Paper Citation Record · LEDGER

One-Time Soft Alignment Enables Resilient Learning without Weight Transport

As of 8 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 1 inbound Pith citation observation for arXiv:2505.20892.

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

pith.paper-citation-record.v1
2505.20892 v1

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:51:08.024625Z

measured 68 of 68 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:48:55.160266Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T05:48:59.352106Z

Reference resolution

67 of 67 outbound references displayed

verified exact1
verified fuzzy38
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4cdc23ff-0304-453d-8f37-32e5c0fcaeef · outbound

This paper cites Rumelhart, Geoffrey E.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Rumelhart, Geoffrey E

Reference 1

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Observation 1877ade1-7ce5-450f-b542-a9420e904aaf · outbound

This paper cites Deep learning.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Deep learning

Reference 2

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Observation 4731c814-070d-4f21-9bfe-2dcdf437552e · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Imagenet classification with deep convolutional neural networks

Reference 3

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Observation 2658b7ce-679d-4c34-afe7-153cd6dd92c3 · outbound

This paper cites Deep residual learning for image recognition.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Deep residual learning for image recognition

Reference 4

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Observation 31fb1bf4-5aea-4bbb-ad07-f18192942140 · outbound

This paper cites GPT-4 Technical Report.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport GPT-4 Technical Report

Reference 5

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Observation 9173ba47-d737-4774-b9ce-e92f09d5b71c · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Gemini: A Family of Highly Capable Multimodal Models

Reference 6

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Observation d653684b-516f-48a9-9fa6-a7a76a114206 · outbound

This paper cites Energy and policy considerations for modern deep learning research.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Energy and policy considerations for modern deep learning research

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:16.628094Z

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Observation eb177090-d3ba-4c7b-9234-5f3254ea756a · outbound

This paper cites Efficient processing of deep neural networks: A tutorial and survey.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Efficient processing of deep neural networks: A tutorial and survey

Reference 8

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Observation 37d62b99-bd52-4855-99f9-d647393a3b42 · outbound

This paper cites Green ai.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Green ai

Reference 9

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Observation b9b1a589-94c4-474b-94a9-96095fce6d0f · outbound

This paper cites Carbon Emissions and Large Neural Network Training.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Carbon Emissions and Large Neural Network Training

Reference 10

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Observation 9e68a750-7275-4bf6-b268-5569ed8d926c · outbound

This paper cites Estimating the carbon footprint of bloom, a 176b parameter language model.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Estimating the carbon footprint of bloom, a 176b parameter language model

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:16.439201Z

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Observation a3f8726e-e29e-411b-abeb-d1a503879685 · outbound

This paper cites Computing’s energy problem (and what we can do about it).

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Computing’s energy problem (and what we can do about it)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:16.289034Z

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Observation 2e665849-79f0-4872-a447-d25a4e973ca8 · outbound

This paper cites Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:16.028206Z

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Observation c6eafa6a-776a-40a2-8d7e-3cc16bae5e50 · outbound

This paper cites On computable numbers, with an application to the entscheidungsproblem.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport On computable numbers, with an application to the entscheidungsproblem

Reference 14

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Observation fe79038d-ef52-46ed-975e-a09362b89880 · outbound

This paper cites First draft of a report on the edvac.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport First draft of a report on the edvac

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:15.830427Z

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Observation 80e5789f-76fa-4913-b7f8-e2a005a49439 · outbound

This paper cites Reconstruction and simulation of neocortical microcircuitry.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Reconstruction and simulation of neocortical microcircuitry

Reference 16

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Observation 9ced11e7-7831-4ce5-9bb6-54bd9404fbe6 · outbound

This paper cites Memory and information processing in neuromorphic systems.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Memory and information processing in neuromorphic systems

Reference 17

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Observation 5c7e3efc-858d-485c-9484-fa2ab1561c72 · outbound

This paper cites A million spiking-neuron integrated circuit with a scalable communication network and interface.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport A million spiking-neuron integrated circuit with a scalable communication network and interface

Reference 18

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Observation fc311a1a-3e1d-4b9d-a2f6-298cd0c00d2c · outbound

This paper cites Distributed hierarchical processing in the primate cerebral cortex.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Distributed hierarchical processing in the primate cerebral cortex

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:15.103440Z

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Observation b5f35e97-4fbe-4719-aa84-ec5c27a7fc57 · outbound

This paper cites Visual areas exert feedforward and feedback influences through distinct frequency channels.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Visual areas exert feedforward and feedback influences through distinct frequency channels

Reference 20

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Observation 157dbabf-31a2-452c-94d9-71fe2239ad53 · outbound

This paper cites Competitive learning: From interactive activation to adaptive resonance.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Competitive learning: From interactive activation to adaptive resonance

Reference 21

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Observation 0bd270e1-f9cf-4a47-b0ec-02b18b2b3381 · outbound

This paper cites The recent excitement about neural networks.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport The recent excitement about neural networks

Reference 22

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Observation 02940621-7362-4414-a8a3-00941291eade · outbound

This paper cites Random synaptic feedback weights support error backpropagation for deep learning.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Random synaptic feedback weights support error backpropagation for deep learning

Reference 23

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Observation ffa4f27a-d60a-4253-b119-78db5e5e5101 · outbound

This paper cites Backpropagation and the brain.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Backpropagation and the brain

Reference 24

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Observation 53049027-8bec-46dd-bf3c-14892a57880b · outbound

This paper cites Dendritic solutions to the credit assignment problem.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Dendritic solutions to the credit assignment problem

Reference 25

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Observation 4fd9bff4-0738-4df5-b258-1eb88d9855d4 · outbound

This paper cites Assessing the scalability of biologically-motivated deep learning algorithms and architectures.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Assessing the scalability of biologically-motivated deep learning algorithms and architectures

Reference 26

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Observation 032e9104-1293-439c-bfe5-630a800dcc23 · outbound

This paper cites How important is weight symmetry in backpropagation? In Proceedings of the AAAI Conference on Artificial Intelligence, volume 30, 2016.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport How important is weight symmetry in backpropagation? In Proceedings of the AAAI Conference on Artificial Intelligence, volume 30, 2016

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:13.828272Z

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Observation f33d0dd0-e7c2-46ef-9848-bbdf9b204659 · outbound

This paper cites Biologically-plausible learning algorithms can scale to large datasets.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Biologically-plausible learning algorithms can scale to large datasets

Reference 28

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Observation 1d93818a-9dd1-4f5f-ac4a-6b8103602a9a · outbound

This paper cites An approximation of the error backpropagation algorithm in a predictive coding network with local hebbian synaptic plasticity.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport An approximation of the error backpropagation algorithm in a predictive coding network with local hebbian synaptic plasticity

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:13.418312Z

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Observation 2c76b77d-7415-4778-97f5-d128c7982046 · outbound

This paper cites Inferring neural activity before plasticity as a foundation for learning beyond backpropagation.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Inferring neural activity before plasticity as a foundation for learning beyond backpropagation

Reference 30

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Observation 5ebd1206-5d19-4616-a121-282d6f246fba · outbound

This paper cites Equilibrium propagation: Bridging the gap between energy-based models and backpropagation.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Equilibrium propagation: Bridging the gap between energy-based models and backpropagation

Reference 31

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This paper cites Backpropagation without weight transport.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Backpropagation without weight transport

Reference 32

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This paper cites Deep learning without weight transport.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Deep learning without weight transport

Reference 33

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Observation 3f472113-de7e-41f6-a547-744879b70743 · outbound

This paper cites Spontaneous impulse activity of rat retinal ganglion cells in prenatal life.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Spontaneous impulse activity of rat retinal ganglion cells in prenatal life

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:12.763206Z

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Observation 59d28d15-4b1a-445d-9bf0-3bb17eccdb02 · outbound

This paper cites Retinal waves coordinate patterned activity throughout the developing visual system.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Retinal waves coordinate patterned activity throughout the developing visual system

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:12.528800Z

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Observation 7890b136-7414-41b8-bfe1-31058d844cfa · outbound

This paper cites Prenatal activity from thalamic neurons governs the emergence of functional cortical maps in mice.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Prenatal activity from thalamic neurons governs the emergence of functional cortical maps in mice

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:12.401318Z

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This paper cites Spontaneous activity in developing thalamic and cortical sensory networks.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Spontaneous activity in developing thalamic and cortical sensory networks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:12.194070Z

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Observation a69d2e64-cc4a-43bc-8260-e20a7c7f3fa7 · outbound

This paper cites Pretraining with random noise for fast and robust learning without weight transport.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Pretraining with random noise for fast and robust learning without weight transport

Reference 38

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Observation ad53f60c-6ef4-4d9b-9396-f946d2416437 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Reading digits in natural images with unsupervised feature learning

Reference 39

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

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Learning multiple layers of features from tiny images

Reference 40

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This paper cites An analysis of single-layer networks in unsupervised feature learning.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport An analysis of single-layer networks in unsupervised feature learning

Reference 41

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One-Time Soft Alignment Enables Resilient Learning without Weight Transport Gradient-based learning applied to document recognition

Reference 42

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One-Time Soft Alignment Enables Resilient Learning without Weight Transport Visualizing the loss landscape of neural nets

Reference 43

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One-Time Soft Alignment Enables Resilient Learning without Weight Transport Pyhessian: Neural networks through the lens of the hessian

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:11.543059Z

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One-Time Soft Alignment Enables Resilient Learning without Weight Transport Flat minima

Reference 45

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This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T13:51:06.395087Z

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This paper cites Entropy-sgd: Biasing gradient descent into wide valleys.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Entropy-sgd: Biasing gradient descent into wide valleys

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:11.305286Z

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Observation 99e2c61d-1e13-409f-8bc5-d902fe2370c2 · outbound

This paper cites Averaging Weights Leads to Wider Optima and Better Generalization.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Averaging Weights Leads to Wider Optima and Better Generalization

Reference 48

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This paper cites Sharpness-aware minimization for efficiently improving generalization.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Sharpness-aware minimization for efficiently improving generalization

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T13:51:06.649757Z

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Observation 5be52539-c546-4458-8452-ab159e79db86 · outbound

This paper cites Benchmarking neural network robustness to common corruptions and perturbations.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Benchmarking neural network robustness to common corruptions and perturbations

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T13:51:06.735432Z

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Observation 12525b35-44f8-4219-9982-5752bd9dad6d · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Explaining and Harnessing Adversarial Examples

Reference 51

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This paper cites Understanding the difficulty of training deep feedforward neural networks.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Understanding the difficulty of training deep feedforward neural networks

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T13:51:06.886382Z

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This paper cites On the importance of initialization and momentum in deep learning.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport On the importance of initialization and momentum in deep learning

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:11.003019Z

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Observation fd7779df-0725-48f2-b6e5-c04a4e5af671 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T13:51:07.029435Z

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Observation 81fd28dd-ad42-442b-bd6b-45d56dc0a42e · outbound

This paper cites Exact solutions to the nonlinear dynamics of learning in deep linear neural networks.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

Reference 55

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Observation b07b38bc-549d-4e4f-b71d-b30844909e6d · outbound

This paper cites Pretraining with random noise for uncertainty calibration.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Pretraining with random noise for uncertainty calibration

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:51:08.245892Z

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Observation 84dd8943-faa0-401b-b5f4-19ece2a1b8b2 · outbound

This paper cites Deep physical neural networks trained with backpropagation.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Deep physical neural networks trained with backpropagation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:10.748686Z

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Observation 9a906413-fc03-4d16-b267-15cf402471fe · outbound

This paper cites Backpropagation-free training of deep physical neural networks.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Backpropagation-free training of deep physical neural networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:10.339278Z

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Observation 8f92d423-87df-4e53-9bf4-cc117eb0bda0 · outbound

This paper cites Adversarial examples in the physical world.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Adversarial examples in the physical world

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T13:51:07.411993Z

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Observation 64d2cc96-602d-4971-993a-fdb791da1a9a · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T13:51:07.499050Z

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Observation 41b2af7c-ac15-4e01-a4c4-b94090164ec1 · outbound

This paper cites Hessian-based analysis of large batch training and robustness to adversaries.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Hessian-based analysis of large batch training and robustness to adversaries

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:09.949471Z

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Observation 203c72d8-2034-47e9-8557-d249b8a555be · outbound

This paper cites Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:09.658803Z

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Observation 9e9d2473-1c7c-4952-ba2e-f3274ce401b0 · outbound

This paper cites Calculation of gauss quadrature rules.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Calculation of gauss quadrature rules

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:09.397433Z

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Observation b9ac0437-2c1a-4cdb-8d67-16fe75b30513 · outbound

This paper cites An investigation into neural net optimization via hessian eigenvalue density.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport An investigation into neural net optimization via hessian eigenvalue density

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:09.215284Z

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Observation 2f1fce95-6dd9-427e-be48-305bf30f991a · outbound

This paper cites Additionally, we investigate several representative conditions to visualize learning curves: large forward weight variance at (24, √.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport Additionally, we investigate several representative conditions to visualize learning curves: large forward weight variance at (24, √

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:09.061492Z

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Observation ccbe07e9-8221-4dfe-a12f-626610a66cdf · outbound

This paper cites For each variance condition, learning curves of training accuracy, test accuracy, training loss, and test loss are presented from top to bottom.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport For each variance condition, learning curves of training accuracy, test accuracy, training loss, and test loss are presented from top to bottom

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:08.893156Z

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Observation 5eb88e35-abe1-4684-a397-c2fca38f1cad · outbound

This paper cites We also explored a wide range of variances by varying a and b from 10−6 to 100 (smaller variances), and from 20 to 27 (larger variances), using exponential step sizes of 1.

One-Time Soft Alignment Enables Resilient Learning without Weight Transport We also explored a wide range of variances by varying a and b from 10−6 to 100 (smaller variances), and from 20 to 27 (larger variances), using exponential step sizes of 1

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:51:08.640374Z

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Pith citing papers

Observation 866f42b4-0982-48ab-8961-c99c88a10a0d · inbound

Eigen Neural Network: Unlocking Generalizable Vision with Eigenbasis cites this paper.

Eigen Neural Network: Unlocking Generalizable Vision with Eigenbasis One-Time Soft Alignment Enables Resilient Learning without Weight Transport

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-06T05:48:59.445309Z

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