Paper Citation Record · LEDGER

Once-for-All: Train One Network and Specialize it for Efficient Deployment

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 43 inbound Pith citation observations for arXiv:1908.09791.

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

pith.paper-citation-record.v1
1908.09791 v5

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 43 of 43 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 43 of 43 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:49:36.179681Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T16:58:42.984963Z

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External citation measurements

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Outbound references

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Pith citing papers

Observation 37752f1a-b871-4976-a746-a6cc2ba20b2d · inbound

Inner Monologue: Embodied Reasoning through Planning with Language Models cites this paper.

Inner Monologue: Embodied Reasoning through Planning with Language Models Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 89

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:10:44.959396Z

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 e8dd7067-09a8-4604-91fe-5591af87f3cf · inbound

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights cites this paper.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T20:49:36.179681Z

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Observation 02f74818-fb6d-4c7e-b14a-a15d7ed7e395 · inbound

Synergistic Effects of Knowledge Distillation and Structured Pruning for Self-Supervised Speech Models cites this paper.

Synergistic Effects of Knowledge Distillation and Structured Pruning for Self-Supervised Speech Models Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T17:49:20.077724Z

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Observation 0926c307-2e17-47fc-bd36-b637063f0ae2 · inbound

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT cites this paper.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T16:49:23.273243Z

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Observation 6eacaf97-e309-42b5-96b8-72f4521e9fc6 · inbound

EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models cites this paper.

EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T14:48:53.736258Z

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Observation cf065fd0-e16c-4102-9f24-a7bbf6a711ea · inbound

Heterogeneous Resource Allocation with Multi-task Learning for Wireless Networks cites this paper.

Heterogeneous Resource Allocation with Multi-task Learning for Wireless Networks Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T19:45:13.173738Z

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Observation d220d2b8-0d89-457a-a6c9-d34cb4bce3a5 · inbound

Searching Neural Architectures for Sensor Nodes on IoT Gateways cites this paper.

Searching Neural Architectures for Sensor Nodes on IoT Gateways Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 37

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Source-reported events for the cited work

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Observation a3bcb3f3-8e8c-425d-85ca-8c3598d332e8 · inbound

Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices cites this paper.

Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 9

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Observation fb0dcbd9-5916-424f-95a3-82d6f648f1f8 · inbound

Frugal Machine Learning for Energy-efficient, and Resource-aware Artificial Intelligence cites this paper.

Frugal Machine Learning for Energy-efficient, and Resource-aware Artificial Intelligence Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 85

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Observation 885119fc-4a88-47a9-b3f7-f2e40c076c2a · inbound

FlexGS: Train Once, Deploy Everywhere with Many-in-One Flexible 3D Gaussian Splatting cites this paper.

FlexGS: Train Once, Deploy Everywhere with Many-in-One Flexible 3D Gaussian Splatting Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T10:52:26.157558Z

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Observation 809b1e8e-d131-4e25-93d8-dc6763f72a8e · inbound

Loss Functions for Predictor-based Neural Architecture Search cites this paper.

Loss Functions for Predictor-based Neural Architecture Search Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T10:20:30.804900Z

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Observation 338efdd6-40c5-4870-b53c-d583248bfe66 · inbound

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning cites this paper.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 11

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Observation 105abdbf-7510-4142-9daf-fd3206018389 · inbound

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices cites this paper.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 2

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Observation 986922d5-ecc3-4c4b-a2f4-77cf1fac760b · inbound

NN-Former: Rethinking Graph Structure in Neural Architecture Representation cites this paper.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T21:11:58.008671Z

Source-reported events for the cited work

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Observation b7a8ce25-7798-4d00-894a-dac55c786b41 · inbound

DANCE: Resource-Efficient Neural Architecture Search with Data-Aware and Continuous Adaptation cites this paper.

DANCE: Resource-Efficient Neural Architecture Search with Data-Aware and Continuous Adaptation Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T19:48:31.612621Z

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Observation a133ab54-e034-48ca-950b-793dff18b477 · inbound

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design cites this paper.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T10:50:47.271808Z

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Observation 79a20d9d-a962-428d-9f0a-97bd0ae5ffb3 · inbound

ESM: A Framework for Building Effective Surrogate Models for Hardware-Aware Neural Architecture Search cites this paper.

ESM: A Framework for Building Effective Surrogate Models for Hardware-Aware Neural Architecture Search Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T05:40:26.466244Z

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Observation 42414928-292c-4db6-b228-094df75cdeaa · inbound

SHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated Learning cites this paper.

SHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated Learning Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 13

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Observation a41012ab-f2a8-4769-9a94-d3c7ffea6e9c · inbound

SAR-NAS: Lightweight SAR Object Detection with Neural Architecture Search cites this paper.

SAR-NAS: Lightweight SAR Object Detection with Neural Architecture Search Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 3

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Observation 6124280a-fa44-413b-8b31-46c55ca326c2 · inbound

Spiking Neural Network Architecture Search: A Survey cites this paper.

Spiking Neural Network Architecture Search: A Survey Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 132

Resolution: verified exact; arxiv_id, observed 2026-05-18T07:01:01.421984Z

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Observation b3b6b4a4-5130-47d4-a6a8-116cf70259d8 · inbound

Elastic ViTs from Pretrained Models without Retraining cites this paper.

Elastic ViTs from Pretrained Models without Retraining Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 5

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Observation cae1732a-e175-49ac-9b31-ce094150e4ea · inbound

Animal Re-Identification on Microcontrollers cites this paper.

Animal Re-Identification on Microcontrollers Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-03T17:48:13.318344Z

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Observation b3053f27-be98-463d-b7ac-cbc02da01268 · inbound

SuperSFL: Resource-Heterogeneous Federated Split Learning with Weight-Sharing Super-Networks cites this paper.

SuperSFL: Resource-Heterogeneous Federated Split Learning with Weight-Sharing Super-Networks Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 27

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Observation 52fee64e-65f4-4af8-8aef-17d82dc39250 · inbound

DeepFedNAS: Efficient Hardware-Aware Architecture Adaptation for Heterogeneous IoT Federations via Pareto-Guided Supernet Training cites this paper.

DeepFedNAS: Efficient Hardware-Aware Architecture Adaptation for Heterogeneous IoT Federations via Pareto-Guided Supernet Training Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:07:50.758412Z

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

Observation 9c29ef74-b686-48e2-9d7c-969da39d1ade · inbound

Prune-Quantize-Distill: An Ordered Pipeline for Efficient Neural Network Compression cites this paper.

Prune-Quantize-Distill: An Ordered Pipeline for Efficient Neural Network Compression Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:08:01.020362Z

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

Observation cd391fef-4f10-4d57-82b8-9eff3590b800 · inbound

Streaming Chain cites this paper.

Streaming Chain Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-13T10:56:59.609600Z

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Observation 0d97812e-4ffa-444a-93c3-ae72bbfe83f3 · inbound

Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading cites this paper.

Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:21:26.881574Z

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Observation 59b57cf3-01bb-431a-8fe1-659ec262777a · inbound

Equinox: Decentralized Scheduling for Hardware-Aware Orbital Intelligence cites this paper.

Equinox: Decentralized Scheduling for Hardware-Aware Orbital Intelligence Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T01:04:50.172617Z

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

Observation f70826cd-c320-444e-8382-2a8c5d9df4b3 · inbound

SWAN: World-Aware Adaptive Multimodal Networks for Runtime Variations cites this paper.

SWAN: World-Aware Adaptive Multimodal Networks for Runtime Variations Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T23:51:29.173168Z

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 75e93246-ce57-4706-aa82-5e71b6c836fc · inbound

MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning cites this paper.

MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:10:53.361048Z

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

Observation 61dd7681-a89f-42c0-8405-ff5727431987 · inbound

TCP-SSM: Efficient Vision State Space Models with Token-Conditioned Poles cites this paper.

TCP-SSM: Efficient Vision State Space Models with Token-Conditioned Poles Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:52:04.979527Z

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

Observation f319114b-7907-4c6d-81b4-f153cc85b298 · inbound

Elastic Attention Cores for Scalable Vision Transformers cites this paper.

Elastic Attention Cores for Scalable Vision Transformers Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:07:22.548121Z

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

Observation 484a44ff-f4c7-4aa6-9127-9a1973bf049e · inbound

SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesign cites this paper.

SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesign Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:33:55.744464Z

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

Observation 0dd26a03-81f9-41dc-aff5-8889f31b2e82 · inbound

AutoMCU: Feasibility-First MCU Neural Network Customization via LLM-based Multi-Agent Systems cites this paper.

AutoMCU: Feasibility-First MCU Neural Network Customization via LLM-based Multi-Agent Systems Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-22T10:01:22.917160Z

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

Observation da8a32c6-8202-4e72-af25-5faddf354b17 · inbound

Orion: Enabling Self-adaptive Memory Management for On-device Online Continual Learning cites this paper.

Orion: Enabling Self-adaptive Memory Management for On-device Online Continual Learning Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:33:39.070799Z

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 bc7d9a17-38e3-4a69-9153-61267365686e · inbound

JetViT: Efficient High-Resolution Vision Transformer with Post-Training Attention Search cites this paper.

JetViT: Efficient High-Resolution Vision Transformer with Post-Training Attention Search Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:13:48.378792Z

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 5001c9a0-5d0e-47b9-b8c7-9e1d6a600d13 · inbound

elasticAI.explorer: Towards a Unified End-to-End Framework for Hardware-Aware Neural Architecture Search cites this paper.

elasticAI.explorer: Towards a Unified End-to-End Framework for Hardware-Aware Neural Architecture Search Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T00:22:51.653856Z

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 6c693300-a5ab-4e75-898c-23682d9dffe7 · inbound

DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation cites this paper.

DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 121

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T19:02:34.584113Z

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 45df35fd-09cb-4108-b4af-dcc807f98cf4 · inbound

HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning cites this paper.

HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-06-28T19:32:34.807581Z

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

Observation 1f3707d0-62a2-4585-9d21-fc674e1d820d · inbound

Structure-Conditioned Actor-Critic Branches for Quality-Diversity Reinforcement Learning cites this paper.

Structure-Conditioned Actor-Critic Branches for Quality-Diversity Reinforcement Learning Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:47:26.382074Z

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

Observation 20283905-95a2-439d-9a50-05231c6a2df6 · inbound

Running hardware-aware neural architecture search on embedded devices under 512MB of RAM cites this paper.

Running hardware-aware neural architecture search on embedded devices under 512MB of RAM Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T16:58:42.986867Z

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 8153b7b6-f5c9-4879-b622-168625e4bebd · inbound

Bilevel Optimization for Neural Architecture Search cites this paper.

Bilevel Optimization for Neural Architecture Search Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:34:21.961588Z

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 c99c709b-62d4-4cb2-acbb-e4ea6ceab339 · inbound

Adaptive Routing for Efficient Diffusion Transformer-Based PNI Prediction cites this paper.

Adaptive Routing for Efficient Diffusion Transformer-Based PNI Prediction Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-14T04:56:11.857547Z

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Unavailable: canonical work link unavailable.