{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:MNYIM642Q6NTEYQ45RCQQABBMF","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9104e57c1abb6c897d4c4762694821d896fc43beb0f5d2426dc0b8874a3a1949","cross_cats_sorted":["stat.CO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-02T18:11:15Z","title_canon_sha256":"4ffcc48dd8836b3f82589b2913f1e8284684a4af3a3eda928fb5de375cf4c15d"},"schema_version":"1.0","source":{"id":"2409.01410","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.01410","created_at":"2026-07-05T09:02:28Z"},{"alias_kind":"arxiv_version","alias_value":"2409.01410v1","created_at":"2026-07-05T09:02:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.01410","created_at":"2026-07-05T09:02:28Z"},{"alias_kind":"pith_short_12","alias_value":"MNYIM642Q6NT","created_at":"2026-07-05T09:02:28Z"},{"alias_kind":"pith_short_16","alias_value":"MNYIM642Q6NTEYQ4","created_at":"2026-07-05T09:02:28Z"},{"alias_kind":"pith_short_8","alias_value":"MNYIM642","created_at":"2026-07-05T09:02:28Z"}],"graph_snapshots":[{"event_id":"sha256:00842bce64f66bc2ed75e52f63110ddcac5d8d6d7e3ce3d837d7f791125d852d","target":"graph","created_at":"2026-07-05T09:02:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2409.01410/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dataset distillation (DD) is an increasingly important technique that focuses on constructing a synthetic dataset capable of capturing the core information in training data to achieve comparable performance in models trained on the latter. While DD has a wide range of applications, the theory supporting it is less well evolved. New methods of DD are compared on a common set of benchmarks, rather than oriented towards any particular learning task. In this work, we present a formal model of DD, arguing that a precise characterization of the underlying optimization problem must specify the infere","authors_text":"Anthony Quinn, Fadwa Idlahcen, Jianyang Gu, Saeed Vahidian, Vyacheslav Kungurtsev, Yiran Chen, Yuanfang Peng","cross_cats":["stat.CO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-02T18:11:15Z","title":"Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.01410","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d94b8e385d1d9670e370f56585e3b98c42cdcd454ba8a08f5757d34c08369249","target":"record","created_at":"2026-07-05T09:02:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9104e57c1abb6c897d4c4762694821d896fc43beb0f5d2426dc0b8874a3a1949","cross_cats_sorted":["stat.CO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-02T18:11:15Z","title_canon_sha256":"4ffcc48dd8836b3f82589b2913f1e8284684a4af3a3eda928fb5de375cf4c15d"},"schema_version":"1.0","source":{"id":"2409.01410","kind":"arxiv","version":1}},"canonical_sha256":"6370867b9a879b32621cec4508002161774204fece8c271abdb2bc8885600ef0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6370867b9a879b32621cec4508002161774204fece8c271abdb2bc8885600ef0","first_computed_at":"2026-07-05T09:02:28.744439Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:02:28.744439Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EPt3il8fCEfMr/TfKwzm6RVbd9bmQl2amvKjwPj35QIfG/eU2Zx/eM5UUWtXKyFZNyAb0UyAFeMC6TkhLx+fDA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:02:28.744883Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.01410","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d94b8e385d1d9670e370f56585e3b98c42cdcd454ba8a08f5757d34c08369249","sha256:00842bce64f66bc2ed75e52f63110ddcac5d8d6d7e3ce3d837d7f791125d852d"],"state_sha256":"4ab38c9a8f80a0278fc85c56d52bcec46f55903e0434e7609ee40d421ee06e55"}