{"as_of":"2026-08-14T22:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5677c757f89c86bc5804b38e251819547a85367bf8d45bb5d866589d95a4e2c2","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:17:52.375631Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.05867/citation-record","integrity":"/paper/2506.05867/integrity","json":"/paper/2506.05867/citation-record.json","paper":"/paper/2506.05867"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.269462Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.269462Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:663f3a379c5a0b48a8ecc1e3412a1d624a25910feaffb7a69621171bc9abdfd9","observation_id":"65767bce-2522-4ee6-baa1-f83852f5f9c8","resolution":{"observed_at":"2026-08-07T10:17:52.269462Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.697354Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":"34f07c15-5132-4c8e-97fb-3dc610f6e3be","year":2009},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.273123Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:0df3a5f866db022fa628050cd8b3c8ccb21a5d5c2aaa0f9e4589159a06ea9586","observation_id":"a3352cad-cb91-4d36-8ffb-2a11fa285563","resolution":{"observed_at":"2026-08-07T10:17:52.700450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.689061Z","title":"and Caruana, R","venue":null,"work_id":"ec38905e-7666-4d03-ae46-b88ba664df6b","year":2014},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.275830Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:baed0e4793b62f23570f07a57ad900250b9743be0485384e367434444ca6d7de","observation_id":"ca4d6fe1-091a-43b0-92ef-6513950ab037","resolution":{"observed_at":"2026-08-07T10:17:52.691739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.680841Z","title":"S., and Shah, M","venue":null,"work_id":"9c31da56-d6e3-4b75-a4d2-3b0d3caba314","year":2022},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.278641Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:92b194ac6c8987fd49cc67da9db692b11c559bd710b5dc55ac73b951ed4dc755","observation_id":"79ea31ee-28a9-49c1-b75d-8aa67e2740fa","resolution":{"observed_at":"2026-08-07T10:17:52.683356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.673107Z","title":"D., Steinke, T., Hayase, J., Cooper, A","venue":null,"work_id":"3aa9ec79-5bea-423b-b54f-b0a2c09247b3","year":2024},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.281668Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:be7e0ce4ba6b1e26afea33f06233d94abafb155bd4713204e8648826e6904d14","observation_id":"404fce44-8580-48cb-9a1f-fa82f0ed3d0e","resolution":{"observed_at":"2026-08-07T10:17:52.675768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.664101Z","title":"Reproducible scaling laws for contrastive language-image learning","venue":null,"work_id":"e5289505-1843-4539-90ef-a9a0c125f8b1","year":2023},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.284516Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:3c7791aaa50b852902a36a2fe103305f7d321f0c307d14b457dff092e7ca9e4c","observation_id":"e2879a84-505a-4ebd-91dc-b29acafcc43c","resolution":{"observed_at":"2026-08-07T10:17:52.667452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.655779Z","title":null,"venue":null,"work_id":"8059ac04-b734-4772-ad9e-6a839b39e8cc","year":2023},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.287235Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:b58a8599ffafd702a4c7ad09228f951bfe405cef8cf32379d597ef9e6ef57b6e","observation_id":"c9b3c0b2-6d62-439e-967c-26c4dc6bf4c2","resolution":{"observed_at":"2026-08-07T10:17:52.658279Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.289911Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.289911Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:632043e39c99e5654211c6d843820ce4f0d5fa89474b11f5e2b99d9ad83bf187","observation_id":"01de1bf5-2b2c-4404-8f49-45f482b895b1","resolution":{"observed_at":"2026-08-07T10:17:52.289911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.642833Z","title":null,"venue":null,"work_id":"8feba419-31a3-4fb8-b62f-dd37ec7adfd8","year":2010},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.293030Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:7ca61a0213401092268f948fc0b439e96b4cce76ead466a6888ccc32e9216059","observation_id":"d54164c9-636e-400e-bd26-c0bf917b4c73","resolution":{"observed_at":"2026-08-07T10:17:52.645746Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.11006","last_updated":"2020-03-02T12:12:43Z","snapshot_observed_at":"2026-08-14T02:55:22.370407Z","submitted_at":"2019-12-23T18:08:33Z","title":"Data-Free Adversarial Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.11006","snapshot_observed_at":"2026-08-07T10:17:52.295327Z","title":"Data-free adversarial distillation","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.295327Z"},"links":{"cited_paper":"/paper/1912.11006","citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:c537de93fc6374cca8c71655a75579908c88b5d7ea10c7772e39fa99bf45b71c","observation_id":"eff50830-4242-41bf-851b-fbef3aa3683c","resolution":{"observed_at":"2026-08-07T10:17:52.295327Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.634996Z","title":"H., Chechik, G., and Cohen-Or, D","venue":null,"work_id":"c718468a-5306-4683-a844-3fbb90fc2bd8","year":2023},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.298142Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:52a6703d55d868a178189ddc99c633dd47bdbaba5a6ca8c40f87ecab9b1d7ee2","observation_id":"298ecfff-e755-4e12-a46e-25f122db208f","resolution":{"observed_at":"2026-08-07T10:17:52.637682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.627204Z","title":"Is synthetic data from generative models ready for image recognition? In International Conference on Learning Representations (ICLR), 2023","venue":null,"work_id":"118b6517-595b-45a2-b0cb-671f2d53e3bc","year":2023},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.300944Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:8065085714b32f4ee4ec33620c9e36b1711e3aa677e433bb84805ade4ffa3570","observation_id":"ca11b5fe-0ab7-4033-930f-dc07c4d5a0fd","resolution":{"observed_at":"2026-08-07T10:17:52.629945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.303338Z","title":"Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.303338Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:656b923bd4318227282472ff4c1cf159bd18e5c5aa602a3cdc483ec26ad4242b","observation_id":"554d941c-8bed-4d7c-92e5-c854beea67cb","resolution":{"observed_at":"2026-08-07T10:17:52.303338Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-07-06T04:11:24.157003Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-07T10:17:52.305894Z","title":"Distilling the knowledge in a neural network","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.305894Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:33df5d90b3013c706232183ce83c474f82489f8fff055633f1c99ceba51fa1f3","observation_id":"315b3058-616f-4ea6-aee3-18fd71bce316","resolution":{"observed_at":"2026-08-07T10:17:52.305894Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00096","last_updated":"2025-03-14T16:52:55Z","snapshot_observed_at":"2026-08-13T10:01:51.646336Z","submitted_at":"2023-09-29T19:09:27Z","title":"Towards Few-Call Model Stealing via Active Self-Paced Knowledge Distillation and Diffusion-Based Image Generation","version":2},"cited_work":{"arxiv_id":"2310.00096","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.00096","snapshot_observed_at":"2026-08-07T10:17:52.413077Z","title":"Towards Few-Call Model Stealing via Active Self-Paced Knowledge Distillation and Diffusion-Based Image Generation","venue":"cs.CV","work_id":"9fbce763-5a15-42b6-bb8e-5eb41276ab83","year":2023},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.308823Z"},"links":{"cited_paper":"/paper/2310.00096","citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:3059d7aa50382695e656425168be48aa80846398c87ae787002056e733e09aeb","observation_id":"c4ef7c35-fb81-4a87-814d-9ff08d0d3a33","resolution":{"observed_at":"2026-08-07T10:17:52.418125Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.614559Z","title":"S., Parikh, A","venue":null,"work_id":"8a6560a1-f497-4574-b7c2-f8d9ba83319b","year":2020},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.311574Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:94ed47b66288416da7fafd4df3ec26743df610ca6a6aa19f1166d74330082e64","observation_id":"2774991d-2bfb-497b-a89f-692cbe4e4f98","resolution":{"observed_at":"2026-08-07T10:17:52.617109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.313685Z","title":"Improved precision and recall metric for assessing generative models","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.313685Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:fe13680f7d7ae23a9460b03b8b2ead4ef152d6139d83cb5a3c58d2230e483a6c","observation_id":"33a52715-39bf-45ab-b0ac-e09f2f53a7a7","resolution":{"observed_at":"2026-08-07T10:17:52.313685Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.601063Z","title":"Defending against machine learning model stealing attacks using deceptive perturbations","venue":null,"work_id":"008d4b59-07e2-4b33-aa90-9c8b9b74cc67","year":2019},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.316434Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:3aba41597ad658967b77417c1a9457b193f9756fde9a8725f1ce37f3d1d2fb71","observation_id":"a1602ed6-ee94-4c1c-b235-f1abe84c9fbd","resolution":{"observed_at":"2026-08-07T10:17:52.603733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.593264Z","title":"Not-safe-for-work dataset","venue":null,"work_id":"124bf919-3b75-46e4-9102-e450baad3326","year":2024},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.318725Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:18c267983d2a979da1f6d70ffff026ff3098c68fe6a18f977485cb23de2cf4eb","observation_id":"66a0e630-8eb8-4f02-8eab-30395d871ccb","resolution":{"observed_at":"2026-08-07T10:17:52.596045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.585634Z","title":"G., Fenu, S., and Starner, T","venue":null,"work_id":"ccdafda1-4030-4034-b912-57ec1a55d544","year":2017},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.325764Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:3b8fa80c57d7137393121e9462ca965edb9270658ae939f293489a539b3ac048","observation_id":"aa8371b3-332d-44da-9c86-a2073dd3e2a8","resolution":{"observed_at":"2026-08-07T10:17:52.588328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.577885Z","title":"How to steer your adversary: Targeted and efficient model stealing defenses with gradient redirection","venue":null,"work_id":"3de8c508-03be-4e76-a1ec-209e5a3eaf9f","year":2022},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.328197Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:1cd0ec0bb407d3279ac385b29d24dd60b05ea220b353a38b861ec465b846ec28","observation_id":"4b31d9e4-c976-4ab3-b261-8b0da53708e6","resolution":{"observed_at":"2026-08-07T10:17:52.580503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.570837Z","title":"and Storkey, A","venue":null,"work_id":"b8a6d97e-657e-45e4-9cb4-6f56eec160e5","year":2019},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.330804Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:36a7ad587c430b178ffa6419898f3ce033bda13c0638c9421119d37b263997c9","observation_id":"e24cda83-f2fa-4ff8-a104-0b8002209b21","resolution":{"observed_at":"2026-08-07T10:17:52.573252Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.562928Z","title":"I know what you trained last summer: A survey on stealing machine learning models and defences","venue":null,"work_id":"75e36f70-7e55-46be-ab11-c4a12052b6eb","year":2023},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.333271Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:646a16793af41cf6439b9ebf7e05b3d2344039e73182d69e5436cc5092382ed2","observation_id":"e550151b-8d4b-4af9-ac03-53c456615405","resolution":{"observed_at":"2026-08-07T10:17:52.565606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.555262Z","title":"Knockoff nets: Stealing functionality of black-box models","venue":null,"work_id":"e1a6c1b7-31fc-46e7-9e44-582c1eefec7e","year":2019},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.335783Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:fd81f8c1ec3bf8c67f648b6b8770483c1f995e347c48f53f09f888a75f1d254d","observation_id":"ce0dff98-9030-4df3-b8e2-8d8795630f1d","resolution":{"observed_at":"2026-08-07T10:17:52.558133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.547638Z","title":"Moment matching for multi-source domain adaptation","venue":null,"work_id":"562bdd25-aff0-481a-81ea-f25b0fe8b3c8","year":2019},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.338049Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:c688a97ba32a4ce811a2fbd7390b1ad7b4ad554ea22d71d5194fdb4f41f9a475","observation_id":"0feb3b10-535a-41e0-a5a9-47836e6fcc03","resolution":{"observed_at":"2026-08-07T10:17:52.550454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.340504Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.340504Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:a28663402663c74b802a3ac2932136f522631f9e21cbf2482e10d89972898906","observation_id":"f5c61e3d-1c20-49ec-9b59-2980ed458afa","resolution":{"observed_at":"2026-08-07T10:17:52.340504Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.535354Z","title":null,"venue":null,"work_id":"fac1b37a-824f-44aa-9120-58703e08ae6a","year":2022},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.342961Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:7d041e7d47e3a0134acf6223a0dff984320a6510b19ec977dc9ae68bf7b92b4e","observation_id":"ef3660f9-43ff-49b5-a23a-21802d3efd42","resolution":{"observed_at":"2026-08-07T10:17:52.537814Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.12872","last_updated":"2024-03-30T06:59:35Z","snapshot_observed_at":"2026-08-13T20:00:03.664890Z","submitted_at":"2023-07-24T15:10:22Z","title":"Latent Code Augmentation Based on Stable Diffusion for Data-free Substitute Attacks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.12872","snapshot_observed_at":"2026-08-07T10:17:52.345745Z","title":"Data-free black-box attack based on diffusion model","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.345745Z"},"links":{"cited_paper":"/paper/2307.12872","citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:fbde3f57ca584b59666ad188ce743598035c5bf4462f16a1e16bba6afa119002","observation_id":"ab0b77f0-5735-4cd9-beee-3541cbbc495f","resolution":{"observed_at":"2026-08-07T10:17:52.345745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.527922Z","title":"Medical multimodal model stealing attacks via adversarial domain alignment","venue":null,"work_id":"fc3f2dc6-6872-400c-94e2-5b5bdda8b8b6","year":2025},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.348361Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:2ee7c4b22a78b1ccbd8edb9fd9afa73684c0197536931ffd370e815f4f316f03","observation_id":"ec80da1d-a561-4f0e-a82d-dc8eeaff5a0b","resolution":{"observed_at":"2026-08-07T10:17:52.530747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.519936Z","title":"Not-safe-for-work image detection","venue":null,"work_id":"65f26840-8e31-442a-bbce-9b7d7a221770","year":2023},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.350978Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:6d9bad1eec4081bd31d9f590f0d3010653026148d2cdf776567b70fe3e413ec2","observation_id":"20d70eb4-a174-469e-8560-a0fe176c3534","resolution":{"observed_at":"2026-08-07T10:17:52.523180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.512313Z","title":"Effective data augmentation with diffusion models","venue":null,"work_id":"612343d3-3ee3-4256-8ad1-c2609e1f8919","year":2024},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.353477Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:3492dd310320c80fc6b4e9df341450a4a8be8c65a1d4574436052ce12bd71108","observation_id":"1857ba9d-a4b5-485c-a4b6-56faf261984b","resolution":{"observed_at":"2026-08-07T10:17:52.514960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.504142Z","title":"K., and Ristenpart, T","venue":null,"work_id":"efc4a3a8-bbbc-4df6-ab5b-6d646684abc3","year":2016},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.355734Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:0fa819d62c08f8cb42f6cb3318ac9b87384767a8e6c93f9fdeebe22b08542990","observation_id":"b2f22048-1e46-4a6e-9afd-5102e80462e7","resolution":{"observed_at":"2026-08-07T10:17:52.506632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.496881Z","title":"J., and Papernot, N","venue":null,"work_id":"3cc747d6-7442-4bb3-858c-72144068fcdd","year":2021},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.357967Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:ec57ef2bea0d44fce9e997b73161f7fae84eebcdf1d1f07b559b5582a7b4a966","observation_id":"ead408df-a715-49a0-a6ff-fd383985270b","resolution":{"observed_at":"2026-08-07T10:17:52.499322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.489523Z","title":"S., Linmans, J., Winkens, J., Cohen, T., and Welling, M","venue":null,"work_id":"3c4014e9-3f41-4209-94a2-46241608820c","year":2018},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.360305Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:ff29f991d292be990471e98372f7b528429faca7ead96e45b218245fc193af8c","observation_id":"d0b19736-606c-4c9b-8cb8-db0188cd8923","resolution":{"observed_at":"2026-08-07T10:17:52.492187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.481770Z","title":"and Gong, N","venue":null,"work_id":"04cdbaa3-7b12-4e8e-917f-17100088e821","year":2018},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.362643Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:d14a837e42ed06d59d7a182a147f55b9e5e9bcd2b8ab9dc316a418f78dfdf09d","observation_id":"7dd2663b-56b2-4221-9550-32de68ceeb78","resolution":{"observed_at":"2026-08-07T10:17:52.484282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.473843Z","title":"Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery","venue":null,"work_id":"97ccb5e3-d780-4a26-be5d-595bde047835","year":2024},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.365195Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:f8f3282e8c50558794a35ca0f9c2d1af201cc9afcceea4e0926323e562d3aa92","observation_id":"4542a3a9-e405-4849-a393-4a6f239761d0","resolution":{"observed_at":"2026-08-07T10:17:52.476512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.465981Z","title":"J., Jordan, M., and Duchi, J","venue":null,"work_id":"5c8f8282-9a0e-4b78-a05c-ed0923513259","year":2012},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.367533Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:9597419be6b4db2cfaf606e4f001758b909876d82bc5ed288b1e15dabcf5e95e","observation_id":"5e98fe4e-d778-4243-98c6-b36888c5c637","resolution":{"observed_at":"2026-08-07T10:17:52.468922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.458232Z","title":"Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification","venue":null,"work_id":"59b8afe3-90f5-4208-9db3-1cf976a43490","year":2023},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.369879Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:80ae5af69cbbe602618fe1b1a36f1472b2e4629b2f87bb99b2c5354f1be7eb8f","observation_id":"482a8a03-e050-4459-b58b-986f23653945","resolution":{"observed_at":"2026-08-07T10:17:52.461084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.450010Z","title":"Genetic algorithms in search, optimization and machine learning","venue":null,"work_id":"6a736315-2d5f-4893-a6e5-58ed2a1aec02","year":1981},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.372709Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:997f11b644ccbc3cf620f9efb47b78af295fb97db7b54cdcd14da70961779d6a","observation_id":"67efd9de-4e1f-4125-a184-b37f224bc04b","resolution":{"observed_at":"2026-08-07T10:17:52.453171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:17:52.441426Z","title":"Stealthy imitation: Reward-guided environment-free policy stealing","venue":null,"work_id":"a7813cf3-d2d1-4d66-b407-dad995dcaeb6","year":2024},"citing_paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T10:17:52.375631Z"},"links":{"citing_paper":"/paper/2506.05867"},"observation_digest":"sha256:4d3d57d0eb0553128df11f9a7d2f0875cff6617dc716c368df90b240efe37df8","observation_id":"d136a642-4f25-4a98-afa2-d66bb4703382","resolution":{"observed_at":"2026-08-07T10:17:52.444303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.05867","last_updated":"2025-06-06T08:34:00Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-14T02:55:17.955242Z","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":1,"verified_fuzzy":28},"total_outbound_references":40},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.05867."}