{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:NUJRS6YPNMDR2SB34BJUXFS4YH","short_pith_number":"pith:NUJRS6YP","canonical_record":{"source":{"id":"2504.13292","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-17T19:08:40Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a25704bd7bee06ceede8f9cb73750499edfd25c485689eebc22d4d7bca1f49b5","abstract_canon_sha256":"6adfe91db3f58486212ac337dcbfbb1f0a8fce6cf9cb692256efdc5a96826c96"},"schema_version":"1.0"},"canonical_sha256":"6d13197b0f6b071d483be0534b965cc1c285a49246e255356ac9db03dbb6681a","source":{"kind":"arxiv","id":"2504.13292","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.13292","created_at":"2026-07-05T10:50:52Z"},{"alias_kind":"arxiv_version","alias_value":"2504.13292v1","created_at":"2026-07-05T10:50:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.13292","created_at":"2026-07-05T10:50:52Z"},{"alias_kind":"pith_short_12","alias_value":"NUJRS6YPNMDR","created_at":"2026-07-05T10:50:52Z"},{"alias_kind":"pith_short_16","alias_value":"NUJRS6YPNMDR2SB3","created_at":"2026-07-05T10:50:52Z"},{"alias_kind":"pith_short_8","alias_value":"NUJRS6YP","created_at":"2026-07-05T10:50:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:NUJRS6YPNMDR2SB34BJUXFS4YH","target":"record","payload":{"canonical_record":{"source":{"id":"2504.13292","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-17T19:08:40Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a25704bd7bee06ceede8f9cb73750499edfd25c485689eebc22d4d7bca1f49b5","abstract_canon_sha256":"6adfe91db3f58486212ac337dcbfbb1f0a8fce6cf9cb692256efdc5a96826c96"},"schema_version":"1.0"},"canonical_sha256":"6d13197b0f6b071d483be0534b965cc1c285a49246e255356ac9db03dbb6681a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:50:52.721852Z","signature_b64":"FR1UqPPnUUWd5spGkkN+hV/YCNk4/jEEZE2LQkwg2VZlwxTqOdGZKonn+Y+vOJIl1sMWAMa4lUk4YT7MuxISDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d13197b0f6b071d483be0534b965cc1c285a49246e255356ac9db03dbb6681a","last_reissued_at":"2026-07-05T10:50:52.721367Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:50:52.721367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.13292","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":"2026-07-05T10:50:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bij5syBXcLGNIV0JRiBovNt5hJ7+xlIiiqCvEBm7BmAGfVvoX8AuXsjKqXy7xOPTb/NOz36skPazzRS1BAWiAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-25T18:43:16.926784Z"},"content_sha256":"3a8eb06f4e5e05307bdc16412f77cdca4c31037984640d609dc3b5fa8bd84b7f","schema_version":"1.0","event_id":"sha256:3a8eb06f4e5e05307bdc16412f77cdca4c31037984640d609dc3b5fa8bd84b7f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:NUJRS6YPNMDR2SB34BJUXFS4YH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Let Me Grok for You: Accelerating Grokking via Embedding Transfer from a Weaker Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Wei Hu, Yixin Wang, Zhiwei Xu, Zhiyu Ni","submitted_at":"2025-04-17T19:08:40Z","abstract_excerpt":"''Grokking'' is a phenomenon where a neural network first memorizes training data and generalizes poorly, but then suddenly transitions to near-perfect generalization after prolonged training. While intriguing, this delayed generalization phenomenon compromises predictability and efficiency. Ideally, models should generalize directly without delay. To this end, this paper proposes GrokTransfer, a simple and principled method for accelerating grokking in training neural networks, based on the key observation that data embedding plays a crucial role in determining whether generalization is delay"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.13292","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/2504.13292/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":"2026-07-05T10:50:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vb+tVuSedEFI0l74K512dPUjq4z0dN2R+rW2ckBSk0nZ7XyPyDLvZ1t4OMCgRTyPPoYXNSvefEYDsEKJ4qesAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-25T18:43:16.927144Z"},"content_sha256":"4e7728ef1956a855556c4ee235226a80642611fc679771ae23977755a42f5f03","schema_version":"1.0","event_id":"sha256:4e7728ef1956a855556c4ee235226a80642611fc679771ae23977755a42f5f03"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NUJRS6YPNMDR2SB34BJUXFS4YH/bundle.json","state_url":"https://pith.science/pith/NUJRS6YPNMDR2SB34BJUXFS4YH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NUJRS6YPNMDR2SB34BJUXFS4YH/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":"2026-07-25T18:43:16Z","links":{"resolver":"https://pith.science/pith/NUJRS6YPNMDR2SB34BJUXFS4YH","bundle":"https://pith.science/pith/NUJRS6YPNMDR2SB34BJUXFS4YH/bundle.json","state":"https://pith.science/pith/NUJRS6YPNMDR2SB34BJUXFS4YH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NUJRS6YPNMDR2SB34BJUXFS4YH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:NUJRS6YPNMDR2SB34BJUXFS4YH","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":"6adfe91db3f58486212ac337dcbfbb1f0a8fce6cf9cb692256efdc5a96826c96","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-17T19:08:40Z","title_canon_sha256":"a25704bd7bee06ceede8f9cb73750499edfd25c485689eebc22d4d7bca1f49b5"},"schema_version":"1.0","source":{"id":"2504.13292","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.13292","created_at":"2026-07-05T10:50:52Z"},{"alias_kind":"arxiv_version","alias_value":"2504.13292v1","created_at":"2026-07-05T10:50:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.13292","created_at":"2026-07-05T10:50:52Z"},{"alias_kind":"pith_short_12","alias_value":"NUJRS6YPNMDR","created_at":"2026-07-05T10:50:52Z"},{"alias_kind":"pith_short_16","alias_value":"NUJRS6YPNMDR2SB3","created_at":"2026-07-05T10:50:52Z"},{"alias_kind":"pith_short_8","alias_value":"NUJRS6YP","created_at":"2026-07-05T10:50:52Z"}],"graph_snapshots":[{"event_id":"sha256:4e7728ef1956a855556c4ee235226a80642611fc679771ae23977755a42f5f03","target":"graph","created_at":"2026-07-05T10:50:52Z","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/2504.13292/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"''Grokking'' is a phenomenon where a neural network first memorizes training data and generalizes poorly, but then suddenly transitions to near-perfect generalization after prolonged training. While intriguing, this delayed generalization phenomenon compromises predictability and efficiency. Ideally, models should generalize directly without delay. To this end, this paper proposes GrokTransfer, a simple and principled method for accelerating grokking in training neural networks, based on the key observation that data embedding plays a crucial role in determining whether generalization is delay","authors_text":"Wei Hu, Yixin Wang, Zhiwei Xu, Zhiyu Ni","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-17T19:08:40Z","title":"Let Me Grok for You: Accelerating Grokking via Embedding Transfer from a Weaker Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.13292","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":"sha256:3a8eb06f4e5e05307bdc16412f77cdca4c31037984640d609dc3b5fa8bd84b7f","target":"record","created_at":"2026-07-05T10:50:52Z","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":"6adfe91db3f58486212ac337dcbfbb1f0a8fce6cf9cb692256efdc5a96826c96","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-17T19:08:40Z","title_canon_sha256":"a25704bd7bee06ceede8f9cb73750499edfd25c485689eebc22d4d7bca1f49b5"},"schema_version":"1.0","source":{"id":"2504.13292","kind":"arxiv","version":1}},"canonical_sha256":"6d13197b0f6b071d483be0534b965cc1c285a49246e255356ac9db03dbb6681a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6d13197b0f6b071d483be0534b965cc1c285a49246e255356ac9db03dbb6681a","first_computed_at":"2026-07-05T10:50:52.721367Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:50:52.721367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FR1UqPPnUUWd5spGkkN+hV/YCNk4/jEEZE2LQkwg2VZlwxTqOdGZKonn+Y+vOJIl1sMWAMa4lUk4YT7MuxISDw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:50:52.721852Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.13292","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3a8eb06f4e5e05307bdc16412f77cdca4c31037984640d609dc3b5fa8bd84b7f","sha256:4e7728ef1956a855556c4ee235226a80642611fc679771ae23977755a42f5f03"],"state_sha256":"f29afa8768f37bcd90fb0ed9c71e6207d7355bf6c8eb02a7e1a810fcb33cd842"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2HBjxG5liD5s3G+lfB8oJsFS5zw9vpDk8pFMaD4svR0etUecUnV9UpF62gXTI/xqgTSqo3mSatD8F5tlBVrCBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-25T18:43:16.929580Z","bundle_sha256":"0c5d896b84e3c81e8b7cadd2b85f14c78298c8e7ff22759edba4507860592cf6"}}