{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:DYUV5IC55YOZI3SKHD5GEBWPST","short_pith_number":"pith:DYUV5IC5","canonical_record":{"source":{"id":"2411.16658","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-11-25T18:42:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4344ca850e218de556a3cfc788be42cafc3cabc8583e879bf0e33f13cd66e4e1","abstract_canon_sha256":"15730defe3f2a6887b7c7b046fe638674e6db66c0877e3935003ea23f718338e"},"schema_version":"1.0"},"canonical_sha256":"1e295ea05dee1d946e4a38fa6206cf94e417e52d28f5c198ceb75bb4a56128ab","source":{"kind":"arxiv","id":"2411.16658","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.16658","created_at":"2026-07-05T09:40:12Z"},{"alias_kind":"arxiv_version","alias_value":"2411.16658v1","created_at":"2026-07-05T09:40:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.16658","created_at":"2026-07-05T09:40:12Z"},{"alias_kind":"pith_short_12","alias_value":"DYUV5IC55YOZ","created_at":"2026-07-05T09:40:12Z"},{"alias_kind":"pith_short_16","alias_value":"DYUV5IC55YOZI3SK","created_at":"2026-07-05T09:40:12Z"},{"alias_kind":"pith_short_8","alias_value":"DYUV5IC5","created_at":"2026-07-05T09:40:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:DYUV5IC55YOZI3SKHD5GEBWPST","target":"record","payload":{"canonical_record":{"source":{"id":"2411.16658","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-11-25T18:42:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4344ca850e218de556a3cfc788be42cafc3cabc8583e879bf0e33f13cd66e4e1","abstract_canon_sha256":"15730defe3f2a6887b7c7b046fe638674e6db66c0877e3935003ea23f718338e"},"schema_version":"1.0"},"canonical_sha256":"1e295ea05dee1d946e4a38fa6206cf94e417e52d28f5c198ceb75bb4a56128ab","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:12.494631Z","signature_b64":"TaeV9UqfilbeM0QWorhlzY2I6Gj6mRzuTzMLdp6/Y4CiHAmxDzwPq9LRjPsMgbdS8CAQS6LFKN8Oy+snzi0lDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e295ea05dee1d946e4a38fa6206cf94e417e52d28f5c198ceb75bb4a56128ab","last_reissued_at":"2026-07-05T09:40:12.494192Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:12.494192Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.16658","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:40:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xO7gX1HkxTxlE2ekIDwAefIbzE4ywNV4WnmJXwMtwya1XKbXFZxP0dIhx+UKzpX9s62rF+GFb4J4gpYm+dlxCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T10:42:52.246805Z"},"content_sha256":"3eb0d8bb4303564946b630dd5351f4c56971bdf91e642161b1a8911078e32e11","schema_version":"1.0","event_id":"sha256:3eb0d8bb4303564946b630dd5351f4c56971bdf91e642161b1a8911078e32e11"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:DYUV5IC55YOZI3SKHD5GEBWPST","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast training of large kernel models with delayed projections","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit, Siyuan Ma","submitted_at":"2024-11-25T18:42:13Z","abstract_excerpt":"Classical kernel machines have historically faced significant challenges in scaling to large datasets and model sizes--a key ingredient that has driven the success of neural networks. In this paper, we present a new methodology for building kernel machines that can scale efficiently with both data size and model size. Our algorithm introduces delayed projections to Preconditioned Stochastic Gradient Descent (PSGD) allowing the training of much larger models than was previously feasible, pushing the practical limits of kernel-based learning. We validate our algorithm, EigenPro4, across multiple"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.16658","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/2411.16658/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:40:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W21UyVi8kZvlXDdQrm/8K9dckpARrBf2IRZJBfCsG95Ccfb1wg0PHPvwOkKHgH2ipO3MS2qXnJ2+dIg9B6L/Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T10:42:52.247351Z"},"content_sha256":"68dbe172bf5478cb1138ec87ccf650d0ec85ddafe8228876f2ec227e3cb4866b","schema_version":"1.0","event_id":"sha256:68dbe172bf5478cb1138ec87ccf650d0ec85ddafe8228876f2ec227e3cb4866b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DYUV5IC55YOZI3SKHD5GEBWPST/bundle.json","state_url":"https://pith.science/pith/DYUV5IC55YOZI3SKHD5GEBWPST/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DYUV5IC55YOZI3SKHD5GEBWPST/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-13T10:42:52Z","links":{"resolver":"https://pith.science/pith/DYUV5IC55YOZI3SKHD5GEBWPST","bundle":"https://pith.science/pith/DYUV5IC55YOZI3SKHD5GEBWPST/bundle.json","state":"https://pith.science/pith/DYUV5IC55YOZI3SKHD5GEBWPST/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DYUV5IC55YOZI3SKHD5GEBWPST/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:DYUV5IC55YOZI3SKHD5GEBWPST","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":"15730defe3f2a6887b7c7b046fe638674e6db66c0877e3935003ea23f718338e","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-11-25T18:42:13Z","title_canon_sha256":"4344ca850e218de556a3cfc788be42cafc3cabc8583e879bf0e33f13cd66e4e1"},"schema_version":"1.0","source":{"id":"2411.16658","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.16658","created_at":"2026-07-05T09:40:12Z"},{"alias_kind":"arxiv_version","alias_value":"2411.16658v1","created_at":"2026-07-05T09:40:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.16658","created_at":"2026-07-05T09:40:12Z"},{"alias_kind":"pith_short_12","alias_value":"DYUV5IC55YOZ","created_at":"2026-07-05T09:40:12Z"},{"alias_kind":"pith_short_16","alias_value":"DYUV5IC55YOZI3SK","created_at":"2026-07-05T09:40:12Z"},{"alias_kind":"pith_short_8","alias_value":"DYUV5IC5","created_at":"2026-07-05T09:40:12Z"}],"graph_snapshots":[{"event_id":"sha256:68dbe172bf5478cb1138ec87ccf650d0ec85ddafe8228876f2ec227e3cb4866b","target":"graph","created_at":"2026-07-05T09:40:12Z","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/2411.16658/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Classical kernel machines have historically faced significant challenges in scaling to large datasets and model sizes--a key ingredient that has driven the success of neural networks. In this paper, we present a new methodology for building kernel machines that can scale efficiently with both data size and model size. Our algorithm introduces delayed projections to Preconditioned Stochastic Gradient Descent (PSGD) allowing the training of much larger models than was previously feasible, pushing the practical limits of kernel-based learning. We validate our algorithm, EigenPro4, across multiple","authors_text":"Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit, Siyuan Ma","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-11-25T18:42:13Z","title":"Fast training of large kernel models with delayed projections"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.16658","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":"sha256:3eb0d8bb4303564946b630dd5351f4c56971bdf91e642161b1a8911078e32e11","target":"record","created_at":"2026-07-05T09:40:12Z","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":"15730defe3f2a6887b7c7b046fe638674e6db66c0877e3935003ea23f718338e","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-11-25T18:42:13Z","title_canon_sha256":"4344ca850e218de556a3cfc788be42cafc3cabc8583e879bf0e33f13cd66e4e1"},"schema_version":"1.0","source":{"id":"2411.16658","kind":"arxiv","version":1}},"canonical_sha256":"1e295ea05dee1d946e4a38fa6206cf94e417e52d28f5c198ceb75bb4a56128ab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1e295ea05dee1d946e4a38fa6206cf94e417e52d28f5c198ceb75bb4a56128ab","first_computed_at":"2026-07-05T09:40:12.494192Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:40:12.494192Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TaeV9UqfilbeM0QWorhlzY2I6Gj6mRzuTzMLdp6/Y4CiHAmxDzwPq9LRjPsMgbdS8CAQS6LFKN8Oy+snzi0lDA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:40:12.494631Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.16658","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3eb0d8bb4303564946b630dd5351f4c56971bdf91e642161b1a8911078e32e11","sha256:68dbe172bf5478cb1138ec87ccf650d0ec85ddafe8228876f2ec227e3cb4866b"],"state_sha256":"fcdef7c71acf320cb702075d08be52568435f70e1e6bc9cd099169a03dc9cf46"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LYcmouHazJu2f49cpIcMAtIT8Cq8w8ZKzSLAT9RF20xNp2XsowR6/rq1lnPsDJXJwASW4EcAB3PtiB3Dslq7Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T10:42:52.252778Z","bundle_sha256":"87456ef842d38607b4ba58f23751bebf8ea1c3bf1af228d6d7c925e63bac4866"}}