Paper Citation Record · LEDGER

Navigating the Latent Space Dynamics of Neural Models

As of 18 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 2 inbound Pith citation observations for arXiv:2505.22785.

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

pith.paper-citation-record.v1
2505.22785 v4

Coverage vector

measured 72 of 72 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:08:02.154367Z

measured 74 of 74 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T21:08:34.575090Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T21:09:02.569030Z

Reference resolution

72 of 72 outbound references displayed

verified exact1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 971b269f-2110-4715-987a-29e30a543693 · outbound

This paper cites write newline.

Navigating the Latent Space Dynamics of Neural Models write newline

Reference 1

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Observation 8219dce2-c9ee-4c3b-8477-413e90ae02dd · outbound

This paper cites Gated autoencoders with tied input weights.

Navigating the Latent Space Dynamics of Neural Models Gated autoencoders with tied input weights

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:08:08.450779Z

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Observation 839d4d36-bb2d-4716-8ea0-02586f65c752 · outbound

This paper cites What regularized auto-encoders learn from the data-generating distribution.

Navigating the Latent Space Dynamics of Neural Models What regularized auto-encoders learn from the data-generating distribution

Reference 3

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Observation 2a66ae4d-fb4b-4f67-918f-5dab864ca49f · outbound

This paper cites SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model.

Navigating the Latent Space Dynamics of Neural Models SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model

Reference 4

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Observation 6e8cac99-7e9c-4c69-a333-8a27e614818b · outbound

This paper cites A closer look at memorization in deep networks.

Navigating the Latent Space Dynamics of Neural Models A closer look at memorization in deep networks

Reference 5

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Observation e07898de-08dc-4c5a-9057-7bf1de24b0d5 · outbound

This paper cites Deep equilibrium models.

Navigating the Latent Space Dynamics of Neural Models Deep equilibrium models

Reference 6

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Observation 0af9ab32-5231-4532-80ad-1511356e50cd · outbound

This paper cites Representation learning: A review and new perspectives.

Navigating the Latent Space Dynamics of Neural Models Representation learning: A review and new perspectives

Reference 7

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Observation dd8db87d-47be-41a6-a4e4-b729ec6c87e7 · outbound

This paper cites Nonlinear power method for computing eigenvectors of proximal operators and neural networks.

Navigating the Latent Space Dynamics of Neural Models Nonlinear power method for computing eigenvectors of proximal operators and neural networks

Reference 8

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Source-reported events for the cited work

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Observation 19c9d909-51df-4168-8564-a286d2591a6f · outbound

This paper cites Neural ordinary differential equations.

Navigating the Latent Space Dynamics of Neural Models Neural ordinary differential equations

Reference 9

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Observation 351fc6bf-158a-409e-a2d3-e109c7234adf · outbound

This paper cites Describing textures in the wild.

Navigating the Latent Space Dynamics of Neural Models Describing textures in the wild

Reference 10

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Source-reported events for the cited work

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Observation 3ef44025-a043-41a2-9f30-c2c4c1524fb5 · outbound

This paper cites Why do we need weight decay in modern deep learning? Advances in Neural Information Processing Systems, 37: 0 23191--23223, 2024.

Navigating the Latent Space Dynamics of Neural Models Why do we need weight decay in modern deep learning? Advances in Neural Information Processing Systems, 37: 0 23191--23223, 2024

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:08:07.687815Z

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Observation 885dcfad-692e-44d2-85a4-b9187047ad46 · outbound

This paper cites Investigating data memorization in 3d latent diffusion models for medical image synthesis.

Navigating the Latent Space Dynamics of Neural Models Investigating data memorization in 3d latent diffusion models for medical image synthesis

Reference 12

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Observation dfe5a744-921d-4512-a661-b7a67b04734e · outbound

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

Navigating the Latent Space Dynamics of Neural Models Imagenet: A large-scale hierarchical image database

Reference 13

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Observation 0fcdf857-7071-4539-8083-196b595ada06 · outbound

This paper cites Nonlinear spectral geometry processing via the tv transform.

Navigating the Latent Space Dynamics of Neural Models Nonlinear spectral geometry processing via the tv transform

Reference 14

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Observation c9b8387e-a54f-40cd-9655-a35095685e26 · outbound

This paper cites Latent functional maps: a spectral framework for representation alignment.

Navigating the Latent Space Dynamics of Neural Models Latent functional maps: a spectral framework for representation alignment

Reference 15

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Observation 48805679-fa13-45a6-9791-3d2e9053094b · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Navigating the Latent Space Dynamics of Neural Models The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 16

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Observation 257f6c7f-f124-4281-95ec-bd50236d7b11 · outbound

This paper cites Scaling and evaluating sparse autoencoders.

Navigating the Latent Space Dynamics of Neural Models Scaling and evaluating sparse autoencoders

Reference 17

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Observation c910a0cf-eb04-46c0-a705-b9e0b054a4aa · outbound

This paper cites Nonlinear spectral analysis via one-homogeneous functionals: overview and future prospects.

Navigating the Latent Space Dynamics of Neural Models Nonlinear spectral analysis via one-homogeneous functionals: overview and future prospects

Reference 18

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Observation 67068c9a-7dc8-4c7e-9208-efb7f89c51ee · outbound

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

Navigating the Latent Space Dynamics of Neural Models Understanding the difficulty of training deep feedforward neural networks

Reference 19

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Observation fd6ed4b1-591e-4d95-bcb9-d5f626ca3d94 · outbound

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

Navigating the Latent Space Dynamics of Neural Models Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 20

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Observation d5999bea-12eb-458a-a0d3-aa8368dba8f4 · outbound

This paper cites Deep residual learning for image recognition.

Navigating the Latent Space Dynamics of Neural Models Deep residual learning for image recognition

Reference 21

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Observation 172b605c-8d24-4961-b1bc-5c0d45ac42f9 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Navigating the Latent Space Dynamics of Neural Models Masked autoencoders are scalable vision learners

Reference 22

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Observation fdd764a9-eb25-44b4-b623-db61603a49f3 · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.

Navigating the Latent Space Dynamics of Neural Models Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 23

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Observation 770a5a47-6971-4fa8-9622-0037f162d938 · outbound

This paper cites Neural networks and physical systems with emergent collective computational abilities.

Navigating the Latent Space Dynamics of Neural Models Neural networks and physical systems with emergent collective computational abilities

Reference 24

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Observation e04a1e7e-b96f-44ae-b667-9ccf72728ced · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Navigating the Latent Space Dynamics of Neural Models Lora: Low-rank adaptation of large language models

Reference 25

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Observation 1027b3cd-4ec3-4376-a02b-00d85c6c6183 · outbound

This paper cites The Platonic Representation Hypothesis.

Navigating the Latent Space Dynamics of Neural Models The Platonic Representation Hypothesis

Reference 26

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Observation 76bef5b9-0880-4b55-bbee-32e6f58390e1 · outbound

This paper cites Associative memory in iterated overparameterized sigmoid autoencoders.

Navigating the Latent Space Dynamics of Neural Models Associative memory in iterated overparameterized sigmoid autoencoders

Reference 27

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Observation f3777c49-a7dc-41d3-a1ae-aaa99a1b0370 · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

Navigating the Latent Space Dynamics of Neural Models Highly accurate protein structure prediction with alphafold

Reference 28

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Observation 9b1f5f62-7c94-45b9-bce1-e00c9682067b · outbound

This paper cites Generalization in diffusion models arises from geometry-adaptive harmonic representations.

Navigating the Latent Space Dynamics of Neural Models Generalization in diffusion models arises from geometry-adaptive harmonic representations

Reference 29

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Observation 96b9d6a9-8858-4ed3-8940-34817cc12abc · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Navigating the Latent Space Dynamics of Neural Models Adam: A Method for Stochastic Optimization

Reference 30

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This paper cites Auto-encoding variational bayes, 2013.

Navigating the Latent Space Dynamics of Neural Models Auto-encoding variational bayes, 2013

Reference 31

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

Navigating the Latent Space Dynamics of Neural Models Learning multiple layers of features from tiny images

Reference 32

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Observation 30318d31-356a-4a9e-a2d9-86415afe7100 · outbound

This paper cites A simple weight decay can improve generalization.

Navigating the Latent Space Dynamics of Neural Models A simple weight decay can improve generalization

Reference 33

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Navigating the Latent Space Dynamics of Neural Models Efficient backprop

Reference 34

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Observation 5e329b84-1c6a-4ace-9d37-1a8299ad10bb · outbound

This paper cites 1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset.

Navigating the Latent Space Dynamics of Neural Models 1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset

Reference 35

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Observation 3e30e8ec-2b18-4e57-9af1-1d7cb7faf87b · outbound

This paper cites Decoupled Weight Decay Regularization.

Navigating the Latent Space Dynamics of Neural Models Decoupled Weight Decay Regularization

Reference 36

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This paper cites Mallat and Zhifeng Zhang.

Navigating the Latent Space Dynamics of Neural Models Mallat and Zhifeng Zhang

Reference 37

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This paper cites An empirical bayes estimator of the mean of a normal population.

Navigating the Latent Space Dynamics of Neural Models An empirical bayes estimator of the mean of a normal population

Reference 38

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This paper cites Relative representations enable zero-shot latent space communication.

Navigating the Latent Space Dynamics of Neural Models Relative representations enable zero-shot latent space communication

Reference 39

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This paper cites Deep double descent: Where bigger models and more data hurt.

Navigating the Latent Space Dynamics of Neural Models Deep double descent: Where bigger models and more data hurt

Reference 40

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Navigating the Latent Space Dynamics of Neural Models Sparse autoencoder

Reference 41

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Navigating the Latent Space Dynamics of Neural Models DINOv2: Learning Robust Visual Features without Supervision

Reference 42

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This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Navigating the Latent Space Dynamics of Neural Models PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 43

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This paper cites Exponential expressivity in deep neural networks through transient chaos.

Navigating the Latent Space Dynamics of Neural Models Exponential expressivity in deep neural networks through transient chaos

Reference 44

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This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.

Navigating the Latent Space Dynamics of Neural Models Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 45

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Navigating the Latent Space Dynamics of Neural Models Learning transferable visual models from natural language supervision

Reference 46

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Navigating the Latent Space Dynamics of Neural Models Overparameterized neural networks implement associative memory

Reference 47

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Navigating the Latent Space Dynamics of Neural Models Hopfield Networks is All You Need

Reference 48

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This paper cites Contractive auto-encoders: Explicit invariance during feature extraction.

Navigating the Latent Space Dynamics of Neural Models Contractive auto-encoders: Explicit invariance during feature extraction

Reference 49

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Observation acf013a9-6152-4930-b10b-81cd3aaee87e · outbound

This paper cites An empirical bayes approach to statistics.

Navigating the Latent Space Dynamics of Neural Models An empirical bayes approach to statistics

Reference 50

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This paper cites High-resolution image synthesis with latent diffusion models.

Navigating the Latent Space Dynamics of Neural Models High-resolution image synthesis with latent diffusion models

Reference 51

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Navigating the Latent Space Dynamics of Neural Models Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 52

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Observation 5f0d7f96-b458-4a60-8b14-7040de97ee88 · outbound

This paper cites Diffusion art or digital forgery? investigating data replication in diffusion models.

Navigating the Latent Space Dynamics of Neural Models Diffusion art or digital forgery? investigating data replication in diffusion models

Reference 53

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Observation 71fd5a05-79b9-4145-8794-27fd6891665f · outbound

This paper cites How to Train Your Energy-Based Models.

Navigating the Latent Space Dynamics of Neural Models How to Train Your Energy-Based Models

Reference 54

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Observation b3e57415-6bf8-4246-bbbd-0590ca45e036 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.

Navigating the Latent Space Dynamics of Neural Models Roformer: Enhanced transformer with rotary position embedding

Reference 55

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Observation 1ad27d59-7003-45c9-95bc-62bf4cede2ed · outbound

This paper cites Out-of-distribution detection with deep nearest neighbors.

Navigating the Latent Space Dynamics of Neural Models Out-of-distribution detection with deep nearest neighbors

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:08:03.879205Z

Source-reported events for the cited work

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Observation 010c5fd8-eadc-45b4-ab16-39a9087e9cf4 · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

Navigating the Latent Space Dynamics of Neural Models SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 57

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Observation 08d9c443-b5d1-4aa9-955f-b6ba160021db · outbound

This paper cites The inaturalist species classification and detection dataset.

Navigating the Latent Space Dynamics of Neural Models The inaturalist species classification and detection dataset

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T13:08:00.966527Z

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Observation 9ad552cb-7481-451b-8a88-7e7f8b23fee4 · outbound

This paper cites A connection between score matching and denoising autoencoders.

Navigating the Latent Space Dynamics of Neural Models A connection between score matching and denoising autoencoders

Reference 59

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This paper cites Extracting and composing robust features with denoising autoencoders.

Navigating the Latent Space Dynamics of Neural Models Extracting and composing robust features with denoising autoencoders

Reference 60

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This paper cites Sun database: Exploring a large collection of scene categories.

Navigating the Latent Space Dynamics of Neural Models Sun database: Exploring a large collection of scene categories

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:08:03.482421Z

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Observation 1de21259-ec3d-40f4-9457-4835552f8340 · outbound

This paper cites Qwen3 Technical Report.

Navigating the Latent Space Dynamics of Neural Models Qwen3 Technical Report

Reference 62

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This paper cites Generalized out-of-distribution detection: A survey.

Navigating the Latent Space Dynamics of Neural Models Generalized out-of-distribution detection: A survey

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:08:03.329240Z

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Observation fb229853-4688-45a5-9934-2ab7afde0b36 · outbound

This paper cites Deep structured energy based models for anomaly detection.

Navigating the Latent Space Dynamics of Neural Models Deep structured energy based models for anomaly detection

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:08:03.198993Z

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This paper cites Identity Crisis: Memorization and Generalization under Extreme Overparameterization.

Navigating the Latent Space Dynamics of Neural Models Identity Crisis: Memorization and Generalization under Extreme Overparameterization

Reference 65

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Observation 9fe8c9ac-dd5e-4691-894d-1b8524b30aaa · outbound

This paper cites Understanding deep learning (still) requires rethinking generalization.

Navigating the Latent Space Dynamics of Neural Models Understanding deep learning (still) requires rethinking generalization

Reference 66

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This paper cites Diffusion Transformers with Representation Autoencoders.

Navigating the Latent Space Dynamics of Neural Models Diffusion Transformers with Representation Autoencoders

Reference 67

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Observation fac2d5c6-14b9-4572-8a51-8cfeec2b576f · outbound

This paper cites Anomaly detection with robust deep autoencoders.

Navigating the Latent Space Dynamics of Neural Models Anomaly detection with robust deep autoencoders

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:08:02.963080Z

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Observation f84bf5ec-ae84-4570-99b1-e3982dd39583 · outbound

This paper cites A Survey on Latent Reasoning.

Navigating the Latent Space Dynamics of Neural Models A Survey on Latent Reasoning

Reference 69

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This paper cites @esa (Ref.

Navigating the Latent Space Dynamics of Neural Models @esa (Ref

Reference 70

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Observation a9dd1b11-7c32-46ba-b2c5-435b4d3f68a1 · outbound

This paper cites an unresolved cited work.

Navigating the Latent Space Dynamics of Neural Models Unresolved cited work

Reference 71

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Observation 1c190883-44a4-473b-9819-8ccba38c1525 · outbound

This paper cites The encoder E_ _1 maps inputs x p(x) supported on X R ^m to a typically lower-dimensional space Z R ^k , and the decoder D_ _2 reconstructs the input.

Navigating the Latent Space Dynamics of Neural Models The encoder E_ _1 maps inputs x p(x) supported on X R ^m to a typically lower-dimensional space Z R ^k , and the decoder D_ _2 reconstructs the input

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T13:08:02.154367Z

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Pith citing papers

Observation 4d1e0997-b658-4b5f-adb0-8c1587add102 · inbound

SEMASIA: A Large-Scale Dataset of Semantically Structured Latent Representations cites this paper.

SEMASIA: A Large-Scale Dataset of Semantically Structured Latent Representations Navigating the Latent Space Dynamics of Neural Models

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-07-30T02:04:16.455541Z

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Observation 98608b47-1b1e-4e04-902a-0e96c8750d47 · inbound

Lost and Found in Translation: Variational Diagnostics for Neural Codebook Channels cites this paper.

Lost and Found in Translation: Variational Diagnostics for Neural Codebook Channels Navigating the Latent Space Dynamics of Neural Models

Reference 26

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