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Paper Citation Record · LEDGER

Stealix: Model Stealing via Prompt Evolution

As of 8 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.05867.

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

pith.paper-citation-record.v1
2506.05867 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:17:52.375631Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

40 of 40 outbound references displayed

  • verified exact1
  • verified fuzzy28
  • unresolved11
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 65767bce-2522-4ee6-baa1-f83852f5f9c8 · outbound

This paper cites write newline.

Stealix: Model Stealing via Prompt Evolution write newline

Reference 1

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:17:52.269462Z digest=sha256:ed7f43b1d44ab354c5ece216587ad0b472750fd276d29c98883879e1f2a85830

Observation a3352cad-cb91-4d36-8ffb-2a11fa285563 · outbound

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

Stealix: Model Stealing via Prompt Evolution Learning multiple layers of features from tiny images

Reference 2

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.273123Z digest=sha256:95f5a96ef2cced0c1eb1b976cbe0cae55bd9faabbe96f6183590419ef24743af

Observation ca4d6fe1-091a-43b0-92ef-6513950ab037 · outbound

This paper cites and Caruana, R.

Stealix: Model Stealing via Prompt Evolution and Caruana, R

Reference 3

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 79ea31ee-28a9-49c1-b75d-8aa67e2740fa · outbound

This paper cites S., and Shah, M.

Stealix: Model Stealing via Prompt Evolution S., and Shah, M

Reference 4

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 404fce44-8580-48cb-9a1f-fa82f0ed3d0e · outbound

This paper cites D., Steinke, T., Hayase, J., Cooper, A.

Stealix: Model Stealing via Prompt Evolution D., Steinke, T., Hayase, J., Cooper, A

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.675768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.281668Z digest=sha256:7b673f4bac5df6b116eb4d601ff26d615c3b179c7e595dd1fbb75275f573a6b6

Observation e2879a84-505a-4ebd-91dc-b29acafcc43c · outbound

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

Stealix: Model Stealing via Prompt Evolution Reproducible scaling laws for contrastive language-image learning

Reference 6

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation c9b3c0b2-6d62-439e-967c-26c4dc6bf4c2 · outbound

This paper cites an unresolved cited work.

Stealix: Model Stealing via Prompt Evolution Unresolved cited work

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 01de1bf5-2b2c-4404-8f49-45f482b895b1 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Stealix: Model Stealing via Prompt Evolution An image is worth 16x16 words: Transformers for image recognition at scale

Reference 8

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no resolver link, observed 2026-08-07T10:17:52.289911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:17:52.289911Z digest=sha256:8847b470e53d3a24f8aff8dd80363d68ea0077418c89f935b9030a281ce8b0ed

Observation d54164c9-636e-400e-bd26-c0bf917b4c73 · outbound

This paper cites an unresolved cited work.

Stealix: Model Stealing via Prompt Evolution Unresolved cited work

Reference 9

Resolution
unresolved
raw_fallback, observed 2026-08-07T10:17:52.645746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.293030Z digest=sha256:7a0006ae210ffd810e171e1b26da85c8413e5f65f4990a2344eb8e6ea57cd11b

Observation eff50830-4242-41bf-851b-fbef3aa3683c · outbound

This paper cites Data-Free Adversarial Distillation.

Stealix: Model Stealing via Prompt Evolution Data-Free Adversarial Distillation

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T10:17:52.295327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:17:52.295327Z digest=sha256:23a5c1e856a79ac4cfedb76e589e744c78ad7368436eb895c782319c981f6059

Observation 298ecfff-e755-4e12-a46e-25f122db208f · outbound

This paper cites H., Chechik, G., and Cohen-Or, D.

Stealix: Model Stealing via Prompt Evolution H., Chechik, G., and Cohen-Or, D

Reference 11

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation ca11b5fe-0ab7-4033-930f-dc07c4d5a0fd · outbound

This paper cites Is synthetic data from generative models ready for image recognition? In International Conference on Learning Representations (ICLR), 2023.

Stealix: Model Stealing via Prompt Evolution Is synthetic data from generative models ready for image recognition? In International Conference on Learning Representations (ICLR), 2023

Reference 12

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 554d941c-8bed-4d7c-92e5-c854beea67cb · outbound

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

Stealix: Model Stealing via Prompt Evolution Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T10:17:52.303338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:17:52.303338Z digest=sha256:af1cbe7bfc03bea2606d1eba9ec816f249bfc186b279f77ae55a88c9a182f60a

Observation 315b3058-616f-4ea6-aee3-18fd71bce316 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Stealix: Model Stealing via Prompt Evolution Distilling the Knowledge in a Neural Network

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T10:17:52.305894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:17:52.305894Z digest=sha256:8a84c858c43de8d8d286495f5fcdaa84da216333a68c5eb452cb5ddc343cfb25

Observation c4ef7c35-fb81-4a87-814d-9ff08d0d3a33 · outbound

This paper cites Towards Few-Call Model Stealing via Active Self-Paced Knowledge Distillation and Diffusion-Based Image Generation.

Stealix: Model Stealing via Prompt Evolution Towards Few-Call Model Stealing via Active Self-Paced Knowledge Distillation and Diffusion-Based Image Generation

Reference 15

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verified exact
local_arxiv, observed 2026-08-07T10:17:52.418125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.308823Z digest=sha256:6c71a2d244bc9d8d3e90fbdd51db9faec1f53b5ccbc2f92a8fbfaa4430608e31

Observation 2774991d-2bfb-497b-a89f-692cbe4e4f98 · outbound

This paper cites S., Parikh, A.

Stealix: Model Stealing via Prompt Evolution S., Parikh, A

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.617109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.311574Z digest=sha256:73d4708764c684efc37c9228646d53b836bdfa40c368d27b5931fc30117003e9

Observation 33a52715-39bf-45ab-b0ac-e09f2f53a7a7 · outbound

This paper cites Improved precision and recall metric for assessing generative models.

Stealix: Model Stealing via Prompt Evolution Improved precision and recall metric for assessing generative models

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T10:17:52.313685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:17:52.313685Z digest=sha256:aedc1a3773bb59b33e702e9d80b2d117ee20871363ddf6e2ad54174b5ba8c37c

Observation a1602ed6-ee94-4c1c-b235-f1abe84c9fbd · outbound

This paper cites Defending against machine learning model stealing attacks using deceptive perturbations.

Stealix: Model Stealing via Prompt Evolution Defending against machine learning model stealing attacks using deceptive perturbations

Reference 18

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.316434Z digest=sha256:770a1995bb34d69e73d30d4cdf8791fb8ed7232ab57c34e19abfdf4b22bb2ea4

Observation 66a0e630-8eb8-4f02-8eab-30395d871ccb · outbound

This paper cites Not-safe-for-work dataset.

Stealix: Model Stealing via Prompt Evolution Not-safe-for-work dataset

Reference 19

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation aa8371b3-332d-44da-9c86-a2073dd3e2a8 · outbound

This paper cites G., Fenu, S., and Starner, T.

Stealix: Model Stealing via Prompt Evolution G., Fenu, S., and Starner, T

Reference 20

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 4b31d9e4-c976-4ab3-b261-8b0da53708e6 · outbound

This paper cites How to steer your adversary: Targeted and efficient model stealing defenses with gradient redirection.

Stealix: Model Stealing via Prompt Evolution How to steer your adversary: Targeted and efficient model stealing defenses with gradient redirection

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.580503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.328197Z digest=sha256:e0af11db2d4be94a7597409fd83277fc2d9589197fc1f6fdd4dfce0aa223420e

Observation e24cda83-f2fa-4ff8-a104-0b8002209b21 · outbound

This paper cites and Storkey, A.

Stealix: Model Stealing via Prompt Evolution and Storkey, A

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.573252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.330804Z digest=sha256:1fc76ab28d978a63d098b2298a3d0ae6e8f8c432cd45ad91710b77a54b8e4a38

Observation e550151b-8d4b-4af9-ac03-53c456615405 · outbound

This paper cites I know what you trained last summer: A survey on stealing machine learning models and defences.

Stealix: Model Stealing via Prompt Evolution I know what you trained last summer: A survey on stealing machine learning models and defences

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.565606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.333271Z digest=sha256:7b9d06c508c032a6cd29806fcc071c63eea327013513bd64a875749a3164b46f

Observation ce0dff98-9030-4df3-b8e2-8d8795630f1d · outbound

This paper cites Knockoff nets: Stealing functionality of black-box models.

Stealix: Model Stealing via Prompt Evolution Knockoff nets: Stealing functionality of black-box models

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.558133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.335783Z digest=sha256:9be981e9bd2abaf6656a9e1c8d9ec27975b9185201b280a47d48cd6411aa5f65

Observation 0feb3b10-535a-41e0-a5a9-47836e6fcc03 · outbound

This paper cites Moment matching for multi-source domain adaptation.

Stealix: Model Stealing via Prompt Evolution Moment matching for multi-source domain adaptation

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.550454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.338049Z digest=sha256:d2e60c61c7006ac677818ba1055f4be2fedb0cbe94f4c8327965ef49db50f30d

Observation f5c61e3d-1c20-49ec-9b59-2980ed458afa · outbound

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

Stealix: Model Stealing via Prompt Evolution High-resolution image synthesis with latent diffusion models

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T10:17:52.340504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ef3660f9-43ff-49b5-a23a-21802d3efd42 · outbound

This paper cites an unresolved cited work.

Stealix: Model Stealing via Prompt Evolution Unresolved cited work

Reference 27

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation ab0b77f0-5735-4cd9-beee-3541cbbc495f · outbound

This paper cites Latent Code Augmentation Based on Stable Diffusion for Data-free Substitute Attacks.

Stealix: Model Stealing via Prompt Evolution Latent Code Augmentation Based on Stable Diffusion for Data-free Substitute Attacks

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-07T10:17:52.345745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:17:52.345745Z digest=sha256:7544f15f44a161d880510909a2c4c0fdafa5e6802429597ee2ec156f828133ac

Observation ec80da1d-a561-4f0e-a82d-dc8eeaff5a0b · outbound

This paper cites Medical multimodal model stealing attacks via adversarial domain alignment.

Stealix: Model Stealing via Prompt Evolution Medical multimodal model stealing attacks via adversarial domain alignment

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.530747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.348361Z digest=sha256:8db38d8409498a06e1aafdc3e56a35bee467e241e7c43fde52fa7f58c1872cc7

Observation 20d70eb4-a174-469e-8560-a0fe176c3534 · outbound

This paper cites Not-safe-for-work image detection.

Stealix: Model Stealing via Prompt Evolution Not-safe-for-work image detection

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.523180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.350978Z digest=sha256:76e4e0807580d2cd2422a8ab31722cc128108d0992b7c3302609cde63892b117

Observation 1857ba9d-a4b5-485c-a4b6-56faf261984b · outbound

This paper cites Effective data augmentation with diffusion models.

Stealix: Model Stealing via Prompt Evolution Effective data augmentation with diffusion models

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.514960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.353477Z digest=sha256:b407286b8a6f657344c4899b0c959d721fb2b6a52543ebe99aa8345ffa8d2bd4

Observation b2f22048-1e46-4a6e-9afd-5102e80462e7 · outbound

This paper cites K., and Ristenpart, T.

Stealix: Model Stealing via Prompt Evolution K., and Ristenpart, T

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.506632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.355734Z digest=sha256:ede6db49c6c5011de98c5058a6450f25f738d748da739e81bb41b128831fdf06

Observation ead408df-a715-49a0-a6ff-fd383985270b · outbound

This paper cites J., and Papernot, N.

Stealix: Model Stealing via Prompt Evolution J., and Papernot, N

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.499322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.357967Z digest=sha256:13be0df34cbba019e412f991f76082087fa610bf5b3d4da5a857559a7703d83c

Observation d0b19736-606c-4c9b-8cb8-db0188cd8923 · outbound

This paper cites S., Linmans, J., Winkens, J., Cohen, T., and Welling, M.

Stealix: Model Stealing via Prompt Evolution S., Linmans, J., Winkens, J., Cohen, T., and Welling, M

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.492187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.360305Z digest=sha256:6aaa9770388f03c5672b1086dafefce29017f6cc8b3e8a494f6051f5654eaf81

Observation 7dd2663b-56b2-4221-9550-32de68ceeb78 · outbound

This paper cites and Gong, N.

Stealix: Model Stealing via Prompt Evolution and Gong, N

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.484282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.362643Z digest=sha256:afc1be6018e3aa20652b16b03bba824fbd9dd092c2ebe60cae079f70ac74a17a

Observation 4542a3a9-e405-4849-a393-4a6f239761d0 · outbound

This paper cites Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery.

Stealix: Model Stealing via Prompt Evolution Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.476512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.365195Z digest=sha256:d9a21fac2d55545f83a15dfbb0ab9f1366df9f61696dbcd37a6bf51b7dc97206

Observation 5e98fe4e-d778-4243-98c6-b36888c5c637 · outbound

This paper cites J., Jordan, M., and Duchi, J.

Stealix: Model Stealing via Prompt Evolution J., Jordan, M., and Duchi, J

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.468922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.367533Z digest=sha256:f28fb367a4e17ff39d84070ef7e3553d3626fd01a5bb9d8a7d390d387c11db80

Observation 482a8a03-e050-4459-b58b-986f23653945 · outbound

This paper cites Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification.

Stealix: Model Stealing via Prompt Evolution Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.461084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.369879Z digest=sha256:aa935346f92c0927604075555b01cd55ad6282c0e9663e5a1a47504430f21b1b

Observation 67efd9de-4e1f-4125-a184-b37f224bc04b · outbound

This paper cites Genetic algorithms in search, optimization and machine learning.

Stealix: Model Stealing via Prompt Evolution Genetic algorithms in search, optimization and machine learning

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.453171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.372709Z digest=sha256:61f4cc43fb2ebf3cea8507fb82153394955becb7a45b69802c196ba42af4ccae

Observation d136a642-4f25-4a98-afa2-d66bb4703382 · outbound

This paper cites Stealthy imitation: Reward-guided environment-free policy stealing.

Stealix: Model Stealing via Prompt Evolution Stealthy imitation: Reward-guided environment-free policy stealing

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:17:52.444303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.375631Z digest=sha256:02d7a789c1a068fa106e45c3130202f167b923555634b9b2d896bd821c33e691

Pith citing papers

No inbound Pith citation observations are available.