficient neural network

Reference 46

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Observation 28e4ee8a-e7b1-4d5f-8f6e-7acfce1a8090 · outbound

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Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Movement Pruning: Adaptive Sparsity by Fine-Tuning,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:00:03.120097Z

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Observation a4187a87-bc1b-4503-82d6-232d012c5410 · outbound

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

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques AMC: AutoML for Model Compression and Acceleration on Mobile Devices,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:00:03.109420Z

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Observation 961b9d44-5d51-4642-b26d-f117af87f2f4 · outbound

This paper cites Adaptive Mixtures of Local Experts,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Adaptive Mixtures of Local Experts,

Reference 49

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Observation 066102ca-6e2d-4342-bc76-13bf52190b76 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 50

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Observation 053c2ce6-a4de-4b95-840f-144a03ff4eb3 · outbound

This paper cites Glam: Efficient scaling of language models with mixture-of- experts.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Glam: Efficient scaling of language models with mixture-of- experts

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:00:03.098889Z

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Observation ac2d43f2-ff5c-4ffd-9796-5b7caf24d3ca · outbound

This paper cites BranchyNet: Fast inference via early exiting from deep neural networks,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques BranchyNet: Fast inference via early exiting from deep neural networks,

Reference 52

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This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks ,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Retrieval-augmented generation for knowledge-intensive nlp tasks ,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:00:03.088933Z

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Observation 9f338af2-e4b2-4e14-b2a1-10b564c56cba · outbound

This paper cites Structured Pruning of Deep Convolutional Neural Networks,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Structured Pruning of Deep Convolutional Neural Networks,

Reference 54

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Observation 5b0dcfe9-b2ef-4c16-b7f1-06a9e9a31012 · outbound

This paper cites Pruning and quantization for deep neural network acceleration: A survey,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Pruning and quantization for deep neural network acceleration: A survey,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T01:00:02.132271Z

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Observation 69e3d51b-4a89-4a73-8d86-82f96ff5d67b · outbound

This paper cites Large Language Models Are Reasoning Teachers.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Large Language Models Are Reasoning Teachers

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-16T01:00:02.135615Z

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Observation 753c26f6-492c-4594-bff5-86c8f087c8e0 · outbound

This paper cites Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T01:00:02.139093Z

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Observation 3d2840ab-81b5-4ee5-b465-e6317ac87890 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms,.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Qlora: Efficient finetuning of quantized llms,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:00:03.079105Z

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Observation ca54efa4-926e-401d-a1bd-5f7b22efbd9f · outbound

This paper cites LLaMA-NAS: Efficient Neural Architecture Search for Large Language Models.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques LLaMA-NAS: Efficient Neural Architecture Search for Large Language Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-16T01:00:02.145689Z

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Observation ba9da55d-147a-4e1f-963d-61488fe03ff5 · outbound

This paper cites Learning best combination for efficient n: M sparsity.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques Learning best combination for efficient n: M sparsity

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:00:03.069047Z

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Observation a9fae284-dcc2-482c-a0ad-278819833061 · outbound

This paper cites FP8 Formats for Deep Learning.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques FP8 Formats for Deep Learning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-16T01:00:02.152354Z

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Observation 1f918c21-784a-44b8-a88b-c1319ad3934a · outbound

This paper cites unsloth/DeepSeek-V3-0324-GGUF · Hugging Face.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques unsloth/DeepSeek-V3-0324-GGUF · Hugging Face

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:00:03.059404Z

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Pith citing papers

Observation 630aa57b-db79-4a07-bbe5-cd63dbbef4f4 · inbound

Opportunities and Applications of GenAI in Smart Cities: A User-Centric Survey cites this paper.

Opportunities and Applications of GenAI in Smart Cities: A User-Centric Survey Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T22:07:48.814065Z

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Observation f2a26418-c362-4ca8-96e0-504e52e46ff4 · inbound

From Construction to Injection: Edit-Based Fingerprints for Large Language Models cites this paper.

From Construction to Injection: Edit-Based Fingerprints for Large Language Models Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T11:12:42.870802Z

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Observation a27bfd1d-3670-4dee-a185-d1e1b21b5ce8 · inbound

Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation cites this paper.

Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-05T10:38:35.630644Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.