APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning | Paper Citation Record

Observation d7e5b989-dadb-465e-ac41-b1bd9abd23a1 · outbound

This paper cites Deep residual learning for image recognition.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Deep residual learning for image recognition

Reference 1

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Observation c886a4d1-3ea4-4256-8942-5f2beb2c6466 · outbound

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

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 2

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Observation 678ba923-88ec-4a57-b704-0297bf6c6d61 · outbound

This paper cites Identification of plant-parasitic nematode genera in turfgrass using deep learning algorithms.Scientific Reports, 2025.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Identification of plant-parasitic nematode genera in turfgrass using deep learning algorithms.Scientific Reports, 2025

Reference 3

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Observation 6fab0d16-5394-4130-9cfc-340e9e928e67 · outbound

This paper cites Deep learning in agriculture: A survey.Computers and electronics in agriculture, 147:70–90, 2018.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Deep learning in agriculture: A survey.Computers and electronics in agriculture, 147:70–90, 2018

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:01:00.194571Z

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Observation 862a9e00-699b-42cf-a790-f82a9a4c8319 · outbound

This paper cites A survey on deep learning in medical image analysis.Medical image analysis, 42:60–88, 2017.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning A survey on deep learning in medical image analysis.Medical image analysis, 42:60–88, 2017

Reference 5

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Observation 7980ec3b-b2f3-419a-8453-520259035704 · outbound

This paper cites White blood cell classification: Convolutional neural network (cnn) and vision transformer (vit) under medical microscope.Algorithms, 16(11):525, 2023.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning White blood cell classification: Convolutional neural network (cnn) and vision transformer (vit) under medical microscope.Algorithms, 16(11):525, 2023

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:01:00.168183Z

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Observation e167dae8-e8eb-465f-beb3-ecb3e56fc08a · outbound

This paper cites Model compression for deep neural networks: A survey.Computers, 12(3):60, 2023.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Model compression for deep neural networks: A survey.Computers, 12(3):60, 2023

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:01:00.150697Z

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Observation 09bf3f20-0814-4cfa-a9e5-58a0c5640f41 · outbound

This paper cites an unresolved cited work.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:01:00.134421Z

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Observation 1c251475-91cf-4dd5-905b-47617a88c3e9 · outbound

This paper cites Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.690928Z

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Observation 78baabd7-509a-43aa-ab60-a33d5911bfe3 · outbound

This paper cites Distilling the knowledge in a neural network.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Distilling the knowledge in a neural network

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:01:00.116395Z

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Observation a86e0e8e-8188-46bd-aae3-4585fadb65db · outbound

This paper cites Learning both weights and connections for efficient neural network.Advances in neural information processing systems, 28, 2015.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Learning both weights and connections for efficient neural network.Advances in neural information processing systems, 28, 2015

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.701248Z

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Observation fdf2759a-d0fc-40eb-b3e1-e63c57f2e134 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Pruning Filters for Efficient ConvNets

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.706092Z

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Observation d536ec57-34bd-4abd-af37-c1f0253dc340 · outbound

This paper cites an unresolved cited work.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Unresolved cited work

Reference 13

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Observation 06aa0682-0bf5-4477-a062-ddd1c794cc2c · outbound

This paper cites Depgraph: Towards any structural pruning.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Depgraph: Towards any structural pruning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:01:00.078465Z

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Observation c8a9315d-a7ad-481d-b372-470c705d1802 · outbound

This paper cites Mahoney, and Kurt Keutzer.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Mahoney, and Kurt Keutzer

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:01:00.060833Z

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Observation 627cd54d-02ed-4d95-8440-b8f82324aac5 · outbound

This paper cites Automatic joint structured pruning and quantization for efficient neural network training and compression.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Automatic joint structured pruning and quantization for efficient neural network training and compression

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:01:00.041882Z

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Observation ca1ff3f8-a5f9-4da6-b1d2-0934bca79b2c · outbound

This paper cites Profiling the real world potential of neural network compression.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Profiling the real world potential of neural network compression

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:01:00.018790Z

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Observation 4eb61984-a988-4400-8412-7d98f12b6885 · outbound

This paper cites dpro: A generic performance diagnosis and optimization toolkit for expediting distributed dnn training.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning dpro: A generic performance diagnosis and optimization toolkit for expediting distributed dnn training

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.999731Z

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Observation 3f68c55d-e2b1-406e-9cb2-b0eb25cacd73 · outbound

This paper cites A comprehensive review of network pruning based on pruning granularity and pruning time perspectives.Neurocomputing, 626:129382, 2025.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning A comprehensive review of network pruning based on pruning granularity and pruning time perspectives.Neurocomputing, 626:129382, 2025

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.982320Z

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Observation 170168ae-0ec7-4f35-8a77-7e63f5ae8ca2 · outbound

This paper cites Edge intelligence: A review of deep neural network inference in resource-limited environments.Electronics, 14(12):2495, 2025.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Edge intelligence: A review of deep neural network inference in resource-limited environments.Electronics, 14(12):2495, 2025

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.966857Z

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Observation 67cb452d-5bab-47cb-80a4-9fe10674170d · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.750536Z

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Observation 639405ba-60e8-41ee-924a-3ca476ac070c · outbound

This paper cites Mitigating carbon footprint for knowledge distillation based deep learning model compression.Plos one, 18(5):e0285668, 2023.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Mitigating carbon footprint for knowledge distillation based deep learning model compression.Plos one, 18(5):e0285668, 2023

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.939684Z

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Observation c5618204-e8a4-4e7f-a7ce-a0da1cc8c719 · outbound

This paper cites Efficient and controllable model compression through sequential knowledge distillation and pruning.Big Data and Cognitive Computing, 7(3):154, 2023.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Efficient and controllable model compression through sequential knowledge distillation and pruning.Big Data and Cognitive Computing, 7(3):154, 2023

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.923471Z

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Observation 7865d69d-2954-4706-a6a4-138ef41ace75 · outbound

This paper cites Measuring and improving the energy efficiency of large language models inference.IEEE Access, 12:80194–80207, 2024.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Measuring and improving the energy efficiency of large language models inference.IEEE Access, 12:80194–80207, 2024

Reference 24

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Observation 717a01c7-d151-4bcd-9db8-02031b9441dc · outbound

This paper cites Parameter-efficient fine-tuning methods for pretrained language models: A critical review and assessment.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Parameter-efficient fine-tuning methods for pretrained language models: A critical review and assessment.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.770020Z

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Observation f7be31ff-5abe-4836-9808-f1fc97b37455 · outbound

This paper cites Xprof: An open, scalable, and extensible profiling system for the modern ml stack.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Xprof: An open, scalable, and extensible profiling system for the modern ml stack

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.886804Z

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Observation 692fa5e2-89bb-4222-98c0-c616ef4277e0 · outbound

This paper cites Xsp: Across-stack profiling and analysis of machine learning models on gpus.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Xsp: Across-stack profiling and analysis of machine learning models on gpus

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.870982Z

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Observation d3fa11c8-b6b7-4389-9675-fd48f5c44820 · outbound

This paper cites Floating point operations in matrix-vector calculus.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Floating point operations in matrix-vector calculus

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.854874Z

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Observation a731a710-9459-43f2-9a40-dab99ee81e3c · outbound

This paper cites Pytorch profiler.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Pytorch profiler

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.839511Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 25e4c17f-f447-4e5f-b9d7-84c5ac02b2ef · outbound

This paper cites Tensorflow profiler guide.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Tensorflow profiler guide

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.824656Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ce7a46e7-ac80-49d1-9472-5599f19b9130 · outbound

This paper cites CUDA Profiler User’s Guide.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning CUDA Profiler User’s Guide

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.809313Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 35ef16c1-8be1-4e53-aefa-f272f8c908f2 · outbound

This paper cites Cuda profiling tools interface (cupti) documentation.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Cuda profiling tools interface (cupti) documentation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.792825Z

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Observation e91e5ecd-7c78-4d46-9312-820974265ebd · outbound

This paper cites ptflops: a flops counting tool for neural networks in pytorch framework, 2018-2024.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning ptflops: a flops counting tool for neural networks in pytorch framework, 2018-2024

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.777947Z

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Observation 794d63e4-1942-4326-80fe-6df000fb2c52 · outbound

This paper cites Structured pruning for deep convolutional neural networks: A survey.IEEE transactions on pattern analysis and machine intelligence, 46(5):2900–2919, 2023.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Structured pruning for deep convolutional neural networks: A survey.IEEE transactions on pattern analysis and machine intelligence, 46(5):2900–2919, 2023

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.762078Z

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Observation f489d594-e777-4006-a09a-884218fde37f · outbound

This paper cites Post-training quantization or quantization-aware training? that is the question.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Post-training quantization or quantization-aware training? that is the question

Reference 35

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Observation d3ed1560-6eed-47a0-b20a-bf7f52fe8320 · outbound

This paper cites Pd-quant: Post-training quantization based on prediction difference metric.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Pd-quant: Post-training quantization based on prediction difference metric

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.727047Z

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Observation 4198a18d-6ee3-4740-a692-8db14be2df1b · outbound

This paper cites Efficientqat: Efficient quantization-aware training for large language models.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Efficientqat: Efficient quantization-aware training for large language models

Reference 37

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Observation b10a39b5-722c-433f-a0c8-b031381073d6 · outbound

This paper cites Ternary Weight Networks.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Ternary Weight Networks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.833004Z

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Observation 552cc453-78b4-483b-98b9-cd62bdae8f0c · outbound

This paper cites Learning Discrete Weights Using the Local Reparameterization Trick.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Learning Discrete Weights Using the Local Reparameterization Trick

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-08T12:00:59.314491Z

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Observation 2b6919a7-8b5b-468b-9903-755faf33dbb6 · outbound

This paper cites Relaxed Quantization for Discretized Neural Networks.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Relaxed Quantization for Discretized Neural Networks

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.843112Z

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Observation 7d6d94c7-b24b-4ff5-8902-0908deeff82e · outbound

This paper cites Training and Inference with Integers in Deep Neural Networks.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Training and Inference with Integers in Deep Neural Networks

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.848227Z

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Observation 3817aef2-eb59-4834-bc59-b033c4cbb895 · outbound

This paper cites Mixed Precision DNNs: All you need is a good parametrization.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Mixed Precision DNNs: All you need is a good parametrization

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.853311Z

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Observation ac1e2e82-87ff-4494-a537-4473b1c6f8bb · outbound

This paper cites Differentiable joint pruning and quantization for hardware efficiency.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Differentiable joint pruning and quantization for hardware efficiency

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.699758Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 89f34d76-44ff-4af6-9481-346be170519f · outbound

This paper cites Bayesian bits: Unifying quantization and pruning.Advances in neural information processing systems, 33:5741–5752, 2020.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Bayesian bits: Unifying quantization and pruning.Advances in neural information processing systems, 33:5741–5752, 2020

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.682353Z

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Observation 337bbef7-6eb0-4fbb-9cba-fc41e69535d8 · outbound

This paper cites Xil- inx/brevitas, 2026.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Xil- inx/brevitas, 2026

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.666803Z

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Observation dd56d397-4851-44a6-bfd4-9eedde86f5e9 · outbound

This paper cites PhD thesis, Nanyang Technological University, 2026.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning PhD thesis, Nanyang Technological University, 2026

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.651891Z

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Observation 472ec469-c7fd-4202-a7bc-bc20f2a3e66b · outbound

This paper cites Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.876890Z

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Observation 7984698d-cd62-4b11-9a77-c4961a6b917c · outbound

This paper cites A survey on knowledge distillation: Recent advancements.Machine Learning with Applications, 18:100605, 2024.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning A survey on knowledge distillation: Recent advancements.Machine Learning with Applications, 18:100605, 2024

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.636423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3e78e1bd-31f6-49a7-b596-52f600ce70dc · outbound

This paper cites Parameter-Efficient Fine-Tuning for Foundation Models.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Parameter-Efficient Fine-Tuning for Foundation Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.886161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 549f828e-84d0-4978-a23d-8ea951641fb2 · outbound

This paper cites Parameter-efficient fine-tuning in large language models: a survey of methodologies.Artificial Intelligence Review, 58(8):227, 2025.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Parameter-efficient fine-tuning in large language models: a survey of methodologies.Artificial Intelligence Review, 58(8):227, 2025

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.620478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 99a9a95c-ad7b-4a1b-ae3d-a7f8c2c974eb · outbound

This paper cites EfficientLLM: Efficiency in Large Language Models.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning EfficientLLM: Efficiency in Large Language Models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.896063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95e9b53b-fa4a-45f7-a777-131eb99faa1e · outbound

This paper cites Lora: Low-rank adaptation of large language models.Iclr, 1(2):3, 2022.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Lora: Low-rank adaptation of large language models.Iclr, 1(2):3, 2022

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.900970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b724cef-32da-4a3b-bfd6-4c74ca75ced9 · outbound

This paper cites Dora: Weight-decomposed low-rank adaptation.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Dora: Weight-decomposed low-rank adaptation

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.905842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04780a8b-6894-4004-bb1b-c2ab76a10f85 · outbound

This paper cites Pissa: Principal singular values and singular vectors adaptation of large language models.Advances in Neural Information Processing Systems, 37:121038–121072, 2024.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Pissa: Principal singular values and singular vectors adaptation of large language models.Advances in Neural Information Processing Systems, 37:121038–121072, 2024

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.910506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20eb69bb-c2c8-4f0c-abb2-08bea4363807 · outbound

This paper cites A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.915167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a31eb0f7-6cbb-46e1-ba58-324bfbe31f8c · outbound

This paper cites LoRA+: Efficient Low Rank Adaptation of Large Models.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning LoRA+: Efficient Low Rank Adaptation of Large Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.919834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c44040c5-5c5a-4517-abb5-7696df59c01f · outbound

This paper cites Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.924672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4dc8def6-f9f8-48bc-88b5-47677dd96b3e · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.929388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b8ac2dd-b9de-4822-b5e4-2650f6be56eb · outbound

This paper cites Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.934363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14f7c5a9-0957-4d20-99b6-8ef8e17a8bba · outbound

This paper cites React: Synergizing reasoning and acting in language models.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning React: Synergizing reasoning and acting in language models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.565827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 25dbe279-679d-4118-9fc5-bd41db8062ef · outbound

This paper cites Star: Bootstrapping reasoning with reasoning.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Star: Bootstrapping reasoning with reasoning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.944119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd96c66d-f8b9-4ccc-a34d-a1b6fd896b2c · outbound

This paper cites Do As I Can, Not As I Say: Grounding Language in Robotic Affordances.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.948961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 088d5ead-aa71-4010-9f2e-1c239de68f65 · outbound

This paper cites Swiftsage: A generative agent with fast and slow thinking for complex interactive tasks.Advances in Neural Information Processing Systems, 36:23813–23825, 2023.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Swiftsage: A generative agent with fast and slow thinking for complex interactive tasks.Advances in Neural Information Processing Systems, 36:23813–23825, 2023

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.954123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28fa4036-0843-4288-9fce-feba8eaf70ff · outbound

This paper cites Generative agents: Interactive simulacra of human behavior.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Generative agents: Interactive simulacra of human behavior

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.958710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15351073-1d59-48e8-ba11-ab3237ea2bd0 · outbound

This paper cites Toolformer: Language models can teach themselves to use tools.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Toolformer: Language models can teach themselves to use tools

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.963389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b28d0b3-33a7-43f7-9aee-2b0027f12775 · outbound

This paper cites LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.968282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6ebb803-a38b-4543-b969-90e388fa4c3d · outbound

This paper cites Chatgpt and open-ai models: A preliminary review.Future Internet, 15(6):192, 2023.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Chatgpt and open-ai models: A preliminary review.Future Internet, 15(6):192, 2023

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.973500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6afedf5b-7f3c-4828-a104-ca83e2cc5ef8 · outbound

This paper cites Scalable microservices for llm-vs-llm interaction in board games.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Scalable microservices for llm-vs-llm interaction in board games

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.501368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fb90c842-a650-43c5-8670-334baff7bfb4 · outbound

This paper cites ProfilingAgent: Profiling-Guided Agentic Reasoning for Adaptive Model Optimization.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning ProfilingAgent: Profiling-Guided Agentic Reasoning for Adaptive Model Optimization

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-08T12:00:59.109993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d6e3c78c-4f19-46db-b176-2f433c6dccc3 · outbound

This paper cites Llms can compress llms: Adaptive pruning by agents.arXiv preprint arXiv:2601.09694, 2026.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Llms can compress llms: Adaptive pruning by agents.arXiv preprint arXiv:2601.09694, 2026

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-08-08T12:00:59.088402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 88030d47-b17e-458c-b871-3c5ba2694ca3 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.992440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49d5f9f9-981c-4ca5-be10-ca09c99438cb · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Training data-efficient image transformers & distillation through attention

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.996936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e98560c-0cb6-417c-908f-296a73bb33d5 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Swin transformer: Hierarchical vision transformer using shifted windows

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:54.001698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d3a9a44-18eb-49ac-b4c1-a636b0a6c6ec · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Imagenet: A large-scale hierarchical image database

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:54.006393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 868d861c-aed6-436f-8ac6-78219e62fac6 · outbound

This paper cites Learning multiple layers of features from tiny images.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Learning multiple layers of features from tiny images

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:54.011726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 956d96b1-9f9e-4b89-8e6c-cf4e0be9ebd3 · outbound

This paper cites On information and sufficiency.The annals of mathematical statistics, 22(1):79–86, 1951.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning On information and sufficiency.The annals of mathematical statistics, 22(1):79–86, 1951

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:54.016392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72916c1e-cc13-4a68-b234-62a839609eb0 · outbound

This paper cites Imagenet large scale visual recognition challenge.International journal of computer vision, 115(3):211–252, 2015.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Imagenet large scale visual recognition challenge.International journal of computer vision, 115(3):211–252, 2015

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:54.021269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89b0c26c-45cc-42b4-a799-d6b5cdf8b809 · outbound

This paper cites An empirical study on hugging face trends, topics and challenges on stack overflow.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning An empirical study on hugging face trends, topics and challenges on stack overflow

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.426026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e60186d2-8515-4e87-8c32-5a448ab8ab70 · outbound

This paper cites PhD thesis, UNIVERSITY OF KASDI MERBAH OUARGLA.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning PhD thesis, UNIVERSITY OF KASDI MERBAH OUARGLA

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.410371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c65a2b4d-dae9-4c83-927a-050b3f827230 · outbound

This paper cites A comparative study of resnet-pretrained models for computer vision.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning A comparative study of resnet-pretrained models for computer vision

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.394943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e279a665-abaa-4255-9987-289c0eb33ce1 · outbound

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

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:54.040112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f6e8780-fa0c-4608-8191-c0c662376d25 · outbound

This paper cites End-to-end discrete cosine transform integration in spectral convolutional neural networks for resource-efficient deep learning.Applied Soft Computing, page 114599, 2026.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning End-to-end discrete cosine transform integration in spectral convolutional neural networks for resource-efficient deep learning.Applied Soft Computing, page 114599, 2026

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:00:59.377972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers