Paper Citation Record · LEDGER

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models

As of 21 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 4 inbound Pith citation observations for arXiv:2502.07753.

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

pith.paper-citation-record.v1
2502.07753 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:44:44.307568Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:58:07.753995Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T06:01:34.741781Z

Reference resolution

39 of 39 outbound references displayed

verified exact7
verified fuzzy10
unresolved20
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 50037f0d-92b0-4c27-9d34-0821b2eed647 · outbound

This paper cites write newline.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models write newline

Reference 1

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Observation 26f50b96-43de-474b-b36c-3a4e40ce05e7 · outbound

This paper cites Gan dissection: Visualizing and understanding generative adversarial networks.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Gan dissection: Visualizing and understanding generative adversarial networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:44:45.077466Z

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Observation b043414c-cca8-45f3-b1de-56559ce56f4f · outbound

This paper cites Seeing What a GAN Cannot Generate.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Seeing What a GAN Cannot Generate

Reference 3

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Observation 33fbf651-1b48-4038-a6d7-663ae4bbe0d8 · outbound

This paper cites and Adelson, E.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models and Adelson, E

Reference 4

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Observation bca12967-d1d1-4c7a-a7a0-0f904d917d89 · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Reproducible scaling laws for contrastive language-image learning

Reference 5

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Observation eae84192-bd5e-4421-9f4d-e30331161941 · outbound

This paper cites Vqgan-clip: Open domain image generation and editing with natural language guidance.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Vqgan-clip: Open domain image generation and editing with natural language guidance

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:44:45.062191Z

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Observation 2133d3de-4b97-4476-9336-66851138504e · outbound

This paper cites Scaling Vision Transformers to 22 Billion Parameters.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Scaling Vision Transformers to 22 Billion Parameters

Reference 7

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Observation fd550690-591a-45c9-a0f8-cf1762d95df8 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

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Observation c9f4ec30-2328-497e-8fa8-7aa5ecc44378 · outbound

This paper cites A mathematical framework for transformer circuits.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models A mathematical framework for transformer circuits

Reference 9

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Observation 13c91a68-34e6-4531-98bf-0ee874101772 · outbound

This paper cites Adversarial examples for the openai clip in its zero-shot classification regime and their semantic generalization, Jan 2021 a.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Adversarial examples for the openai clip in its zero-shot classification regime and their semantic generalization, Jan 2021 a

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:44:45.033878Z

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Observation fee72892-9492-4df4-8a04-bf36c2c9a837 · outbound

This paper cites Pixels still beat text: Attacking the openai clip model with text patches and adversarial pixel perturbations, March 2021 b.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Pixels still beat text: Attacking the openai clip model with text patches and adversarial pixel perturbations, March 2021 b

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:44:45.018398Z

Source-reported events for the cited work

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Observation ba271f53-40e8-45c2-86b3-87108a59ab9c · outbound

This paper cites Adversarial vulnerability of powerful near out-of-distribution detection.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Adversarial vulnerability of powerful near out-of-distribution detection

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-08T11:44:44.769939Z

Source-reported events for the cited work

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Observation 5eaa2c72-a61a-40ef-b734-1cb627090470 · outbound

This paper cites A Note on Implementation Errors in Recent Adaptive Attacks Against Multi-Resolution Self-Ensembles.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models A Note on Implementation Errors in Recent Adaptive Attacks Against Multi-Resolution Self-Ensembles

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-08T11:44:44.748361Z

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Observation a9879aa4-0e7e-483c-93ad-e4d79c07ac27 · outbound

This paper cites Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness

Reference 14

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Source-reported events for the cited work

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Observation 1a6cd761-2792-4343-b164-6b6e73db4ff2 · outbound

This paper cites Exploring the Limits of Out-of-Distribution Detection.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Exploring the Limits of Out-of-Distribution Detection

Reference 15

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Observation 63d41eb4-269f-4e7d-a278-471084ec9f62 · outbound

This paper cites What does a deep neural network confidently perceive? The effective dimension of high certainty class manifolds and their low confidence boundaries.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models What does a deep neural network confidently perceive? The effective dimension of high certainty class manifolds and their low confidence boundaries

Reference 16

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Source-reported events for the cited work

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Observation a4ad4bc8-1ea8-40d8-87ca-d0ad03a1f85a · outbound

This paper cites A., Ecker, A.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models A., Ecker, A

Reference 17

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Observation a0760f66-928c-4686-a278-25234ab249a1 · outbound

This paper cites Generative Adversarial Networks.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Generative Adversarial Networks

Reference 18

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Observation d316c0fb-b76b-4206-9452-32d22ac1d184 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Explaining and Harnessing Adversarial Examples

Reference 19

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Observation 49c24e5e-d793-41c7-833e-cd0452adadef · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Denoising Diffusion Probabilistic Models

Reference 20

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Observation 8aa74569-919e-4d49-83cf-4b7e14046c22 · outbound

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Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Unresolved cited work

Reference 21

Resolution: verified exact; doi, observed 2026-08-08T11:44:44.403608Z

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Observation a1608142-538b-419d-a9f0-b6a166136a77 · outbound

This paper cites Adversarial Examples Are Not Bugs, They Are Features.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Adversarial Examples Are Not Bugs, They Are Features

Reference 22

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Observation ee26bd31-edc0-45eb-a362-b20bd8068f61 · outbound

This paper cites Factors influencing spatial frequency extraction in faces: A review.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Factors influencing spatial frequency extraction in faces: A review

Reference 23

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Observation 6e38b28e-3355-4706-8e41-43a02467ccd0 · outbound

This paper cites An analytic theory of creativity in convolutional diffusion models.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models An analytic theory of creativity in convolutional diffusion models

Reference 24

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Observation 7cd567ef-9587-49c3-97f5-0872b447f0bb · outbound

This paper cites Auto-Encoding Variational Bayes.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Auto-Encoding Variational Bayes

Reference 25

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This paper cites Scale-space theory: a basic tool for analyzing structures at different scales.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Scale-space theory: a basic tool for analyzing structures at different scales

Reference 26

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Observation 8e359fea-b479-4afa-a699-4e971ada44fc · outbound

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

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 27

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This paper cites and Vedaldi, A.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models and Vedaldi, A

Reference 28

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Observation 5dc43119-9cbf-49ad-b263-bdfb9c510a4b · outbound

This paper cites Synthesizing the preferred inputs for neurons in neural networks via deep generator networks.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Synthesizing the preferred inputs for neurons in neural networks via deep generator networks

Reference 29

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Observation 46ec0284-4fb8-4581-b7dd-17e65b272e3f · outbound

This paper cites Feature visualization.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Feature visualization

Reference 30

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Observation 74c9fb18-99d6-4f2d-b13f-de926aa75258 · outbound

This paper cites Zoom in: An introduction to circuits.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Zoom in: An introduction to circuits

Reference 31

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Observation 1c3e99ea-c5c6-428f-9a66-53b83b071b5b · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Learning Transferable Visual Models From Natural Language Supervision

Reference 32

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Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models and Monro, S

Reference 33

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This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models High-Resolution Image Synthesis with Latent Diffusion Models

Reference 34

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Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Unresolved cited work

Reference 35

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Observation 47c1003d-8af1-48eb-928d-d984abcf15c1 · outbound

This paper cites Deep image prior.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Deep image prior

Reference 36

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This paper cites and van Hateren , J.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models and van Hateren , J

Reference 37

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This paper cites imstack: Image stack exploration and analysis.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models imstack: Image stack exploration and analysis

Reference 38

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Observation d05e6b18-6c12-467a-a8c4-50be03a4992e · outbound

This paper cites Understanding Neural Networks Through Deep Visualization.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Understanding Neural Networks Through Deep Visualization

Reference 39

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Pith citing papers

Observation fa680c2a-7b2c-42db-9fa8-07e296413172 · inbound

Implicit Inversion turns CLIP into a Decoder cites this paper.

Implicit Inversion turns CLIP into a Decoder Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models

Reference 6

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TRANSPORTER: Transferring Visual Semantics from VLM Manifolds cites this paper.

TRANSPORTER: Transferring Visual Semantics from VLM Manifolds Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models

Reference 27

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Observation 46c547be-ee49-4b98-8d9e-2295747eacc7 · inbound

QoS-Aware Token Scheduling and Private Data Valuation for Multi-Modal Agentic Networks cites this paper.

QoS-Aware Token Scheduling and Private Data Valuation for Multi-Modal Agentic Networks Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models

Reference 12

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Observation 7614cbbc-27bc-45c0-8c58-610b4453adb0 · inbound

Anchoring and Steering Diffusion: Enhancing the Faithfulness of Text-to-Image Generation at Inference Time cites this paper.

Anchoring and Steering Diffusion: Enhancing the Faithfulness of Text-to-Image Generation at Inference Time Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models

Reference 14

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