Paper Citation Record · LEDGER

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training

As of 8 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2507.07149.

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

pith.paper-citation-record.v1
2507.07149 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:03:14.907976Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T08:23:08.693861Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

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verified fuzzy41
unresolved7
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dc282ab2-dd7d-40df-8495-1adc6e8716c6 · outbound

This paper cites Vi-map: Infrastructure-assisted real-time hd mapping for autonomous driving.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Vi-map: Infrastructure-assisted real-time hd mapping for autonomous driving

Reference 1

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Observation 5a75b39a-e878-4b81-90e6-9ef582849195 · outbound

This paper cites Robust real-time multi-vehicle collaboration on asynchronous sensors.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Robust real-time multi-vehicle collaboration on asynchronous sensors

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.678043Z

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Observation 46af045c-c7dd-495d-bfc0-56d0474c7e37 · outbound

This paper cites Autofed: Heterogeneity-aware federated multimodal learning for robust autonomous driving.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Autofed: Heterogeneity-aware federated multimodal learning for robust autonomous driving

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.664818Z

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Observation 5774dbc7-9320-4926-90f9-7f03be6e3cfe · outbound

This paper cites Scaleflow: Efficient deep vision pipeline with closed-loop scale-adaptive inference.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Scaleflow: Efficient deep vision pipeline with closed-loop scale-adaptive inference

Reference 4

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Observation 55738a4f-a7a9-4662-96b4-9f57321eaffa · outbound

This paper cites Speech understanding on tiny devices with a learning cache.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Speech understanding on tiny devices with a learning cache

Reference 5

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Observation 6ffc4ba6-755d-4918-9716-80a43220abb6 · outbound

This paper cites Autodroid: Llm-powered task automation in android.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Autodroid: Llm-powered task automation in android

Reference 6

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Observation fb08c56e-9657-4ddf-bd8e-a82c008359b2 · outbound

This paper cites Mobilegpt: Augmenting llm with human-like app memory for mobile task automation.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Mobilegpt: Augmenting llm with human-like app memory for mobile task automation

Reference 7

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Observation 8cd800b5-2f51-4579-a977-01148fe30bb1 · outbound

This paper cites Experience: Practical challenges for indoor ar applications.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Experience: Practical challenges for indoor ar applications

Reference 8

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Observation 6b24dde8-9999-46a9-aefe-59bb241cb358 · outbound

This paper cites Arise: High-capacity ar offloading inference serving via proactive scheduling.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Arise: High-capacity ar offloading inference serving via proactive scheduling

Reference 9

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Observation bef29838-329a-4b0d-b08f-af52b4d057df · outbound

This paper cites Band: coordinated multi-dnn inference on heterogeneous mobile processors.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Band: coordinated multi-dnn inference on heterogeneous mobile processors

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.583858Z

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Observation 4af86d22-ecf6-41ea-9167-9ab768414d50 · outbound

This paper cites Theia: Gaze-driven and perception-aware volumetric content delivery for mixed reality headsets.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Theia: Gaze-driven and perception-aware volumetric content delivery for mixed reality headsets

Reference 11

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Observation 2c407dc4-757b-4480-9c10-7e87daef78e4 · outbound

This paper cites Zero-effort cross-domain gesture recognition with wi-fi.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Zero-effort cross-domain gesture recognition with wi-fi

Reference 12

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Observation d0dd489b-d726-4ba1-9a81-4b1170acf814 · outbound

This paper cites Rf genesis: Zero-shot generalization of mmwave sensing through simulation-based data synthesis and generative diffusion models.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Rf genesis: Zero-shot generalization of mmwave sensing through simulation-based data synthesis and generative diffusion models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.542663Z

Source-reported events for the cited work

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Observation d258a1f8-e8bd-4c92-b888-db7ec5580aed · outbound

This paper cites Cosmo: contrastive fusion learning with small data for multimodal human activity recognition.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Cosmo: contrastive fusion learning with small data for multimodal human activity recognition

Reference 14

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Observation 842da943-a6f9-4667-bdae-a30bf150ac2b · outbound

This paper cites In USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18), Boston, MA, July 2018.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training In USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18), Boston, MA, July 2018

Reference 15

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Source-reported events for the cited work

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Observation 2c10a7a7-b311-4dde-8c77-d82f6538b838 · outbound

This paper cites On-device training under 256kb memory.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training On-device training under 256kb memory

Reference 16

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Observation 973a34d0-c9d2-4504-a9b9-d5bbb2ea1721 · outbound

This paper cites ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity

Reference 17

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Observation 619ae321-6dc0-4db0-b80e-08fa799a97c0 · outbound

This paper cites Spotlight: Optimizing device place- ment for training deep neural networks.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Spotlight: Optimizing device place- ment for training deep neural networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.487175Z

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Observation 372f9455-1c91-4d4d-81c3-fc5fe6109ae5 · outbound

This paper cites Actnn: Reducing training memory footprint via 2-bit activation compressed training.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Actnn: Reducing training memory footprint via 2-bit activation compressed training

Reference 19

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Observation cfed7143-6c4c-4913-ae9f-7db49ecc20fd · outbound

This paper cites Division: memory efficient training via dual activation precision.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Division: memory efficient training via dual activation precision

Reference 20

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Observation 4b1ea5e9-27ab-475f-95c2-d3cbd2550452 · outbound

This paper cites Flexpoint: An adaptive numerical format for efficient training of deep neural networks.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Flexpoint: An adaptive numerical format for efficient training of deep neural networks

Reference 21

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Observation 48803f5f-a1cb-4fe3-a21e-60079092b4bf · outbound

This paper cites Fractrain: Fractionally squeezing bit savings both temporally and spatially for efficient dnn training.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Fractrain: Fractionally squeezing bit savings both temporally and spatially for efficient dnn training

Reference 22

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Observation 2f0cdc25-9d95-4396-bd19-62198ed1f953 · outbound

This paper cites Gact: Activation com- pressed training for generic network architectures.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Gact: Activation com- pressed training for generic network architectures

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.418086Z

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Observation 2665bcea-9ea4-4691-a78a-2580113258a5 · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 24

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Observation 7dc87a99-b4ee-471f-b191-628768754234 · outbound

This paper cites Fixed-point back-propagation training.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Fixed-point back-propagation training

Reference 25

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Observation fa0a1d20-0916-40c4-b559-82b6465b21e6 · outbound

This paper cites Dynaspa: Exploiting spatial sparsity for efficient dynamic dnn inference on devices.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Dynaspa: Exploiting spatial sparsity for efficient dynamic dnn inference on devices

Reference 26

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Observation 81ef9d7d-abbb-421a-9029-b732ecbe5bae · outbound

This paper cites Memory-efficient dnn training on mobile devices.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Memory-efficient dnn training on mobile devices

Reference 27

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Observation 71dec313-57b5-4502-8027-ecc1d23f8084 · outbound

This paper cites Poet: Training neural networks on tiny devices with integrated rematerialization and paging.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Poet: Training neural networks on tiny devices with integrated rematerialization and paging

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.361757Z

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Observation 60d27467-1a38-4464-9cd0-27c027ac56e6 · outbound

This paper cites Elastictrainer: Speeding up on-device training with runtime elastic tensor selection.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Elastictrainer: Speeding up on-device training with runtime elastic tensor selection

Reference 29

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Observation dacaa99f-92f0-4402-b86f-3867dee9b0dd · outbound

This paper cites Nvidia cub, 2024.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Nvidia cub, 2024

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.332315Z

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Observation 74ef4342-b013-48a6-b5fa-f5709f5267ac · outbound

This paper cites Identity mappings in deep residual networks.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Identity mappings in deep residual networks

Reference 31

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Observation 01bbd465-1762-4d0c-bcd4-027fecc72f35 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 32

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Observation 8f789fe3-49cd-4b40-82dd-5ba8e878b30f · outbound

This paper cites Language models are unsupervised multitask learners.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Language models are unsupervised multitask learners

Reference 33

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Observation 94039612-c6d1-4265-9c8c-25e6ceaccb27 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training LoRA: Low-Rank Adaptation of Large Language Models

Reference 34

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Observation 49ead13a-ea02-4504-9441-0b810b4a4e66 · outbound

This paper cites Throughput of native arithmetic instructions, 2024.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Throughput of native arithmetic instructions, 2024

Reference 35

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Observation 564ac629-9d5f-4d28-aae3-afcd264e88ed · outbound

This paper cites Instruction throughput and latency, 2024.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Instruction throughput and latency, 2024

Reference 36

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Observation 6fa0b337-55c8-49b3-9388-abee6a3c395f · outbound

This paper cites TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Reference 37

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This paper cites The multiobjective multidimensional knapsack problem: a survey and a new approach.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training The multiobjective multidimensional knapsack problem: a survey and a new approach

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.263921Z

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Observation 073de215-44ea-4c29-bd78-bc5202573de7 · outbound

This paper cites Canadian institute for advanced research, 10 classes,.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Canadian institute for advanced research, 10 classes,

Reference 39

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Observation f17c98c2-ed1d-412e-ab46-6183e005cd44 · outbound

This paper cites Canadian institute for advanced research, 100 classes,.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Canadian institute for advanced research, 100 classes,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.235565Z

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Observation baf7a4c6-ea90-432f-b42a-24a8d3da3ef5 · outbound

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DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Imagenet: A large-scale hierarchical image database,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.220482Z

Source-reported events for the cited work

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Observation 4bbb7734-eb8b-400c-88d3-960b4b12cc3c · outbound

This paper cites Semantic textual similarity,.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Semantic textual similarity,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.205964Z

Source-reported events for the cited work

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Observation a80956cb-42b2-4d00-ba6a-00e4fd8bfd89 · outbound

This paper cites Microsoft research paraphrase corpus,.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Microsoft research paraphrase corpus,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.190554Z

Source-reported events for the cited work

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Observation c828255c-d009-4960-9252-27b69df83ac8 · outbound

This paper cites The stanford sentiment treebank,.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training The stanford sentiment treebank,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.174569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6bc568b2-c008-40d4-8fda-2dc0481d35fd · outbound

This paper cites End-to-end nlg challenge,.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training End-to-end nlg challenge,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.159970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 76dfb396-d2f9-404c-a31d-04fbacd6b014 · outbound

This paper cites Creating training corpora for nlg micro-planners,.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Creating training corpora for nlg micro-planners,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.145611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 24cb2ed6-63ba-4b0b-8e44-cbf1b65cb704 · outbound

This paper cites Towards unified int8 training for convolutional neural network.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Towards unified int8 training for convolutional neural network

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.131224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5c06b368-ee71-42ed-acf8-a06a99903e87 · outbound

This paper cites Re- source management with deep reinforcement learning.

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training Re- source management with deep reinforcement learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:03:15.116114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 635a5586-8c90-40b4-839a-4cbbfea2d48d · inbound

FBLayout: Optimizing Memory Layout for Efficient LLM Finetuning on Mobile GPUs cites this paper.

FBLayout: Optimizing Memory Layout for Efficient LLM Finetuning on Mobile GPUs DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training

Reference 30

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