{"as_of":"2026-08-23T08:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1d36851c8e35ff7bc8501ec61dc7a5ee8c56945d3f20b9014f5c7631ecee0934","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-25T09:08:05.044020Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-25T09:10:33.830107Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2111.10130","last_updated":"2021-11-19T09:59:28Z","snapshot_observed_at":"2026-08-23T02:06:59.642041Z","submitted_at":"2021-11-19T09:59:28Z","title":"Fooling Adversarial Training with Inducing Noise","version":1},"cited_work":{"arxiv_id":"2111.10130","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.10130","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"author Wang, Y","venue":null,"work_id":"6ce4d4f0-8878-47ce-b354-ce468b600291","year":2021},"citing_paper":{"arxiv_id":"2406.02883","last_updated":"2026-05-22T16:09:51Z","snapshot_observed_at":"2026-08-14T11:25:05.331044Z","submitted_at":"2024-06-05T03:00:47Z","title":"Nonlinear Transformations Against Unlearnable Datasets","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-05-25T09:08:05.044020Z"},"links":{"cited_paper":"/paper/2111.10130","citing_paper":"/paper/2406.02883"},"observation_digest":"sha256:12b449ef5309d8d9714a1ab55f129d731262bae3ce8cc79fedee930880cefb45","observation_id":"5fc15372-a469-4506-ac64-b2976f86e5ee","resolution":{"observed_at":"2026-05-25T09:10:33.833212Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.10130","last_updated":"2021-11-19T09:59:28Z","snapshot_observed_at":"2026-08-23T02:06:59.642041Z","submitted_at":"2021-11-19T09:59:28Z","title":"Fooling Adversarial Training with Inducing Noise","version":1},"cited_work":{"arxiv_id":"2111.10130","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.10130","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"author Wang, Y","venue":null,"work_id":"6ce4d4f0-8878-47ce-b354-ce468b600291","year":2021},"citing_paper":{"arxiv_id":"2605.05224","last_updated":"2026-04-18T11:00:56Z","snapshot_observed_at":"2026-08-15T12:43:07.735001Z","submitted_at":"2026-04-18T11:00:56Z","title":"Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-10T07:07:13.072332Z"},"links":{"cited_paper":"/paper/2111.10130","citing_paper":"/paper/2605.05224"},"observation_digest":"sha256:e4bbed38cb343da33214b62bea815f6b5ec4edc058554aba2fcdcc40c7a55062","observation_id":"979f2881-708a-463f-9c5e-98b451b146d1","resolution":{"observed_at":"2026-05-10T07:11:53.508272Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.10130","last_updated":"2021-11-19T09:59:28Z","snapshot_observed_at":"2026-08-23T02:06:59.642041Z","submitted_at":"2021-11-19T09:59:28Z","title":"Fooling Adversarial Training with Inducing Noise","version":1},"cited_work":{"arxiv_id":"2111.10130","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.10130","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"author Wang, Y","venue":null,"work_id":"6ce4d4f0-8878-47ce-b354-ce468b600291","year":2021},"citing_paper":{"arxiv_id":"2605.12792","last_updated":"2026-05-12T22:10:01Z","snapshot_observed_at":"2026-07-06T23:24:27.821980Z","submitted_at":"2026-05-12T22:10:01Z","title":"SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-05-14T20:41:10.931383Z"},"links":{"cited_paper":"/paper/2111.10130","citing_paper":"/paper/2605.12792"},"observation_digest":"sha256:72d5397ec80bdd3e052bc128b3337f6f8326ef17ef0a7d75120fe85aebc174fb","observation_id":"1612a680-556d-411d-b362-1cdbbee79fdd","resolution":{"observed_at":"2026-05-14T20:42:57.390800Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2111.10130/citation-record","integrity":"/paper/2111.10130/integrity","json":"/paper/2111.10130/citation-record.json","paper":"/paper/2111.10130"},"outbound":[],"paper":{"arxiv_id":"2111.10130","last_updated":"2021-11-19T09:59:28Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-23T02:06:59.642041Z","submitted_at":"2021-11-19T09:59:28Z","title":"Fooling Adversarial Training with Inducing Noise"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2111.10130."}