Paper Citation Record · LEDGER

NNGPT: Rethinking AutoML with Large Language Models

As of 6 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 4 inbound Pith citation observations for arXiv:2511.20333.

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

pith.paper-citation-record.v1
2511.20333 v1

Coverage vector

measured 76 of 76 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T20:21:11.691407Z

measured 80 of 80 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-10T20:15:59.064352Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T20:17:33.764539Z

Reference resolution

76 of 76 outbound references displayed

verified exact0
verified fuzzy0
unresolved76
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a51cd810-0bc8-460b-8983-af9e9b94c1d3 · outbound

This paper cites Efficient bayesian learning curve extrapo- lation using prior-data fitted networks.Advances in Neural Information Processing Systems, 36:19858–19886, 2023.

NNGPT: Rethinking AutoML with Large Language Models Efficient bayesian learning curve extrapo- lation using prior-data fitted networks.Advances in Neural Information Processing Systems, 36:19858–19886, 2023

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:01.603382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84d26b5c-5365-431f-97a1-a1a980fdabc1 · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework.

NNGPT: Rethinking AutoML with Large Language Models Optuna: A next-generation hyperparameter optimization framework

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:01.687375Z

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Observation f133707a-00ac-47e6-bba7-4dcaf21f42cf · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework.

NNGPT: Rethinking AutoML with Large Language Models Optuna: A next-generation hyperparameter optimization framework

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:01.789283Z

Source-reported events for the cited work

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Observation 36353b2d-6f31-45ef-8105-456a835ff495 · outbound

This paper cites Mixture cure semiparametric additive hazard models under partly interval censoring -- a penalized likelihood approach.

NNGPT: Rethinking AutoML with Large Language Models Mixture cure semiparametric additive hazard models under partly interval censoring -- a penalized likelihood approach

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:01.899498Z

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Observation d202a82e-9fec-42f5-9d22-842365891625 · outbound

This paper cites Algorithms for hyper-parameter optimization.

NNGPT: Rethinking AutoML with Large Language Models Algorithms for hyper-parameter optimization

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:01.980602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d39c01bd-81d4-4faa-845c-3a65d5184b79 · outbound

This paper cites Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms.SciPy, 13: 20, 2013.

NNGPT: Rethinking AutoML with Large Language Models Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms.SciPy, 13: 20, 2013

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:02.095647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64c65f96-2a79-416b-8e6f-45f94053276e · outbound

This paper cites EvoPrompting: Language Models for Code-Level Neural Architecture Search.

NNGPT: Rethinking AutoML with Large Language Models EvoPrompting: Language Models for Code-Level Neural Architecture Search

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:02.187739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a018110-2765-4cae-84c3-1d7f9c2bf0c2 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

NNGPT: Rethinking AutoML with Large Language Models Evaluating Large Language Models Trained on Code

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:02.315634Z

Source-reported events for the cited work

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Observation 665d3431-9832-4030-a41c-dbae72971057 · outbound

This paper cites Stabilizing differen- tiable architecture search via perturbation-based regulariza- tion.

NNGPT: Rethinking AutoML with Large Language Models Stabilizing differen- tiable architecture search via perturbation-based regulariza- tion

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:02.399893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f4b7037-0f7f-4496-9a71-83bed8aa50e6 · outbound

This paper cites an unresolved cited work.

NNGPT: Rethinking AutoML with Large Language Models Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:02.513460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af57fe3c-ac18-42a7-8318-36877def1bf2 · outbound

This paper cites DeepSeek-V3 technical report, 2025.

NNGPT: Rethinking AutoML with Large Language Models DeepSeek-V3 technical report, 2025

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:02.627577Z

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Observation 15de391f-6404-4306-ae1d-d888425c93a2 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

NNGPT: Rethinking AutoML with Large Language Models Imagenet: A large-scale hierarchical image database

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:02.743634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ada30574-4139-4b38-ab2e-2c2dfb44624c · outbound

This paper cites Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation.

NNGPT: Rethinking AutoML with Large Language Models Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:02.803885Z

Source-reported events for the cited work

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Observation 01d19853-3ea6-4d52-a8f6-ae535ae17445 · outbound

This paper cites Speeding up automatic hyperparameter opti- mization of deep neural networks by extrapolation of learn- ing curves.

NNGPT: Rethinking AutoML with Large Language Models Speeding up automatic hyperparameter opti- mization of deep neural networks by extrapolation of learn- ing curves

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:02.913689Z

Source-reported events for the cited work

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Observation 8373d423-abf3-4647-8d05-a8fc07cd98bc · outbound

This paper cites Google vizier: A service for black-box op- timization.

NNGPT: Rethinking AutoML with Large Language Models Google vizier: A service for black-box op- timization

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:03.039821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e055993-5ffe-4200-8fdd-8283e8d3198d · outbound

This paper cites LEMUR Neural Network Dataset: Towards Seamless AutoML, 2025.

NNGPT: Rethinking AutoML with Large Language Models LEMUR Neural Network Dataset: Towards Seamless AutoML, 2025

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:03.159622Z

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Unavailable: canonical work link unavailable.

Observation 4fa50c3e-688b-4b93-86e8-be1299dd37b9 · outbound

This paper cites an unresolved cited work.

NNGPT: Rethinking AutoML with Large Language Models Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:03.275795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bee5dd62-bbff-405e-a947-946f4711b243 · outbound

This paper cites DeepSeek-Coder: When the large language model meets programming - the rise of code intelligence, 2024.

NNGPT: Rethinking AutoML with Large Language Models DeepSeek-Coder: When the large language model meets programming - the rise of code intelligence, 2024

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:03.343050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecdc0632-8ae5-4962-9af5-340b0e3c82e2 · outbound

This paper cites Lvis: A dataset for large vocabulary instance segmentation, 2019.

NNGPT: Rethinking AutoML with Large Language Models Lvis: A dataset for large vocabulary instance segmentation, 2019

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:03.501644Z

Source-reported events for the cited work

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Observation aee0abe8-066b-438b-8a58-f7b6a005a41d · outbound

This paper cites Designing Network Algorithms via Large Language Models.

NNGPT: Rethinking AutoML with Large Language Models Designing Network Algorithms via Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:03.617029Z

Source-reported events for the cited work

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Observation 0d666a16-5a15-4822-87ad-9f49ccd5e5aa · outbound

This paper cites Imagenette: A smaller subset of ImageNet for quick experimentation.https://github.com/ fastai/imagenette, 2019.

NNGPT: Rethinking AutoML with Large Language Models Imagenette: A smaller subset of ImageNet for quick experimentation.https://github.com/ fastai/imagenette, 2019

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:03.727712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f29dbf3d-c1b7-4dc7-bf1d-21f0be702b9a · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

NNGPT: Rethinking AutoML with Large Language Models Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:03.906698Z

Source-reported events for the cited work

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Observation 92d5d695-ecf9-4b1d-8104-cdf4a6327d44 · outbound

This paper cites LoRA: Low-rank adaptation of large language mod- els.

NNGPT: Rethinking AutoML with Large Language Models LoRA: Low-rank adaptation of large language mod- els

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:04.098618Z

Source-reported events for the cited work

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Observation b0bf084c-30dd-4278-82dc-2aa7dcdc58d1 · outbound

This paper cites Auto-keras: An efficient neural architecture search system.

NNGPT: Rethinking AutoML with Large Language Models Auto-keras: An efficient neural architecture search system

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:04.241775Z

Source-reported events for the cited work

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Observation 02a45444-639c-41a9-b9cd-35155403dcba · outbound

This paper cites Optuna vs Code Llama: Are LLMs a New Paradigm for Hyperparameter Tuning?, 2025.

NNGPT: Rethinking AutoML with Large Language Models Optuna vs Code Llama: Are LLMs a New Paradigm for Hyperparameter Tuning?, 2025

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:04.388135Z

Source-reported events for the cited work

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Observation 804f740e-eff9-4633-84a7-1742e0f7ec04 · outbound

This paper cites Cifar-10 (canadian institute for advanced research).

NNGPT: Rethinking AutoML with Large Language Models Cifar-10 (canadian institute for advanced research)

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:04.492550Z

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Observation 3e4e8bdc-3b3e-4c34-86c0-f31601ad36a6 · outbound

This paper cites an unresolved cited work.

NNGPT: Rethinking AutoML with Large Language Models Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:04.576519Z

Source-reported events for the cited work

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Observation 5f66c448-9507-4492-8310-2bf427d7b948 · outbound

This paper cites Gradient-based learning applied to document recog- nition.Proceedings of the IEEE, 86(11):2278–2324, 1998.

NNGPT: Rethinking AutoML with Large Language Models Gradient-based learning applied to document recog- nition.Proceedings of the IEEE, 86(11):2278–2324, 1998

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:04.698657Z

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Observation b235a2e0-6a3d-48b6-aae2-8cd1d5565657 · outbound

This paper cites Competition-level code generation with alphacode.

NNGPT: Rethinking AutoML with Large Language Models Competition-level code generation with alphacode

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:04.837442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36260c24-e7d8-47e0-9f55-61c8205b4f9c · outbound

This paper cites Libcst: Concrete syntax tree parser and toolkit for python.https : / / libcst.

NNGPT: Rethinking AutoML with Large Language Models Libcst: Concrete syntax tree parser and toolkit for python.https : / / libcst

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:05.071234Z

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Observation bb4d719d-8e01-43c3-9a4f-9f5bf99151ef · outbound

This paper cites Lawrence Zitnick, and Piotr Doll ´ar.

NNGPT: Rethinking AutoML with Large Language Models Lawrence Zitnick, and Piotr Doll ´ar

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:05.270084Z

Source-reported events for the cited work

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Observation bd3dc8cd-35bc-4ab9-8715-8c24dd08f56e · outbound

This paper cites DARTS: Differentiable Architecture Search.

NNGPT: Rethinking AutoML with Large Language Models DARTS: Differentiable Architecture Search

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:05.449598Z

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Observation 90060bdb-4520-4b29-aac9-a8095b2a8443 · outbound

This paper cites Deep learning face attributes in the wild.

NNGPT: Rethinking AutoML with Large Language Models Deep learning face attributes in the wild

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:05.651241Z

Source-reported events for the cited work

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Observation 0382ec5b-4f81-4c66-b25d-0ec8a1b24e63 · outbound

This paper cites Large language models to enhance bayesian opti- mization.

NNGPT: Rethinking AutoML with Large Language Models Large language models to enhance bayesian opti- mization

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:05.757772Z

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Observation ad3b6cab-cacf-40b7-8128-b989c3fb1f06 · outbound

This paper cites Large language models to enhance bayesian opti- mization, 2024.

NNGPT: Rethinking AutoML with Large Language Models Large language models to enhance bayesian opti- mization, 2024

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:05.871564Z

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Observation 4d596f77-3215-4795-9c2d-60f88479367b · outbound

This paper cites an unresolved cited work.

NNGPT: Rethinking AutoML with Large Language Models Unresolved cited work

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:05.963691Z

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Observation 2b97e67d-263b-4a93-ba17-47ef271785e3 · outbound

This paper cites Msr-darts: Minimum stable rank of dif- ferentiable architecture search.

NNGPT: Rethinking AutoML with Large Language Models Msr-darts: Minimum stable rank of dif- ferentiable architecture search

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:06.069137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b56956c-c409-4dda-841c-7ebe41c20e16 · outbound

This paper cites ”torchVision: Py- Torch’s computer vision library”, ”2016”.

NNGPT: Rethinking AutoML with Large Language Models ”torchVision: Py- Torch’s computer vision library”, ”2016”

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:06.225613Z

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Observation d968442c-94ea-4a85-9e9a-7895d5301339 · outbound

This paper cites Peft: State-of-the-art parameter- efficient fine-tuning methods.https://github.com/ huggingface/peft, 2022.

NNGPT: Rethinking AutoML with Large Language Models Peft: State-of-the-art parameter- efficient fine-tuning methods.https://github.com/ huggingface/peft, 2022

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:06.390760Z

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Observation 09ac3c45-322d-44c4-87f3-dacc95dca93d · outbound

This paper cites Llmatic: Neural architecture search via large language models and quality-diversity optimization.

NNGPT: Rethinking AutoML with Large Language Models Llmatic: Neural architecture search via large language models and quality-diversity optimization

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:06.566790Z

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Observation 1fc5d4c3-61d2-482b-be47-60d505d25a01 · outbound

This paper cites an unresolved cited work.

NNGPT: Rethinking AutoML with Large Language Models Unresolved cited work

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:06.660549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 342a0a8f-8798-4607-8217-c5b4bf550bb6 · outbound

This paper cites GPT-4 technical report, 2024.

NNGPT: Rethinking AutoML with Large Language Models GPT-4 technical report, 2024

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:06.821302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0069129-635f-429a-919d-26d9ca0c8444 · outbound

This paper cites Openmmlab: Open-source com- puter vision toolbox ecosystem.https://openmmlab.

NNGPT: Rethinking AutoML with Large Language Models Openmmlab: Open-source com- puter vision toolbox ecosystem.https://openmmlab

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:06.926821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 297b188a-5e2a-461e-a2e8-4b834e070312 · outbound

This paper cites PyTorch: An imperative style, high- performance deep learning library, 2019.

NNGPT: Rethinking AutoML with Large Language Models PyTorch: An imperative style, high- performance deep learning library, 2019

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:07.099400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7632dc75-301e-4b85-91a6-a8c023b714e6 · outbound

This paper cites The impact of AI on developer productivity: Ev- idence from GitHub Copilot, 2023.

NNGPT: Rethinking AutoML with Large Language Models The impact of AI on developer productivity: Ev- idence from GitHub Copilot, 2023

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:07.268916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50780231-3b0f-4a47-bfb1-6bbc4407fd29 · outbound

This paper cites Efficient neural architecture search via parame- ters sharing.

NNGPT: Rethinking AutoML with Large Language Models Efficient neural architecture search via parame- ters sharing

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:07.440315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1aeb003f-53de-4aa5-8672-844ab36c4024 · outbound

This paper cites Code Llama: Open Foundation Models for Code, 2024.

NNGPT: Rethinking AutoML with Large Language Models Code Llama: Open Foundation Models for Code, 2024

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:07.610763Z

Source-reported events for the cited work

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Observation 1d1c7496-56ca-4ab1-8ce4-19c068e25d5a · outbound

This paper cites Code Llama: Open Foundation Models for Code.

NNGPT: Rethinking AutoML with Large Language Models Code Llama: Open Foundation Models for Code

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:07.781631Z

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Observation df96b19a-a8bf-4747-8e5f-a8072c96e9ea · outbound

This paper cites an unresolved cited work.

NNGPT: Rethinking AutoML with Large Language Models Unresolved cited work

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:07.977899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1aa4da8-e7c8-4422-b9e9-0d9101ca8213 · outbound

This paper cites Self-Programming Artificial Intelligence Using Code-Generating Language Models.

NNGPT: Rethinking AutoML with Large Language Models Self-Programming Artificial Intelligence Using Code-Generating Language Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:08.200185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac956c22-3b2c-42b1-995a-b3f1bb73daf4 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

NNGPT: Rethinking AutoML with Large Language Models Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:08.444334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dedb6a9-bb8f-486a-9e70-550c674e4b87 · outbound

This paper cites Grammar prompting for domain-specific lan- guage generation with large language models.

NNGPT: Rethinking AutoML with Large Language Models Grammar prompting for domain-specific lan- guage generation with large language models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:08.608095Z

Source-reported events for the cited work

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Observation b87e18f8-a022-4cfb-9b6a-51693d2ab274 · outbound

This paper cites How powerful are performance predictors in neural architecture search? InNeurIPS, pages 28454–28469,.

NNGPT: Rethinking AutoML with Large Language Models How powerful are performance predictors in neural architecture search? InNeurIPS, pages 28454–28469,

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Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:08.835478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c02213c-6d65-46ed-8f93-1f7431977bc4 · outbound

This paper cites Pytorch image models (timm).https: / / github.

NNGPT: Rethinking AutoML with Large Language Models Pytorch image models (timm).https: / / github

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:08.990955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1800809-5ea0-491e-b115-82b123882bb5 · outbound

This paper cites an unresolved cited work.

NNGPT: Rethinking AutoML with Large Language Models Unresolved cited work

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:09.144105Z

Source-reported events for the cited work

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Observation 29a9bb31-6e0e-460e-b678-f53181c73c0a · outbound

This paper cites ClusT3: Information Invariant Test-Time Training.

NNGPT: Rethinking AutoML with Large Language Models ClusT3: Information Invariant Test-Time Training

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:09.317478Z

Source-reported events for the cited work

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Observation 46be1df0-7d39-4ae5-8a03-d19246fe3b1f · outbound

This paper cites Cyclic differentiable architecture search.

NNGPT: Rethinking AutoML with Large Language Models Cyclic differentiable architecture search

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-03T20:21:09.454980Z

Source-reported events for the cited work

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This paper cites On The Suitability of Differential Dataflow For Datalog Interpretation In Highly Dynamic Settings.

NNGPT: Rethinking AutoML with Large Language Models On The Suitability of Differential Dataflow For Datalog Interpretation In Highly Dynamic Settings

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This paper cites Places: A 10 million image database for scene recognition.IEEE Transactions on Pattern Anal- ysis and Machine Intelligence, 40(6):1452–1464, 2018.

NNGPT: Rethinking AutoML with Large Language Models Places: A 10 million image database for scene recognition.IEEE Transactions on Pattern Anal- ysis and Machine Intelligence, 40(6):1452–1464, 2018

Reference 59

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NNGPT: Rethinking AutoML with Large Language Models It also shows typical failure modes (e.g., schema violations, miss- ing functions) and how NNGPT surfaces them through the validator and executor stack

Reference 60

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NNGPT: Rethinking AutoML with Large Language Models Below we review key methods from both domains, focusing on the limitations ad- dressed by NNGPT

Reference 61

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NNGPT: Rethinking AutoML with Large Language Models It requires Python 3.10 or higher and CUDA for GPU-based training

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Reference 63

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Reference 64

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Reference 66

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NNGPT: Rethinking AutoML with Large Language Models Here is the input code for you to modify: ’’’\n{nn_code}\n’’’ Listing 1

Reference 68

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Reference 69

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Reference 70

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Reference 71

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Reference 72

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Reference 73

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Reference 74

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Reference 75

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Reference 76

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Pith citing papers

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Preparation of Fractal-Inspired Computational Architectures for Advanced Large Language Model Analysis cites this paper.

Preparation of Fractal-Inspired Computational Architectures for Advanced Large Language Model Analysis NNGPT: Rethinking AutoML with Large Language Models

Reference 11

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Preparation of Fractal-Inspired Computational Architectures for Advanced Large Language Model Analysis cites this paper.

Preparation of Fractal-Inspired Computational Architectures for Advanced Large Language Model Analysis NNGPT: Rethinking AutoML with Large Language Models

Reference 11

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Observation 26459452-cf88-4e39-a0bf-306bfcd1dda5 · inbound

Enhancing LLM-Based Neural Network Generation: Few-Shot Prompting and Efficient Validation for Automated Architecture Design cites this paper.

Enhancing LLM-Based Neural Network Generation: Few-Shot Prompting and Efficient Validation for Automated Architecture Design NNGPT: Rethinking AutoML with Large Language Models

Reference 13

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Observation 4d27a94f-265d-4f94-9c62-445443c8b3b1 · inbound

LEMUR 2: Unlocking Neural Network Diversity for AI cites this paper.

LEMUR 2: Unlocking Neural Network Diversity for AI NNGPT: Rethinking AutoML with Large Language Models

Reference 11

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