Paper Citation Record · LEDGER

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments

As of 19 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2507.19261.

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

pith.paper-citation-record.v1
2507.19261 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:02:16.485428Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

37 of 37 outbound references displayed

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verified fuzzy19
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b705f4d7-a273-4f95-aace-aa9f58997453 · outbound

This paper cites Adaptive edge-cloud environments for rural ai.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Adaptive edge-cloud environments for rural ai

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.944821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation efc2e989-5688-40e5-b135-d22694c60d11 · outbound

This paper cites Language Models are Few-Shot Learners.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Language Models are Few-Shot Learners

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T18:02:16.351292Z

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Observation ad8939d7-30aa-4d7e-81e2-2071b0f92fe7 · outbound

This paper cites An Analysis of Deep Neural Network Models for Practical Applications.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments An Analysis of Deep Neural Network Models for Practical Applications

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T18:02:16.355690Z

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Observation 51a07438-94d6-40c4-b612-d6f19eaf2475 · outbound

This paper cites Evaluating deep learning models for effective weed classification in agricultural images.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Evaluating deep learning models for effective weed classification in agricultural images

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.932160Z

Source-reported events for the cited work

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Observation ebac0ed0-f348-4a71-a50c-b38083d862a8 · outbound

This paper cites Dynamic network surgery for efficient dnns.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Dynamic network surgery for efficient dnns

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.920460Z

Source-reported events for the cited work

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Observation 42dc18e4-d3bc-40b2-a03f-962d4855c1d1 · outbound

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

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T18:02:16.368908Z

Source-reported events for the cited work

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Observation 11b39765-c535-4690-8429-b6d4300a52c5 · outbound

This paper cites Learning both weights and connec- tions for efficient neural network.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Learning both weights and connec- tions for efficient neural network

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.907818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 26587e04-bf36-42d7-b265-d642c381db84 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Distilling the Knowledge in a Neural Network

Reference 8

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Observation fda4aa1d-d0bf-41dc-a99c-a49fd32c67e8 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T18:02:16.382149Z

Source-reported events for the cited work

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Observation acc10c87-becf-4328-a654-15a5837bf91b · outbound

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

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Quantization and training of neu- ral networks for efficient integer-arithmetic-only inference

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.895193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation be97c392-6563-4730-827a-a20a592884d0 · outbound

This paper cites Spherical linear interpolation and bézier curves.General Scientific Researches, 2(1):13–17, 2014.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Spherical linear interpolation and bézier curves.General Scientific Researches, 2(1):13–17, 2014

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.884092Z

Source-reported events for the cited work

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Observation b726b121-b913-4bd8-934f-ea90104648d7 · outbound

This paper cites An edge-cloud infrastructure for weed detection in precision agriculture.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments An edge-cloud infrastructure for weed detection in precision agriculture

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.871519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0a91a74b-54a1-4efa-b4cf-b0df4c6c9d25 · outbound

This paper cites Tosim- iot: Towardsasustainableoptimisationofmachinelearningtasksininternetofthings.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Tosim- iot: Towardsasustainableoptimisationofmachinelearningtasksininternetofthings

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.858922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2d7bfc4e-1faa-405e-accb-21bf9a2929ce · outbound

This paper cites Shield: A secure heuristic integrated environment for load distribution in rural-ai.Future Generation Computer Systems, 2024.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Shield: A secure heuristic integrated environment for load distribution in rural-ai.Future Generation Computer Systems, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.845515Z

Source-reported events for the cited work

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Observation 973660c0-e02d-48a7-ae6d-2890589b58f4 · outbound

This paper cites Tosim- iot: Towardsasustainableoptimisationofmachinelearningtasksininternetofthings.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Tosim- iot: Towardsasustainableoptimisationofmachinelearningtasksininternetofthings

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.832723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b1d7c896-ebc6-45db-b5f1-c5275766bed6 · outbound

This paper cites Do better imagenet models transfer better? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2661–2671, 2019.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Do better imagenet models transfer better? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2661–2671, 2019

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.820949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 93cde9f8-a414-447f-972d-68e747f95f95 · outbound

This paper cites Quantifying the Carbon Emissions of Machine Learning.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Quantifying the Carbon Emissions of Machine Learning

Reference 17

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Source-reported events for the cited work

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Observation 6ef48d08-ca1a-4f09-934b-a9dcebb503c9 · outbound

This paper cites Crossfuse: A novel cross attention mechanism based infrared and visible image fusion approach.Information Fusion, 103:102147, 2024.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Crossfuse: A novel cross attention mechanism based infrared and visible image fusion approach.Information Fusion, 103:102147, 2024

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.808507Z

Source-reported events for the cited work

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Observation 28b890b2-6d1c-43ac-ab87-e0eeb80b26a5 · outbound

This paper cites Pruning and Quantization for Deep Neural Network Acceleration: A Survey.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Pruning and Quantization for Deep Neural Network Acceleration: A Survey

Reference 19

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Source-reported events for the cited work

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Observation 44a47e42-dbd5-4135-92ba-eba9ba46887e · outbound

This paper cites Learning efficient convolutional networks through network slimming.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Learning efficient convolutional networks through network slimming

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T18:02:16.422896Z

Source-reported events for the cited work

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Observation 1629f109-048c-4eef-ad2d-0aed961e9904 · outbound

This paper cites Matching the ideal pruning method with knowledge distillation for optimal compression.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Matching the ideal pruning method with knowledge distillation for optimal compression

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.790213Z

Source-reported events for the cited work

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Observation 1b262d0b-71dc-4153-8354-92599f1343df · outbound

This paper cites Augmented Language Models: a Survey.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Augmented Language Models: a Survey

Reference 22

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Observation 26a18725-9bc2-4d9f-85fc-6aa270553e9a · outbound

This paper cites Konovalov, Bronson Philippa, Peter Ridd, Jake C.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Konovalov, Bronson Philippa, Peter Ridd, Jake C

Reference 23

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Observation 77ffd00e-e15e-4286-aa6d-afe103adc4a8 · outbound

This paper cites Rural ai: Serverless-powered federated learning for remote applications.IEEE Internet Computing, 27(2):28–34, 2022.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Rural ai: Serverless-powered federated learning for remote applications.IEEE Internet Computing, 27(2):28–34, 2022

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.777718Z

Source-reported events for the cited work

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Observation 611e8552-b224-4375-9e3e-ad5da92da699 · outbound

This paper cites Model compression via distillation and quantization.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Model compression via distillation and quantization

Reference 25

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Source-reported events for the cited work

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Observation 6951021a-d86f-4790-8315-8671cf92c5df · outbound

This paper cites FitNets: Hints for Thin Deep Nets.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments FitNets: Hints for Thin Deep Nets

Reference 26

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Observation 6e06eaf0-f9b7-484e-8c3f-cee5c772d207 · outbound

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

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T18:02:16.447915Z

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Observation 4f034531-847f-4687-8a13-599ca5c2c7d2 · outbound

This paper cites Energy and Policy Considerations for Deep Learning in NLP.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Energy and Policy Considerations for Deep Learning in NLP

Reference 28

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Source-reported events for the cited work

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Observation ae377ffc-2a90-4a32-830b-85e09a942527 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.757320Z

Source-reported events for the cited work

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Observation 565b917f-6938-4f10-9694-90b3fe7a0eff · outbound

This paper cites Post-training quantization.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Post-training quantization

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.742957Z

Source-reported events for the cited work

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Observation d000562a-907d-493d-a27d-38e7997de03d · outbound

This paper cites Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T18:02:16.463283Z

Source-reported events for the cited work

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Observation 36645a81-714b-4cc4-83e8-b786b7407b31 · outbound

This paper cites Combining multi-objective genetic algorithm and neural network dynamically for the com- plex optimization problems in physics.Scientific Reports, 13(1):1463, 2023.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Combining multi-objective genetic algorithm and neural network dynamically for the com- plex optimization problems in physics.Scientific Reports, 13(1):1463, 2023

Reference 32

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T18:02:16.729964Z

Source-reported events for the cited work

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Observation 06aa728c-9d30-43ab-8e2d-6a8b686423ee · outbound

This paper cites Routing Experts: Learning to Route Dynamic Experts in Multi-modal Large Language Models.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Routing Experts: Learning to Route Dynamic Experts in Multi-modal Large Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T18:02:16.470816Z

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Observation 8bedf498-7d56-492f-b03c-8e36f042ed07 · outbound

This paper cites Designing energy-efficient convolutional neural networks using energy-aware pruning.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Designing energy-efficient convolutional neural networks using energy-aware pruning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.718328Z

Source-reported events for the cited work

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Observation 4a29913a-113e-49d5-babb-5161d1cbbc64 · outbound

This paper cites Scalify: scale propagation for efficient low-precision LLM training.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Scalify: scale propagation for efficient low-precision LLM training

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T18:02:16.477558Z

Source-reported events for the cited work

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Observation fc2c6d58-ff02-48bb-b83a-a37fd7a2512b · outbound

This paper cites Howtransferablearefeaturesin deep neural networks?Advances in Neural Information Processing Systems, 27:3320–3328, 2014.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Howtransferablearefeaturesin deep neural networks?Advances in Neural Information Processing Systems, 27:3320–3328, 2014

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.706296Z

Source-reported events for the cited work

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Observation 8cf7ae19-67a6-4a96-94d3-baaa3d42d2df · outbound

This paper cites Deep neural networks with multi- branch architectures are intrinsically less non-convex.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Deep neural networks with multi- branch architectures are intrinsically less non-convex

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:02:16.692724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

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