Paper Citation Record · LEDGER

The Computational Limits of Deep Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 30 inbound Pith citation observations for arXiv:2007.05558.

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

pith.paper-citation-record.v1
2007.05558 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 30 of 30 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:17:09.819888Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T15:28:33.717389Z

Reference resolution

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External citation measurements

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Outbound references

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Pith citing papers

Observation d0e3a020-507a-4332-8f87-59ab87356012 · inbound

Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models cites this paper.

Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models The Computational Limits of Deep Learning

Reference 216

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T06:38:36.987482Z

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Observation b13a467c-9342-4bdb-9aea-d8d80f2f5fdc · inbound

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices cites this paper.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices The Computational Limits of Deep Learning

Reference 129

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Observation 79bba974-e0e5-431f-b7fd-71a4374c9c40 · inbound

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs cites this paper.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs The Computational Limits of Deep Learning

Reference 33

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Observation d6364922-7944-43b4-8150-93649e230f69 · inbound

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge cites this paper.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge The Computational Limits of Deep Learning

Reference 10

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Observation f7729bdb-4615-4be6-968f-cc40d85ef720 · inbound

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation cites this paper.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation The Computational Limits of Deep Learning

Reference 1

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Observation c8e8b453-f358-4ffa-85dc-21df1caf72b3 · inbound

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness cites this paper.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness The Computational Limits of Deep Learning

Reference 143

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Observation f3f1cd75-366b-479f-8577-9628bfa7e720 · inbound

The Generalist Brain Module: Module Repetition in Neural Networks in Light of the Minicolumn Hypothesis cites this paper.

The Generalist Brain Module: Module Repetition in Neural Networks in Light of the Minicolumn Hypothesis The Computational Limits of Deep Learning

Reference 96

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Observation d88ea417-a9ee-420b-83fd-832e7eb26fff · inbound

From Propagator to Oscillator: The Dual Role of Symmetric Differential Equations in Neural Systems cites this paper.

From Propagator to Oscillator: The Dual Role of Symmetric Differential Equations in Neural Systems The Computational Limits of Deep Learning

Reference 5

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Observation e4d9741d-a37f-4be2-831a-02ed8123e63c · inbound

Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures cites this paper.

Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures The Computational Limits of Deep Learning

Reference 98

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Observation 69aba07d-1ebd-47ff-bc73-3ab3df3f82e4 · inbound

Progressive Depth Up-scaling via Optimal Transport cites this paper.

Progressive Depth Up-scaling via Optimal Transport The Computational Limits of Deep Learning

Reference 2024

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Observation b411fca5-ae1f-4bab-8e99-66b2a2dc322a · inbound

From Membership-Privacy Leakage to Quantum Machine Unlearning cites this paper.

From Membership-Privacy Leakage to Quantum Machine Unlearning The Computational Limits of Deep Learning

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:11:42.865600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6feddc30-0839-4794-8b61-1f0c0c621921 · inbound

Quantum optical neural networks using atom-cavity interactions to provide all-optical nonlinearity cites this paper.

Quantum optical neural networks using atom-cavity interactions to provide all-optical nonlinearity The Computational Limits of Deep Learning

Reference 2

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Observation 23f9bfe9-0cca-4e28-8b9f-c095360349f7 · inbound

A Theoretical Framework for Auxiliary-Loss-Free Load Balancing of Sparse Mixture-of-Experts in Large-Scale AI Models cites this paper.

A Theoretical Framework for Auxiliary-Loss-Free Load Balancing of Sparse Mixture-of-Experts in Large-Scale AI Models The Computational Limits of Deep Learning

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-17T02:18:52.587126Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e4737932-7c50-47e6-87b2-256fd23806ae · inbound

Koopman Model Dimension Reduction via Variational Bayesian Inference and Graph Search cites this paper.

Koopman Model Dimension Reduction via Variational Bayesian Inference and Graph Search The Computational Limits of Deep Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T11:32:57.395221Z

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Observation 14c9143d-e924-42d0-a111-74ec3311827d · inbound

Physical Analogue Kolmogorov-Arnold Networks based on Reconfigurable Nonlinear-Processing Units cites this paper.

Physical Analogue Kolmogorov-Arnold Networks based on Reconfigurable Nonlinear-Processing Units The Computational Limits of Deep Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T03:40:35.559151Z

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Observation 5146e170-c082-46a0-9544-4ae5d04609ff · inbound

Neural Networks With Dense Weights Are Not Universal Approximators cites this paper.

Neural Networks With Dense Weights Are Not Universal Approximators The Computational Limits of Deep Learning

Reference 11

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 75a8d5dd-08d4-4767-a23b-296a37d6806c · inbound

Neural Networks With Dense Weights Are Not Universal Approximators cites this paper.

Neural Networks With Dense Weights Are Not Universal Approximators The Computational Limits of Deep Learning

Reference 11

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Observation cc3268e0-348e-40a1-9839-b318f543baf2 · inbound

STRIDe: Cross-Coupled STT-MRAM Enabling Robust In-Memory-Computing for Deep Neural Network Accelerators cites this paper.

STRIDe: Cross-Coupled STT-MRAM Enabling Robust In-Memory-Computing for Deep Neural Network Accelerators The Computational Limits of Deep Learning

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T22:25:50.296597Z

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Observation ef3341d6-2796-4a11-9266-b402456b10c8 · inbound

Blockchain and AI: Securing Intelligent Networks for the Future cites this paper.

Blockchain and AI: Securing Intelligent Networks for the Future The Computational Limits of Deep Learning

Reference 131

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T23:45:53.578410Z

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Observation dd54ee20-56a2-4a9b-8ee4-7400c5923dbd · inbound

Rates of forgetting for the sequentially Markov coalescent cites this paper.

Rates of forgetting for the sequentially Markov coalescent The Computational Limits of Deep Learning

Reference 152

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T22:54:16.670537Z

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Observation b6a6a210-f62a-49c5-8219-1587f624ff08 · inbound

Mixture of Heterogeneous Grouped Experts for Language Modeling cites this paper.

Mixture of Heterogeneous Grouped Experts for Language Modeling The Computational Limits of Deep Learning

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:36:10.972556Z

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Observation defa1ef1-fb75-426a-ad2b-3ed17c01601b · inbound

Auto-Relational Reasoning cites this paper.

Auto-Relational Reasoning The Computational Limits of Deep Learning

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:31:25.702337Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 31482d30-ab35-42af-aba9-e7f26fa7ef84 · inbound

OptiLookUp: An Optical ROM-Based Lookup Table Engine for Photonic Accelerators cites this paper.

OptiLookUp: An Optical ROM-Based Lookup Table Engine for Photonic Accelerators The Computational Limits of Deep Learning

Reference 2

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 408a4169-80df-4af6-8200-39480f55b93d · inbound

OptiLookUp: An Optical ROM-Based Lookup Table Engine for Photonic Accelerators cites this paper.

OptiLookUp: An Optical ROM-Based Lookup Table Engine for Photonic Accelerators The Computational Limits of Deep Learning

Reference 2

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Observation 2441701d-2c7f-4d98-aa6a-46c32420ff9f · inbound

Position: LLM Inference Should Be Evaluated as Energy-to-Token Production cites this paper.

Position: LLM Inference Should Be Evaluated as Energy-to-Token Production The Computational Limits of Deep Learning

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:27:19.408590Z

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Observation 5f944148-e103-4679-a6e2-1f4cecdb750d · inbound

General-Purpose Photonic Computing Primitive for Contemporary Artificial Intelligence cites this paper.

General-Purpose Photonic Computing Primitive for Contemporary Artificial Intelligence The Computational Limits of Deep Learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:06:38.219710Z

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Observation eb058f75-5394-48d5-8480-2bbf2a6898e1 · inbound

Recursive Block-Diagonal Coupling for Resource-Efficient Training of Vision Models cites this paper.

Recursive Block-Diagonal Coupling for Resource-Efficient Training of Vision Models The Computational Limits of Deep Learning

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-05-25T04:45:20.016621Z

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Observation e51182af-f5da-456d-9833-6836ab5ec566 · inbound

A Functional Data Framework For Analyzing Shapes and Textures in Images cites this paper.

A Functional Data Framework For Analyzing Shapes and Textures in Images The Computational Limits of Deep Learning

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T06:57:43.377663Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4d6c1b03-6e73-4f7b-8439-f55a930e15c2 · inbound

Efficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUs cites this paper.

Efficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUs The Computational Limits of Deep Learning

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T15:28:33.718862Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 238c3819-bd33-48c5-8c67-ca3b5db61beb · inbound

MOSAIC-FL, a micro-service based privacy-preserving framework with application to genomics cites this paper.

MOSAIC-FL, a micro-service based privacy-preserving framework with application to genomics The Computational Limits of Deep Learning

Reference 233

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