Paper Citation Record · LEDGER

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning

As of 9 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2506.07735.

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

pith.paper-citation-record.v1
2506.07735 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:32:13.212850Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

42 of 42 outbound references displayed

verified exact0
verified fuzzy24
unresolved18
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c070d907-d9fd-4315-9f45-4d32c7c8fa4e · outbound

This paper cites {TVM}: An automated {End-to-End} optimizing compiler for deep learning.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning {TVM}: An automated {End-to-End} optimizing compiler for deep learning

Reference 1

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Observation ee99d48b-dc65-434c-97cc-3c474174212b · outbound

This paper cites Brp-nas: Prediction-based nas using gcns.Advances in neural information processing systems, 33:10480–10490, 2020.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Brp-nas: Prediction-based nas using gcns.Advances in neural information processing systems, 33:10480–10490, 2020

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.839482Z

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Observation c2622703-0523-45ee-b864-391cbf19c364 · outbound

This paper cites Nn-meter: Towards accurate latency prediction of deep-learning model inference on diverse edge devices.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Nn-meter: Towards accurate latency prediction of deep-learning model inference on diverse edge devices

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.825013Z

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Observation 574fd083-82fe-4e87-8c1b-1c10328120a0 · outbound

This paper cites Nnlqp: A multi- platform neural network latency query and prediction system with an evolving database.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Nnlqp: A multi- platform neural network latency query and prediction system with an evolving database

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.809554Z

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Observation 033817f3-fca5-42ce-bcc3-27618ab286bc · outbound

This paper cites Tfx: A tensorflow-based production-scale machine learning platform.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Tfx: A tensorflow-based production-scale machine learning platform

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.792321Z

Source-reported events for the cited work

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Observation b6491e81-3461-48ae-8e49-14df2e84ac91 · outbound

This paper cites Neural predictor for neural architecture search.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Neural predictor for neural architecture search

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.776558Z

Source-reported events for the cited work

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Observation 5fee505a-df72-481a-bd9f-39bd588b9044 · outbound

This paper cites A generic graph-based neural architecture encoding scheme for predictor-based nas.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning A generic graph-based neural architecture encoding scheme for predictor-based nas

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.760455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0a19833d-a559-489f-8c33-3e8c8ad8d02b · outbound

This paper cites Nar-former: Neural architecture representation learning towards holistic attributes prediction.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Nar-former: Neural architecture representation learning towards holistic attributes prediction

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.742857Z

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Observation 4a2dac7c-57d3-46a2-b96a-9506354972c7 · outbound

This paper cites Nar-former v2: Re- thinking transformer for universal neural network representation learning.Advances in Neural Information Processing Systems, 36:62727–62739, 2023.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Nar-former v2: Re- thinking transformer for universal neural network representation learning.Advances in Neural Information Processing Systems, 36:62727–62739, 2023

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.728185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3cb7e9f7-6531-49c2-9070-d0d873abe004 · outbound

This paper cites Neural architecture optimiza- tion.Advances in neural information processing systems, 31, 2018.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Neural architecture optimiza- tion.Advances in neural information processing systems, 31, 2018

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.713358Z

Source-reported events for the cited work

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

This paper cites Once-for-All: Train One Network and Specialize it for Efficient Deployment.

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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Source-reported events for the cited work

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Observation e109a24d-97e0-4b74-b547-a53f7146a448 · outbound

This paper cites Semi-supervised neural architecture search.Advances in Neural Information Processing Systems, 33:10547– 10557, 2020.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Semi-supervised neural architecture search.Advances in Neural Information Processing Systems, 33:10547– 10557, 2020

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.698182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e180ec59-b473-4c65-b0f0-6c24da1a07f2 · outbound

This paper cites Renas: Relativistic evaluation of neural architecture search.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Renas: Relativistic evaluation of neural architecture search

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.682737Z

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Observation edfa82a4-add4-4585-9d30-1031eefeaf85 · outbound

This paper cites Contrastive neural architecture search with neural architecture comparators.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Contrastive neural architecture search with neural architecture comparators

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.668583Z

Source-reported events for the cited work

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Observation 8d20884e-16e5-4f7e-80a4-48fb05960067 · outbound

This paper cites Nas-bench-graph: Benchmarking graph neural architecture search.Advances in neural information processing systems, 35:54–69, 2022.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Nas-bench-graph: Benchmarking graph neural architecture search.Advances in neural information processing systems, 35:54–69, 2022

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.653739Z

Source-reported events for the cited work

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Observation baf9b0c7-f645-40c7-88a2-91000ea25e56 · outbound

This paper cites ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

Reference 16

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Source-reported events for the cited work

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Observation f4b6f368-1574-40ac-b5cf-e3a5fc577043 · outbound

This paper cites Understanding and Robustifying Differentiable Architecture Search.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Understanding and Robustifying Differentiable Architecture Search

Reference 17

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Observation 09b2994a-440f-4d14-b284-5d22131781dc · outbound

This paper cites Neural graph embedding for neural architecture search.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Neural graph embedding for neural architecture search

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.638822Z

Source-reported events for the cited work

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Observation 4d0472be-618a-4458-bdd5-847415f902c5 · outbound

This paper cites Pace: A parallelizable computation encoder for directed acyclic graphs.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Pace: A parallelizable computation encoder for directed acyclic graphs

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.623711Z

Source-reported events for the cited work

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Observation fa9724a0-e5ee-4167-aa3a-2305bec675e4 · outbound

This paper cites Transformers over directed acyclic graphs.Advances in Neural Information Processing Systems, 36:47764–47782, 2023.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Transformers over directed acyclic graphs.Advances in Neural Information Processing Systems, 36:47764–47782, 2023

Reference 20

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Source-reported events for the cited work

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Observation b1a205f9-8642-4373-b4dd-21e5a0dbf566 · outbound

This paper cites Peephole: Predicting Network Performance Before Training.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Peephole: Predicting Network Performance Before Training

Reference 21

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Source-reported events for the cited work

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Observation 52e93b8a-0f84-4083-a3ea-310c4d666a5a · outbound

This paper cites Bananas: Bayesian optimization with neural architectures for neural architecture search.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Bananas: Bayesian optimization with neural architectures for neural architecture search

Reference 22

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Observation 7569dba4-a5c0-4c3e-870f-846dc7230c75 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Semi-Supervised Classification with Graph Convolutional Networks

Reference 23

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Observation 6c2c735f-c7db-4757-a705-5d26e2551944 · outbound

This paper cites Graph Attention Networks.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Graph Attention Networks

Reference 24

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Observation e4d33d32-5a5d-407e-bd72-e9b814ea0e8a · outbound

This paper cites Tnasp: A transformer-based nas predictor with a self-evolution framework.Advances in Neural Information Processing Systems, 34:15125–15137, 2021.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Tnasp: A transformer-based nas predictor with a self-evolution framework.Advances in Neural Information Processing Systems, 34:15125–15137, 2021

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.588548Z

Source-reported events for the cited work

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Observation 05f4a6d4-0cdd-4e3b-87f9-9739c2f19946 · outbound

This paper cites Nas-bench-101: Towards reproducible neural architecture search.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Nas-bench-101: Towards reproducible neural architecture search

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.573695Z

Source-reported events for the cited work

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Observation 13f670aa-d25b-4ca9-9950-2da5ea26d1b4 · outbound

This paper cites NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search

Reference 27

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Observation cbbe5566-4e15-4d48-b668-c9cef32917ed · outbound

This paper cites Do transformers really perform badly for graph representation?Advances in neural information processing systems, 34:28877–28888, 2021.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Do transformers really perform badly for graph representation?Advances in neural information processing systems, 34:28877–28888, 2021

Reference 28

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Observation c963bfd8-14b9-4a81-8f5a-2f639606aa9a · outbound

This paper cites Representing long-range context for graph neural networks with global attention.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Representing long-range context for graph neural networks with global attention

Reference 29

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This paper cites A neural architecture predictor based on gnn- enhanced transformer.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning A neural architecture predictor based on gnn- enhanced transformer

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.538988Z

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Observation bfc5a3e1-99d3-408f-811a-ee6425b9d787 · outbound

This paper cites Nn-former: Rethinking graph structure in neural architecture representation.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Nn-former: Rethinking graph structure in neural architecture representation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.523435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4fc38c9f-d2e8-4c12-857c-2d3bb6197b79 · outbound

This paper cites Does unsupervised architecture representation learning help neural architecture search?Advances in neural information processing systems, 33:12486–12498, 2020.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Does unsupervised architecture representation learning help neural architecture search?Advances in neural information processing systems, 33:12486–12498, 2020

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.508053Z

Source-reported events for the cited work

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Observation b39bec0e-8c2f-42ca-92ac-55c66e1a4442 · outbound

This paper cites Ta-gates: an encoding scheme for neural network architectures.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Ta-gates: an encoding scheme for neural network architectures

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.491507Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 33d8ada3-4d3b-49b5-a0b3-e2f18d6391d7 · outbound

This paper cites The graph neural network model.IEEE transactions on neural networks, 20(1):61–80, 2008.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning The graph neural network model.IEEE transactions on neural networks, 20(1):61–80, 2008

Reference 34

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Observation 73298f84-ebb4-41b3-9fb7-f076ba593c63 · outbound

This paper cites Representation Learning on Graphs: Methods and Applications.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Representation Learning on Graphs: Methods and Applications

Reference 35

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Source-reported events for the cited work

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Observation dd7e84e7-ddef-4202-9489-016dcb75116d · outbound

This paper cites How Powerful are Graph Neural Networks?.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning How Powerful are Graph Neural Networks?

Reference 36

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Source-reported events for the cited work

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Observation 124dd30f-446d-4c62-b547-db619b87d7c9 · outbound

This paper cites Benchmarking graph neural networks.Journal of Machine Learning Research, 24(43):1–48, 2023.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Benchmarking graph neural networks.Journal of Machine Learning Research, 24(43):1–48, 2023

Reference 37

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Source-reported events for the cited work

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Observation 7747e900-a8c7-4dc6-9414-593e6795d7fd · outbound

This paper cites Neural Architecture Search with Reinforcement Learning.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Neural Architecture Search with Reinforcement Learning

Reference 38

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Source-reported events for the cited work

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Observation 97c6342a-8481-43ed-9198-6e0f8a78209b · outbound

This paper cites DARTS: Differentiable Architecture Search.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning DARTS: Differentiable Architecture Search

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:13.199809Z

Source-reported events for the cited work

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Observation 8cb06ad6-eb90-4b2a-ab14-6e82fb4c3372 · outbound

This paper cites Autogt: Automated graph transformer architecture search.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Autogt: Automated graph transformer architecture search

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.456985Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a86f8449-ce56-42da-ae89-758701ea2257 · outbound

This paper cites A learned performance model for tensor processing units.Proceedings of Machine Learning and Systems, 3:387–400, 2021.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning A learned performance model for tensor processing units.Proceedings of Machine Learning and Systems, 3:387–400, 2021

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.442234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7c493b95-e7f3-45b7-9f8a-56bb14964275 · outbound

This paper cites Pinat: a permutation invariance augmented transformer for nas predictor.

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Pinat: a permutation invariance augmented transformer for nas predictor

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:13.426826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.