Paper Citation Record · LEDGER

Model alignment using inter-modal bridges

As of 19 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.12322.

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

pith.paper-citation-record.v1
2505.12322 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:42:14.403081Z

measured 41 of 41 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

verified exact4
verified fuzzy11
unresolved26
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 12100fcf-31fe-4b20-a74d-8b0e8d832a69 · outbound

This paper cites Are we done with ImageNet?.

Model alignment using inter-modal bridges Are we done with ImageNet?

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.246876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7caeb258-9fb4-4a91-9ee9-5f8d9baf2214 · outbound

This paper cites For each, we had three variations–small, medium, and large (Table 1)– with the SiLU activation function applied after every layer in all models.

Model alignment using inter-modal bridges For each, we had three variations–small, medium, and large (Table 1)– with the SiLU activation function applied after every layer in all models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:42:14.834741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 40ffa303-54d2-47e9-a47f-7f765ab04f0f · outbound

This paper cites Here, FC is for the fully connected layer, Conv is for the convolutional layer and ConvT is for the convolutional transpose layer.

Model alignment using inter-modal bridges Here, FC is for the fully connected layer, Conv is for the convolutional layer and ConvT is for the convolutional transpose layer

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:42:14.775243Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4e13747c-89b6-4242-94c2-d88852296da1 · outbound

This paper cites Forp,q∈P (X), KL(p∥ q) = R Xp(x) log p(x) q(x)dx denotes the Kullback–Leibler divergence between these two distributions.

Model alignment using inter-modal bridges Forp,q∈P (X), KL(p∥ q) = R Xp(x) log p(x) q(x)dx denotes the Kullback–Leibler divergence between these two distributions

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:42:14.889134Z

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Observation a68bf24a-fec3-4f59-aa0a-46111d52f46c · outbound

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

Model alignment using inter-modal bridges Gemini: A Family of Highly Capable Multimodal Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.272335Z

Source-reported events for the cited work

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Observation 6582b080-587c-45fb-baa9-c0a6e606f1bc · outbound

This paper cites Representation Alignment in Neural Networks.

Model alignment using inter-modal bridges Representation Alignment in Neural Networks

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:42:14.659157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation edf5fbe0-b9b8-499b-8bd5-56d1ef55694b · outbound

This paper cites Kingma and Max Welling.

Model alignment using inter-modal bridges Kingma and Max Welling

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:42:14.935641Z

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Observation 8873cf04-a24a-41a1-be98-764a32f89c73 · outbound

This paper cites GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics.

Model alignment using inter-modal bridges GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:42:14.626352Z

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Observation 56bb8656-7aef-4d52-a2da-44ba0384e5a4 · outbound

This paper cites Big Transfer (BiT): General Visual Representation Learning.

Model alignment using inter-modal bridges Big Transfer (BiT): General Visual Representation Learning

Reference 14

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Observation e9b42304-32f4-493b-a45d-50b332972905 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Model alignment using inter-modal bridges UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.307775Z

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Observation 5192cda8-dc79-4772-bffe-53a596d97d40 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Model alignment using inter-modal bridges Representation Learning with Contrastive Predictive Coding

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.315403Z

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Observation 48e6d0cb-65d5-499a-b743-4a222bffcd70 · outbound

This paper cites GPT-4 Technical Report.

Model alignment using inter-modal bridges GPT-4 Technical Report

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.320121Z

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Observation bcb943e5-c2a8-4f97-b419-42fc79611905 · outbound

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

Model alignment using inter-modal bridges PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.323323Z

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Observation 86c43b39-aa7e-4a47-9d37-0dd8eb376155 · outbound

This paper cites an unresolved cited work.

Model alignment using inter-modal bridges Unresolved cited work

Reference 21

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:42:14.508784Z

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Observation 72127907-e68c-4af5-a9a1-47ea0567754f · outbound

This paper cites an unresolved cited work.

Model alignment using inter-modal bridges Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:42:14.911999Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dd79f27e-f3da-4f4c-b3f2-af4fd0ffeecc · outbound

This paper cites Meyer Scetbon, Gabriel Peyré, and Marco Cuturi.

Model alignment using inter-modal bridges Meyer Scetbon, Gabriel Peyré, and Marco Cuturi

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.333942Z

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Observation 59c6854a-4475-41fc-bb01-b59f4e3f9fa5 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Model alignment using inter-modal bridges Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.337697Z

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Observation fda2c36c-0630-4f67-88fb-003eed6041b5 · outbound

This paper cites However, it may also pose risks, such as enabling surveillance via cross- modal linking of personal data or amplifying biases when aligning poorly disentangled representations.

Model alignment using inter-modal bridges However, it may also pose risks, such as enabling surveillance via cross- modal linking of personal data or amplifying biases when aligning poorly disentangled representations

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:42:14.900239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation deb2cc1c-e527-42b3-be97-e48d9bec623c · outbound

This paper cites Additionally, regularisation can improve convergence properties and ensure the existence of unique solutions[Peyré and Cuturi, 2019].

Model alignment using inter-modal bridges Additionally, regularisation can improve convergence properties and ensure the existence of unique solutions[Peyré and Cuturi, 2019]

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:42:14.877359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d94dfff5-8062-40c6-819a-3664d369a19b · outbound

This paper cites This approach necessitates only a small amount of labelled data but requires an additional model to be trained in the joint space for downstream tasks.

Model alignment using inter-modal bridges This approach necessitates only a small amount of labelled data but requires an additional model to be trained in the joint space for downstream tasks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:42:14.866685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1ae6a9c8-4434-4710-9e68-1c3d380e757a · outbound

This paper cites • Reweighting neural networksηθ,ξθ: multi-layer perceptron (MLP) used in Klein et al.

Model alignment using inter-modal bridges • Reweighting neural networksηθ,ξθ: multi-layer perceptron (MLP) used in Klein et al

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:42:14.855983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f59e0b4e-db33-4242-b287-36f6316c46c9 · outbound

This paper cites an unresolved cited work.

Model alignment using inter-modal bridges Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:42:14.845581Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ebc8f39c-5e39-4fde-8156-7e83e9cf54f3 · outbound

This paper cites an unresolved cited work.

Model alignment using inter-modal bridges Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:42:14.822487Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1e5300c0-9312-4b2b-9fd9-794d79d28084 · outbound

This paper cites embedding space, and each decoder reconstructs the output based on the representation in this joint space.

Model alignment using inter-modal bridges embedding space, and each decoder reconstructs the output based on the representation in this joint space

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:42:14.811446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1e32ca08-e2a5-4469-8935-ad1711370fa1 · outbound

This paper cites an unresolved cited work.

Model alignment using inter-modal bridges Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:42:14.786941Z

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Observation 463ffe3a-9319-417c-bc4a-f63bffde320a · outbound

This paper cites an unresolved cited work.

Model alignment using inter-modal bridges Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:42:14.763355Z

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Observation df2e1167-00c6-44fc-9f9b-8bd4300b097c · outbound

This paper cites an unresolved cited work.

Model alignment using inter-modal bridges Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:42:14.751563Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d90f535c-665d-47d0-968d-10af7d6c1ca3 · outbound

This paper cites For V4 and IT region, we trained separate SVMs to decode neural activity corresponding to the category of core images using the entire training dataset.

Model alignment using inter-modal bridges For V4 and IT region, we trained separate SVMs to decode neural activity corresponding to the category of core images using the entire training dataset

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:42:14.740705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6812ac34-8e6e-4f82-b216-32a6166034c2 · outbound

This paper cites an unresolved cited work.

Model alignment using inter-modal bridges Unresolved cited work

Reference 224

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:42:14.799905Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 42ed9de6-d17f-41a6-8bc9-26e135e4f0b1 · outbound

This paper cites Geodesic sinkhorn for fast and accurate optimal transport on manifolds.

Model alignment using inter-modal bridges Geodesic sinkhorn for fast and accurate optimal transport on manifolds

Reference 1992

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:42:14.948459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation abebf6ba-9cb2-4323-9f45-99577e0ee4ab · outbound

This paper cites Linearly Mapping from Image to Text Space.

Model alignment using inter-modal bridges Linearly Mapping from Image to Text Space

Reference 2011

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Source-reported events for the cited work

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Observation 8aa34d15-8cf7-47d6-b003-c90f0643afe0 · outbound

This paper cites Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein.

Model alignment using inter-modal bridges Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.255391Z

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Observation 773d8261-8f2b-4cb0-8ed1-6b5912c5de20 · outbound

This paper cites GeRA: Label-Efficient Geometrically Regularized Alignment.

Model alignment using inter-modal bridges GeRA: Label-Efficient Geometrically Regularized Alignment

Reference 2014

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:42:14.643326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d2c35c80-1645-497c-8add-e835497cd5f4 · outbound

This paper cites Flow Matching for Generative Modeling.

Model alignment using inter-modal bridges Flow Matching for Generative Modeling

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.304212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62192fbb-dd59-4202-946a-c1d51b60d339 · outbound

This paper cites A contribution to Optimal Transport on incomparable spaces.

Model alignment using inter-modal bridges A contribution to Optimal Transport on incomparable spaces

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.341853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1efb2a4-bc1c-4b71-82c8-c193532c1b5d · outbound

This paper cites Language Models are Few-Shot Learners.

Model alignment using inter-modal bridges Language Models are Few-Shot Learners

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.251635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb9030be-e389-464b-ac46-b1eb675ae7d7 · outbound

This paper cites Minibatch optimal transport distances; analysis and applications.

Model alignment using inter-modal bridges Minibatch optimal transport distances; analysis and applications

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.268132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49e10058-6c5c-482c-bab5-3426fbccc2e2 · outbound

This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks.

Model alignment using inter-modal bridges Sentence-bert: Sentence embeddings using siamese bert-networks

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.326730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12c179c0-9b06-4cb6-be7a-7701fb64c8c1 · outbound

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

Model alignment using inter-modal bridges Imagenet: A large-scale hierarchical image database

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.260185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10c82f55-fc8e-4c9f-b935-ce0b3d356fdc · outbound

This paper cites The Platonic Representation Hypothesis.

Model alignment using inter-modal bridges The Platonic Representation Hypothesis

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.280171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cab46656-3757-4c57-b4bd-beb842fb6cb9 · outbound

This paper cites Learning with minibatch Wasserstein : asymptotic and gradient properties.

Model alignment using inter-modal bridges Learning with minibatch Wasserstein : asymptotic and gradient properties

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:14.263758Z

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Unavailable: canonical work link unavailable.

Pith citing papers

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