{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WNUMPSP3PYHXATVPQHGA2LZJAD","short_pith_number":"pith:WNUMPSP3","schema_version":"1.0","canonical_sha256":"b368c7c9fb7e0f704eaf81cc0d2f2900da99c944a3983338c8b2b6355fcf88a0","source":{"kind":"arxiv","id":"2605.20606","version":1},"attestation_state":"computed","paper":{"title":"Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hang Gou, Ke Qin, Ming Li, Muquan Li, Tao He, Yihong Huang, Yingyi Ma, Yuan-Fang Li","submitted_at":"2026-05-20T01:49:39Z","abstract_excerpt":"Dataset distillation (DD) compresses a large training set into a small synthetic set for efficient training, but most DD methods optimize only clean accuracy and leave robustness uncontrolled. Recent robust DD methods improve robustness, yet they often suffer from a poor accuracy-robustness trade-off because they (i) treat all adversarially perturbed examples uniformly, despite robust risk being dominated by near-zero robust margins, and (ii) do not explicitly increase inter-class separation in the decision boundary where attacks concentrate. We present Contrastive Curriculum for Robust Datase"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.20606","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-20T01:49:39Z","cross_cats_sorted":[],"title_canon_sha256":"b9ac42e8b222f47b8a3d75a27fe563aa2892082d757ed6445f0422e6bdd3c82f","abstract_canon_sha256":"23385aadd46ee297d8d77a540966d620460ec12403afc9e00827541496ab696f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T01:04:44.328468Z","signature_b64":"MPNb+qYx2E4KPL9qhCsFdmzvidS6A4B9pj2hUHhTCOtQdQVnHSPJNbjBytz9VUmqvFGliMgFVyhB7xq7qyX6Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b368c7c9fb7e0f704eaf81cc0d2f2900da99c944a3983338c8b2b6355fcf88a0","last_reissued_at":"2026-05-21T01:04:44.327811Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T01:04:44.327811Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hang Gou, Ke Qin, Ming Li, Muquan Li, Tao He, Yihong Huang, Yingyi Ma, Yuan-Fang Li","submitted_at":"2026-05-20T01:49:39Z","abstract_excerpt":"Dataset distillation (DD) compresses a large training set into a small synthetic set for efficient training, but most DD methods optimize only clean accuracy and leave robustness uncontrolled. Recent robust DD methods improve robustness, yet they often suffer from a poor accuracy-robustness trade-off because they (i) treat all adversarially perturbed examples uniformly, despite robust risk being dominated by near-zero robust margins, and (ii) do not explicitly increase inter-class separation in the decision boundary where attacks concentrate. We present Contrastive Curriculum for Robust Datase"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.20606","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.20606/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.20606","created_at":"2026-05-21T01:04:44.327909+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.20606v1","created_at":"2026-05-21T01:04:44.327909+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.20606","created_at":"2026-05-21T01:04:44.327909+00:00"},{"alias_kind":"pith_short_12","alias_value":"WNUMPSP3PYHX","created_at":"2026-05-21T01:04:44.327909+00:00"},{"alias_kind":"pith_short_16","alias_value":"WNUMPSP3PYHXATVP","created_at":"2026-05-21T01:04:44.327909+00:00"},{"alias_kind":"pith_short_8","alias_value":"WNUMPSP3","created_at":"2026-05-21T01:04:44.327909+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WNUMPSP3PYHXATVPQHGA2LZJAD","json":"https://pith.science/pith/WNUMPSP3PYHXATVPQHGA2LZJAD.json","graph_json":"https://pith.science/api/pith-number/WNUMPSP3PYHXATVPQHGA2LZJAD/graph.json","events_json":"https://pith.science/api/pith-number/WNUMPSP3PYHXATVPQHGA2LZJAD/events.json","paper":"https://pith.science/paper/WNUMPSP3"},"agent_actions":{"view_html":"https://pith.science/pith/WNUMPSP3PYHXATVPQHGA2LZJAD","download_json":"https://pith.science/pith/WNUMPSP3PYHXATVPQHGA2LZJAD.json","view_paper":"https://pith.science/paper/WNUMPSP3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.20606&json=true","fetch_graph":"https://pith.science/api/pith-number/WNUMPSP3PYHXATVPQHGA2LZJAD/graph.json","fetch_events":"https://pith.science/api/pith-number/WNUMPSP3PYHXATVPQHGA2LZJAD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WNUMPSP3PYHXATVPQHGA2LZJAD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WNUMPSP3PYHXATVPQHGA2LZJAD/action/storage_attestation","attest_author":"https://pith.science/pith/WNUMPSP3PYHXATVPQHGA2LZJAD/action/author_attestation","sign_citation":"https://pith.science/pith/WNUMPSP3PYHXATVPQHGA2LZJAD/action/citation_signature","submit_replication":"https://pith.science/pith/WNUMPSP3PYHXATVPQHGA2LZJAD/action/replication_record"}},"created_at":"2026-05-21T01:04:44.327909+00:00","updated_at":"2026-05-21T01:04:44.327909+00:00"}