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Paper Citation Record · LEDGER

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection

As of 20 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2512.12281.

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

pith.paper-citation-record.v1
2512.12281 v2

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T16:45:52.826956Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T18:50:20.111356Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

32 of 32 outbound references displayed

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External citation measurements

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arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 580d9c58-2a3e-4df4-83c7-038169cae835 · outbound

This paper cites Super-gradients, 2021.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Super-gradients, 2021

Reference 1

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Observation 1eca620d-9dd7-4885-9b6f-48144b419712 · outbound

This paper cites Qwen2.5- vl technical report, 2025.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Qwen2.5- vl technical report, 2025

Reference 2

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Observation 8b13885c-3f2d-48cb-ba4b-e0e7248f8c91 · outbound

This paper cites Yolov4: Optimal Speed and Accuracy of Object Detec- tion, 2020.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Yolov4: Optimal Speed and Accuracy of Object Detec- tion, 2020

Reference 3

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Observation 7514431d-0efa-4a24-bca6-aa1bb5519196 · outbound

This paper cites Language models are few- shot learners.Advances in Neural Information Processing Systems, 33:1877–1901.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Language models are few- shot learners.Advances in Neural Information Processing Systems, 33:1877–1901

Reference 4

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source=pdf_text observed=2026-08-03T16:45:50.771004Z digest=sha256:f4b70f747fc006b8dea66366c01aef4be57bdd3d6f14bfb56d5017ddfa329777

Observation 076e605e-47c2-404a-b498-44080d2f27a6 · outbound

This paper cites End-to- end object detection with transformers, 2020.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection End-to- end object detection with transformers, 2020

Reference 5

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source=pdf_text observed=2026-08-03T16:45:50.878320Z digest=sha256:acd1dee1802865c302d3a8bfe3ea6181052e2f6ab1041fa67cd157c889c8ab0c

Observation b47ef315-0390-4ef4-9dd0-6494e9f98633 · outbound

This paper cites Yolo-world: Real-time open- vocabulary object detection.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Yolo-world: Real-time open- vocabulary object detection

Reference 6

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Observation 254cc10e-ca93-43e5-b798-5be4d2516f97 · outbound

This paper cites Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui Qu, J.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui Qu, J

Reference 7

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Observation 5a7ed712-7b48-400c-a811-c9ea0450054c · outbound

This paper cites an unresolved cited work.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Unresolved cited work

Reference 8

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Observation 0a00c357-a10a-4374-a748-68f30abaaa9c · outbound

This paper cites Bert: Pre-training of deep bidirectional transform- ers for language understanding, 2018.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Bert: Pre-training of deep bidirectional transform- ers for language understanding, 2018

Reference 9

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Observation f3bcaa9e-6429-4fa7-9498-c83798a183f8 · outbound

This paper cites LPZero: Language model zero-cost proxy search from zero.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection LPZero: Language model zero-cost proxy search from zero

Reference 10

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Observation d16a2a0d-b729-44f5-801d-8ec10ea768fa · outbound

This paper cites Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Reference 11

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Observation aaaf6e43-544a-4281-b751-54ff7609f5b7 · outbound

This paper cites an unresolved cited work.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Unresolved cited work

Reference 12

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Observation b580cc1f-0ef5-4f39-923e-98f5479c2a01 · outbound

This paper cites Rae, Oriol Vinyals, and Laurent Sifre.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Rae, Oriol Vinyals, and Laurent Sifre

Reference 13

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Observation bf56f0bd-a43b-4fbd-b809-8d6aa143c1ec · outbound

This paper cites Deim: Detr with improved match- ing for fast convergence.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Deim: Detr with improved match- ing for fast convergence

Reference 14

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source=pdf_text observed=2026-08-03T16:45:51.553116Z digest=sha256:38ef6568325e44ba5688bcd4dec7b8cac543265da997e365e434796c6969f402

Observation 039de6ef-0893-497c-96ac-7539cbca7808 · outbound

This paper cites LLM Performance Predictors are good initializers for Architecture Search.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection LLM Performance Predictors are good initializers for Architecture Search

Reference 15

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source=pdf_text observed=2026-08-03T16:45:51.652442Z digest=sha256:6a59745a8d5d78d82800c83e6a98e088660aaa111f715d708e735976bc1e84a1

Observation 086fc1ba-5bf6-40ab-be38-7abd8dfbb87a · outbound

This paper cites RZ-NAS: Enhancing LLM-guided neural architecture search via reflective zero-cost strategy.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection RZ-NAS: Enhancing LLM-guided neural architecture search via reflective zero-cost strategy

Reference 16

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Observation 9a59e690-f836-4759-9b90-0bda4c7514a1 · outbound

This paper cites Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei

Reference 17

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Observation 1275a269-d61d-425d-a3fa-520e3ce42365 · outbound

This paper cites Retrieval-augmented generation for knowledge- intensive nlp tasks.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Retrieval-augmented generation for knowledge- intensive nlp tasks

Reference 18

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Observation 7ca62d9f-8afb-4d9a-a92a-ad3d0e568d11 · outbound

This paper cites Zen-nas: A zero-shot nas for high-performance image recognition.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Zen-nas: A zero-shot nas for high-performance image recognition

Reference 19

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Observation 119eb1ea-3e65-40c5-b409-385b0d27a6f8 · outbound

This paper cites an unresolved cited work.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Unresolved cited work

Reference 20

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Observation e12ae9e0-e4e5-484c-b3b9-71e99964f10f · outbound

This paper cites ChatGPT can now see, hear, and speak, 2023.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection ChatGPT can now see, hear, and speak, 2023

Reference 21

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Observation 4af4e2c7-7aec-457e-88f2-d595d6b81fbf · outbound

This paper cites GPT-4 Technical Report.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection GPT-4 Technical Report

Reference 22

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Observation 369e5de7-41d7-41e6-b08b-7bc991bf0347 · outbound

This paper cites GPT-4V(ision) System Card.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection GPT-4V(ision) System Card

Reference 23

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Observation ad67dc55-31a2-44f4-a54b-12febe54863b · outbound

This paper cites Language models are unsuper- vised multitask learners.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Language models are unsuper- vised multitask learners

Reference 24

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Observation 1d073ea9-9e30-4ef7-b119-b62857eea9d7 · outbound

This paper cites You only look once: Unified, real-time object detec- tion, 2015.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection You only look once: Unified, real-time object detec- tion, 2015

Reference 25

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Observation 0fafa5d7-834a-494f-8d4a-efb31cac330b · outbound

This paper cites Agentic retrieval-augmented generation: A survey on agentic rag, 2025.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Agentic retrieval-augmented generation: A survey on agentic rag, 2025

Reference 26

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source=pdf_text observed=2026-08-03T16:45:52.451496Z digest=sha256:c4f0f1488980508c380266f8f7fe3be030c7a9e26be5515c9d8eb997852e3546

Observation 2a5f9b84-0c3b-4fb6-8542-84cc33e5fd9d · outbound

This paper cites Llama: Open and Efficient Foundation Language Models, 2023.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Llama: Open and Efficient Foundation Language Models, 2023

Reference 27

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Observation acbde76f-d552-4182-b21c-2f7003f811cc · outbound

This paper cites Github - ultralytics/yolov5: Yolov5 in PyTorch > ONNX > CoreML > TFLite,.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Github - ultralytics/yolov5: Yolov5 in PyTorch > ONNX > CoreML > TFLite,

Reference 28

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Observation 3a3f4ea1-cf52-48d3-bf5d-134dcf712dc2 · outbound

This paper cites Github - ultralytics/ultralytics: Ultralytics YOLO,.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Github - ultralytics/ultralytics: Ultralytics YOLO,

Reference 29

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Observation ca31d765-3514-4919-8899-9a337dc5b3c4 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large lan- guage models, 2022.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection Chain-of-thought prompting elicits reasoning in large lan- guage models, 2022

Reference 30

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source=pdf_text observed=2026-08-03T16:45:52.742634Z digest=sha256:b6d3ef8d08160a65cd1780e93cb25c541d81adc06d39cebcf830f8f44e037e28

Observation fba5528f-9a3f-4833-bd9e-2b526b479206 · outbound

This paper cites React: Synergizing reasoning and acting in language models, 2023.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection React: Synergizing reasoning and acting in language models, 2023

Reference 31

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Observation bf4cc6a6-0880-40ef-9692-0d3f9515c16c · outbound

This paper cites LPZero: Language Model Zero-cost Proxy Search from Zero.

Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection LPZero: Language Model Zero-cost Proxy Search from Zero

Reference 2024

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Pith citing papers

Observation 2f8a81f3-cb85-4131-beab-21f47c5d27b9 · inbound

A Hybrid Vision-Language Architecture for Automated Defect Reasoning and Report Generation in Industrial Inspection cites this paper.

A Hybrid Vision-Language Architecture for Automated Defect Reasoning and Report Generation in Industrial Inspection Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection

Reference 31

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arxiv_id, observed 2026-07-21T02:20:33.181213Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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