Paper Citation Record · LEDGER

ACME: Adaptive Customization of Large Models via Distributed Systems

As of 15 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2507.14802.

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

pith.paper-citation-record.v1
2507.14802 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:55:23.421547Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

43 of 43 outbound references displayed

verified exact0
verified fuzzy38
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3060f1af-8aa4-42d7-af14-6afeee05fa6d · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

ACME: Adaptive Customization of Large Models via Distributed Systems An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.726543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 366cbead-ebf9-44ac-986e-54ee3f3d6d9b · outbound

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

ACME: Adaptive Customization of Large Models via Distributed Systems BERT: Pre- training of deep bidirectional transformers for language understanding,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.719184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e06c8607-9a9c-4b58-b8dd-8607e8ab2b87 · outbound

This paper cites Recent advances in natural language processing via large pre-trained language models: A survey,.

ACME: Adaptive Customization of Large Models via Distributed Systems Recent advances in natural language processing via large pre-trained language models: A survey,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T15:55:23.342074Z

Source-reported events for the cited work

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Observation 6120c4d5-c2b3-491f-8266-6ee57465e9f0 · outbound

This paper cites Large language models and future of information retrieval: Opportunities and challenges,.

ACME: Adaptive Customization of Large Models via Distributed Systems Large language models and future of information retrieval: Opportunities and challenges,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.706882Z

Source-reported events for the cited work

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Observation f7154ef5-dddf-43cd-af27-0219209281b5 · outbound

This paper cites EdgeShard: Efficient LLM Inference via Collaborative Edge Computing.

ACME: Adaptive Customization of Large Models via Distributed Systems EdgeShard: Efficient LLM Inference via Collaborative Edge Computing

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T15:55:23.346579Z

Source-reported events for the cited work

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Observation b5efc77c-42d9-4e20-a430-af92ced0b5de · outbound

This paper cites MobileLLM: Optimizing sub-billion parameter language models for on-device use cases,.

ACME: Adaptive Customization of Large Models via Distributed Systems MobileLLM: Optimizing sub-billion parameter language models for on-device use cases,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.699668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1f6b5852-576b-4ac8-805f-3c2bb01ea697 · outbound

This paper cites To talk or to work: Flexible communication compression for energy efficient federated learning over heterogeneous mobile edge devices,.

ACME: Adaptive Customization of Large Models via Distributed Systems To talk or to work: Flexible communication compression for energy efficient federated learning over heterogeneous mobile edge devices,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.692406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation af1b174f-f373-4bf1-a270-8fc73c956e77 · outbound

This paper cites Dependency-aware microservice deployment for edge computing: A deep reinforcement learning approach with network representation,.

ACME: Adaptive Customization of Large Models via Distributed Systems Dependency-aware microservice deployment for edge computing: A deep reinforcement learning approach with network representation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.684999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9aa58c64-8fd7-4a32-bf3c-9dba0a2d2c4f · outbound

This paper cites Finch: Enhancing federated learning with hierarchical neural architecture search,.

ACME: Adaptive Customization of Large Models via Distributed Systems Finch: Enhancing federated learning with hierarchical neural architecture search,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.677735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 52077777-ed7e-46c6-8b3c-f791ebd0dddf · outbound

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

ACME: Adaptive Customization of Large Models via Distributed Systems Learning multiple layers of features from tiny images,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T15:55:23.356923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2db9d6ad-3323-49c5-b50d-61e4d2829c33 · outbound

This paper cites Distributed pruning towards tiny neural networks in federated learning,.

ACME: Adaptive Customization of Large Models via Distributed Systems Distributed pruning towards tiny neural networks in federated learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.665907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation df2595be-4d05-40cd-91a0-eff3959d99f4 · outbound

This paper cites Scalable federated learning with system heterogeneity,.

ACME: Adaptive Customization of Large Models via Distributed Systems Scalable federated learning with system heterogeneity,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.658829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3bf79188-7f4c-4f1a-9345-79c90ad836b6 · outbound

This paper cites Cur- CoEdge: Curiosity-driven collaborative request scheduling in edge-cloud systems,.

ACME: Adaptive Customization of Large Models via Distributed Systems Cur- CoEdge: Curiosity-driven collaborative request scheduling in edge-cloud systems,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.651981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cafbc1b6-6134-49f2-8439-2cc1b403d2e3 · outbound

This paper cites MG²FL: Multi- granularity grouping-based federated learning in green edge computing systems,.

ACME: Adaptive Customization of Large Models via Distributed Systems MG²FL: Multi- granularity grouping-based federated learning in green edge computing systems,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.645538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7df02a7c-fc7a-4970-b1a4-bd502442e595 · outbound

This paper cites Energy-efficient inference ser- vice of transformer-based deep learning models on gpus,.

ACME: Adaptive Customization of Large Models via Distributed Systems Energy-efficient inference ser- vice of transformer-based deep learning models on gpus,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.639057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b11c58c9-9abb-4a54-a26f-3a3dbdc7e472 · outbound

This paper cites EfficientNet: Rethinking model scaling for convo- lutional neural networks,.

ACME: Adaptive Customization of Large Models via Distributed Systems EfficientNet: Rethinking model scaling for convo- lutional neural networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.632550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 195bc80f-701a-42ea-805c-900c8b50f5c6 · outbound

This paper cites Dyn- aBERT: Dynamic bert with adaptive width and depth,.

ACME: Adaptive Customization of Large Models via Distributed Systems Dyn- aBERT: Dynamic bert with adaptive width and depth,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.625797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 175a96f8-4e13-44d9-8d71-739e29b56fe6 · outbound

This paper cites A constrained decomposition approach with grids for evolutionary multiobjective optimization,.

ACME: Adaptive Customization of Large Models via Distributed Systems A constrained decomposition approach with grids for evolutionary multiobjective optimization,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.619361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4a50d242-df03-4ad0-b39f-ae0200548500 · outbound

This paper cites A pareto front grid guided multi-objective evolutionary algorithm,.

ACME: Adaptive Customization of Large Models via Distributed Systems A pareto front grid guided multi-objective evolutionary algorithm,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.612860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2131eb4d-7544-43c8-b9e6-fb6ced3931c2 · outbound

This paper cites Progressive neural architecture search,.

ACME: Adaptive Customization of Large Models via Distributed Systems Progressive neural architecture search,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.606406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 87676e55-d6df-4535-9b10-c9b86dc8d3a9 · outbound

This paper cites LGViT: Dynamic early exiting for accelerating vision transformer,.

ACME: Adaptive Customization of Large Models via Distributed Systems LGViT: Dynamic early exiting for accelerating vision transformer,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.600015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation eadc5eab-02e5-4ca1-b198-b8d2bc510edd · outbound

This paper cites Single-layer vision trans- formers for more accurate early exits with less overhead,.

ACME: Adaptive Customization of Large Models via Distributed Systems Single-layer vision trans- formers for more accurate early exits with less overhead,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.593492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dc9aad7e-196f-433c-9344-d2733726ed7d · outbound

This paper cites Learning transferable architectures for scalable image recognition,.

ACME: Adaptive Customization of Large Models via Distributed Systems Learning transferable architectures for scalable image recognition,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.587025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 22db16dd-157a-4331-9ac6-320fd49f9e70 · outbound

This paper cites ENASFL: A federated neural architecture search scheme for heterogeneous deep models in distributed edge computing systems,.

ACME: Adaptive Customization of Large Models via Distributed Systems ENASFL: A federated neural architecture search scheme for heterogeneous deep models in distributed edge computing systems,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.580401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ac6436a3-0cb5-4f1a-bcdc-8d84762a2776 · outbound

This paper cites Efficient neural architecture search via parameter sharing,.

ACME: Adaptive Customization of Large Models via Distributed Systems Efficient neural architecture search via parameter sharing,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.573891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 41101bee-e2bd-4ef0-89cd-265ea8583def · outbound

This paper cites Simple statistical gradient-following algorithms for connectionist reinforcement learning,.

ACME: Adaptive Customization of Large Models via Distributed Systems Simple statistical gradient-following algorithms for connectionist reinforcement learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.567559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 97d31c6d-8902-4e69-bed5-9c769e1af53f · outbound

This paper cites Importance estimation for neural network pruning,.

ACME: Adaptive Customization of Large Models via Distributed Systems Importance estimation for neural network pruning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.560704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e86a362f-5e1d-4b86-87ea-4d54558f3339 · outbound

This paper cites Data valuation and detections in federated learning,.

ACME: Adaptive Customization of Large Models via Distributed Systems Data valuation and detections in federated learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.553602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 63186e27-17d4-4ed3-8cc0-465475edec5d · outbound

This paper cites Once-for-all: Train one network and specialize it for efficient deployment,.

ACME: Adaptive Customization of Large Models via Distributed Systems Once-for-all: Train one network and specialize it for efficient deployment,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.547179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6711a53f-3b22-4e8b-8bf7-4a68a2a5ded1 · outbound

This paper cites BERxiT: Early exiting for BERT with better fine-tuning and extension to regression,.

ACME: Adaptive Customization of Large Models via Distributed Systems BERxiT: Early exiting for BERT with better fine-tuning and extension to regression,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.540411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7c727b8e-75eb-4908-b078-20f211a8b33e · outbound

This paper cites EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models.

ACME: Adaptive Customization of Large Models via Distributed Systems EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T15:55:23.397225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f83f73a7-552d-4f13-bb1d-0eabe2da0d87 · outbound

This paper cites A survey of visual transformers,.

ACME: Adaptive Customization of Large Models via Distributed Systems A survey of visual transformers,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.533588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 30f74189-9410-4df3-b9e6-8ecdc938aae6 · outbound

This paper cites Efficient-ViT: A light-weight classification model based on CNN and ViT,.

ACME: Adaptive Customization of Large Models via Distributed Systems Efficient-ViT: A light-weight classification model based on CNN and ViT,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.526870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4692a5ff-49c5-4079-8832-c1f8b67c03d2 · outbound

This paper cites MobileViT: Light-weight, general-purpose, and mobile-friendly vision transformer,.

ACME: Adaptive Customization of Large Models via Distributed Systems MobileViT: Light-weight, general-purpose, and mobile-friendly vision transformer,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.520409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation efcad53d-3b0d-4522-9987-9a6b2b0e08c3 · outbound

This paper cites Twins: Revisiting the design of spatial attention in vision transformers,.

ACME: Adaptive Customization of Large Models via Distributed Systems Twins: Revisiting the design of spatial attention in vision transformers,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.512953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 74e5d64f-bea9-4631-9a9b-ecdab2fab9ba · outbound

This paper cites DeViT: Decomposing vision transformers for collaborative inference in edge devices,.

ACME: Adaptive Customization of Large Models via Distributed Systems DeViT: Decomposing vision transformers for collaborative inference in edge devices,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.506104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f0247336-7eb8-416c-80b4-daf8895d0bb7 · outbound

This paper cites Multi-exit vision trans- former for dynamic inference,.

ACME: Adaptive Customization of Large Models via Distributed Systems Multi-exit vision trans- former for dynamic inference,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.499136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e6ac4da0-7ad4-48c1-9af6-cdc99505bb6a · outbound

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

ACME: Adaptive Customization of Large Models via Distributed Systems MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T15:55:23.411506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b92e1ac-f36a-4ae3-abc5-890328f41a2f · outbound

This paper cites Morphnet: Fast & simple resource-constrained structure learn- ing of deep networks,.

ACME: Adaptive Customization of Large Models via Distributed Systems Morphnet: Fast & simple resource-constrained structure learn- ing of deep networks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.491159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ff59767e-5a4f-46af-9eab-ded05a74f309 · outbound

This paper cites Adaptive weighted sum method for multiobjective optimization: a new method for pareto front generation,.

ACME: Adaptive Customization of Large Models via Distributed Systems Adaptive weighted sum method for multiobjective optimization: a new method for pareto front generation,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.483793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 912a253f-920d-4598-8000-07b84572aef6 · outbound

This paper cites 3d object representations for fine-grained categorization,.

ACME: Adaptive Customization of Large Models via Distributed Systems 3d object representations for fine-grained categorization,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.476900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3fbe7c29-952f-4775-9352-59d92e48bb89 · outbound

This paper cites Resource-aware federated neural architecture search over heteroge- neous mobile devices,.

ACME: Adaptive Customization of Large Models via Distributed Systems Resource-aware federated neural architecture search over heteroge- neous mobile devices,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.469760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f22f299e-b838-4cd6-81da-cc77d651576d · outbound

This paper cites Toward tailored models on private aiot devices: Federated direct neural architecture search,.

ACME: Adaptive Customization of Large Models via Distributed Systems Toward tailored models on private aiot devices: Federated direct neural architecture search,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:55:23.462465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.