Pith. sign in

Paper Citation Record · LEDGER

Stealix: Model Stealing via Prompt Evolution

As of 7 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 2026-08-07T06:34:17.273281+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
unresolved
no resolver link, observed 2026-08-07T10:17:52.269462Z

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
raw_fallback, observed 2026-08-07T10:17:52.700450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.273123Z digest=sha256:3367be723287c1489089884e49abf042328ba99ca26da83794b62fcb40de863b

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
raw_fallback, observed 2026-08-07T10:17:52.691739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.275830Z digest=sha256:d4180f94ebce7726662debf519d07b4ca16e841cd9e7e81300518dbeebc46065

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
raw_fallback, observed 2026-08-07T10:17:52.683356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.278641Z digest=sha256:943180c6faf12c55cfbc24c2b91af5d78115af7575cd2173bfa8c572c30fb1a2

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.281668Z digest=sha256:b6225213623f51cb12ae4b74f3e7e1a62992613e92a634e0f9cb8e80d7310baf

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
raw_fallback, observed 2026-08-07T10:17:52.667452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.284516Z digest=sha256:a8bcd5de0b617f789da2a3295f1407ef312d54a683b4b09e19c6fcf605546dfb

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

Resolution
unresolved
raw_fallback, observed 2026-08-07T10:17:52.658279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.287235Z digest=sha256:02cfef709b6ba5f83e7d417babea3a900b950f5959dc6f7892bfb8b578a1bd75

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

Resolution
unresolved
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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.293030Z digest=sha256:a138b3626b373a0cee843d2b4c18140d9c69e414d501848946db083a021eaa18

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
raw_fallback, observed 2026-08-07T10:17:52.637682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.298142Z digest=sha256:512a4f30fb1c7e510c1aa2562d4dc0569bf5d0221e089651da6d8361a593fa62

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
raw_fallback, observed 2026-08-07T10:17:52.629945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.300944Z digest=sha256:5652cae7796b4c30327edfb29cb10e65ef2341d7962018d58b23697d1979f823

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

Resolution
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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.308823Z digest=sha256:cd59438f37ea64c8a13af3c648f89084f89872cc6cf83db7e713668fe92c7250

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.311574Z digest=sha256:d3dae819d15457dc462589cfdf8498128e0ba97e89998e239448d90c005616f8

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
raw_fallback, observed 2026-08-07T10:17:52.603733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.316434Z digest=sha256:98e64ab42f304ab967a0ffdaedf47051d0e614b830c2e1ada55f0819af188b82

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
raw_fallback, observed 2026-08-07T10:17:52.596045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.318725Z digest=sha256:f51537b622e9e8aa12394a6ac0cdfd0312edfaa005867dd33c5502bac4bc0adf

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
raw_fallback, observed 2026-08-07T10:17:52.588328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.325764Z digest=sha256:07b5e311d1c997ddd33581d00b05e6f5659ac5e0732a7155aed4bdc03469a866

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.328197Z digest=sha256:937cff3229bdf23000b9817d29d6b8efb82d26d2e303294175d3bd4abab14ff2

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.330804Z digest=sha256:0154a09f37439ec91ac9a56d6a79771d67c7de2092af124c175276601dbc0029

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.333271Z digest=sha256:7e37fd1fd49b8ebd7749084c52c6b710a9eefb9ea97dc67420504675e05ae688

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.335783Z digest=sha256:b5e28c2d1e2751a605ed646caac0c0e1ddcc1592733f78da0ab0ab4b3668bede

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.338049Z digest=sha256:4cd372e085069e4954dab3243d6fb9794786a3befb539ea4a445b21eb74d6268

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.

source=arxiv_source observed=2026-08-07T10:17:52.340504Z digest=sha256:0b1768480c7e5a5c792b7ca53b001f13a649039ef27417aa4c3976099a298c7e

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
raw_fallback, observed 2026-08-07T10:17:52.537814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.342961Z digest=sha256:bf234f24878162a7eb746cc603a12ad29a41b01b5e463ef8c58d60d2fcc23832

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.348361Z digest=sha256:27feda27897415cafeb20421d1619a3a68ff3a7c383fa2651ec0eba3164e0952

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.350978Z digest=sha256:3c3f0289faf291d6351969f8a33a80104fa8922ede0eb761b3e393e6fd7cc112

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.353477Z digest=sha256:bbf4d69b9228886aaa83c19262413fdcdffa25d34643193687d66d2c6babdcd0

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.355734Z digest=sha256:4987e28767c2a53b56f660dd406745889e4d481189ba52c847b82377c2e2a627

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.357967Z digest=sha256:acdd8af186cb9555218446204f13455a70529e7038af8bc6ca2e7c4aca6eeba1

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.360305Z digest=sha256:11fc096f0384f0f62a24f0b2e2469e9b5a068377de6ee3e117a06aa06e1a6025

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.362643Z digest=sha256:98f0f89a7d8da4f717d4bfedc6f4e805a9d10b36819cc6a57c09659f6eb9046d

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.365195Z digest=sha256:428704ad83165de1e6fa20bb260b4d515e140d2a1e648129c73383ab5554ac71

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.367533Z digest=sha256:cd2a6edeb497620066ab5a581ce8e9ec8c3d42d9254c8616beeb9b588ad76298

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.369879Z digest=sha256:eaa46944b4ab92e492d840b81f367b60f375a0903f1ab7a59f87c49a6bb278ee

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.372709Z digest=sha256:ea2a3fbd9f48a2a2d018339a4bbd2cee8a7354d5b52cdeb04d7f4f9a6a5b4818

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:17:52.375631Z digest=sha256:78483cffbf47a4888b30cc86e38fe650785260e5daee83ddc57b9591bc828235

Pith citing papers

No inbound Pith citation observations are available.