{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:U7SXCBA4Y26GMBABEMZNJAKZBC","short_pith_number":"pith:U7SXCBA4","canonical_record":{"source":{"id":"2505.13900","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-20T04:03:52Z","cross_cats_sorted":[],"title_canon_sha256":"5b85ccd2e740fbb0881961445bbb05a2a9a6166802cc14fc813f60c926f1e70a","abstract_canon_sha256":"a707e103449b6654e3867edc5af0b65a9ad22a5033bb763ac742b7bb239c1527"},"schema_version":"1.0"},"canonical_sha256":"a7e571041cc6bc6604012332d4815908bf18c89b6f42bc0108cda9a1730a2852","source":{"kind":"arxiv","id":"2505.13900","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.13900","created_at":"2026-07-05T11:05:52Z"},{"alias_kind":"arxiv_version","alias_value":"2505.13900v1","created_at":"2026-07-05T11:05:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.13900","created_at":"2026-07-05T11:05:52Z"},{"alias_kind":"pith_short_12","alias_value":"U7SXCBA4Y26G","created_at":"2026-07-05T11:05:52Z"},{"alias_kind":"pith_short_16","alias_value":"U7SXCBA4Y26GMBAB","created_at":"2026-07-05T11:05:52Z"},{"alias_kind":"pith_short_8","alias_value":"U7SXCBA4","created_at":"2026-07-05T11:05:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:U7SXCBA4Y26GMBABEMZNJAKZBC","target":"record","payload":{"canonical_record":{"source":{"id":"2505.13900","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-20T04:03:52Z","cross_cats_sorted":[],"title_canon_sha256":"5b85ccd2e740fbb0881961445bbb05a2a9a6166802cc14fc813f60c926f1e70a","abstract_canon_sha256":"a707e103449b6654e3867edc5af0b65a9ad22a5033bb763ac742b7bb239c1527"},"schema_version":"1.0"},"canonical_sha256":"a7e571041cc6bc6604012332d4815908bf18c89b6f42bc0108cda9a1730a2852","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:05:52.626020Z","signature_b64":"NV4Yemr2IM3U/OJaUMojj4VbLFdgdTWJ9cl/85yKtXno/8aKNIT6h2u5EzE+s9SNcB16aqdrY9qJbEYfHB8zDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7e571041cc6bc6604012332d4815908bf18c89b6f42bc0108cda9a1730a2852","last_reissued_at":"2026-07-05T11:05:52.625588Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:05:52.625588Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.13900","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":"2026-07-05T11:05:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B6W7dIPctlo2QyG6+di1Jm57MZqhDhoZbWF3TR/VkoYu0+TYmJgtXf9oxm/Kta3C/FgTi+x35sfOiKJvI+C0Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T11:07:41.066625Z"},"content_sha256":"83e1333fdaeace3143e89da1692be53443d08f12a3f6d342babe03263b894f14","schema_version":"1.0","event_id":"sha256:83e1333fdaeace3143e89da1692be53443d08f12a3f6d342babe03263b894f14"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:U7SXCBA4Y26GMBABEMZNJAKZBC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"New Evidence of the Two-Phase Learning Dynamics of Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Junchi Yan, Mahito Sugiyama, Yongyi Yang, Zhanpeng Zhou","submitted_at":"2025-05-20T04:03:52Z","abstract_excerpt":"Understanding how deep neural networks learn remains a fundamental challenge in modern machine learning. A growing body of evidence suggests that training dynamics undergo a distinct phase transition, yet our understanding of this transition is still incomplete. In this paper, we introduce an interval-wise perspective that compares network states across a time window, revealing two new phenomena that illuminate the two-phase nature of deep learning. i) \\textbf{The Chaos Effect.} By injecting an imperceptibly small parameter perturbation at various stages, we show that the response of the netwo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.13900","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/2505.13900/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":"2026-07-05T11:05:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LJV1PocdhPu5xR9P0YwCXl4U5JilGhN2jbh4wCUBQ0ycWKo9xCvNrdbSuew9J1ukdQ6dM3In8CFcSEtYpRjJAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T11:07:41.067622Z"},"content_sha256":"42ef8df7da3ed29a8fb2c85c060c49bb61afd3a9abc53f4b8d4e4ae0a89b7b19","schema_version":"1.0","event_id":"sha256:42ef8df7da3ed29a8fb2c85c060c49bb61afd3a9abc53f4b8d4e4ae0a89b7b19"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U7SXCBA4Y26GMBABEMZNJAKZBC/bundle.json","state_url":"https://pith.science/pith/U7SXCBA4Y26GMBABEMZNJAKZBC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U7SXCBA4Y26GMBABEMZNJAKZBC/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":"2026-08-18T11:07:41Z","links":{"resolver":"https://pith.science/pith/U7SXCBA4Y26GMBABEMZNJAKZBC","bundle":"https://pith.science/pith/U7SXCBA4Y26GMBABEMZNJAKZBC/bundle.json","state":"https://pith.science/pith/U7SXCBA4Y26GMBABEMZNJAKZBC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U7SXCBA4Y26GMBABEMZNJAKZBC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:U7SXCBA4Y26GMBABEMZNJAKZBC","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":"a707e103449b6654e3867edc5af0b65a9ad22a5033bb763ac742b7bb239c1527","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-20T04:03:52Z","title_canon_sha256":"5b85ccd2e740fbb0881961445bbb05a2a9a6166802cc14fc813f60c926f1e70a"},"schema_version":"1.0","source":{"id":"2505.13900","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.13900","created_at":"2026-07-05T11:05:52Z"},{"alias_kind":"arxiv_version","alias_value":"2505.13900v1","created_at":"2026-07-05T11:05:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.13900","created_at":"2026-07-05T11:05:52Z"},{"alias_kind":"pith_short_12","alias_value":"U7SXCBA4Y26G","created_at":"2026-07-05T11:05:52Z"},{"alias_kind":"pith_short_16","alias_value":"U7SXCBA4Y26GMBAB","created_at":"2026-07-05T11:05:52Z"},{"alias_kind":"pith_short_8","alias_value":"U7SXCBA4","created_at":"2026-07-05T11:05:52Z"}],"graph_snapshots":[{"event_id":"sha256:42ef8df7da3ed29a8fb2c85c060c49bb61afd3a9abc53f4b8d4e4ae0a89b7b19","target":"graph","created_at":"2026-07-05T11:05:52Z","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/2505.13900/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Understanding how deep neural networks learn remains a fundamental challenge in modern machine learning. A growing body of evidence suggests that training dynamics undergo a distinct phase transition, yet our understanding of this transition is still incomplete. In this paper, we introduce an interval-wise perspective that compares network states across a time window, revealing two new phenomena that illuminate the two-phase nature of deep learning. i) \\textbf{The Chaos Effect.} By injecting an imperceptibly small parameter perturbation at various stages, we show that the response of the netwo","authors_text":"Junchi Yan, Mahito Sugiyama, Yongyi Yang, Zhanpeng Zhou","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-20T04:03:52Z","title":"New Evidence of the Two-Phase Learning Dynamics of Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.13900","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":"sha256:83e1333fdaeace3143e89da1692be53443d08f12a3f6d342babe03263b894f14","target":"record","created_at":"2026-07-05T11:05:52Z","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":"a707e103449b6654e3867edc5af0b65a9ad22a5033bb763ac742b7bb239c1527","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-20T04:03:52Z","title_canon_sha256":"5b85ccd2e740fbb0881961445bbb05a2a9a6166802cc14fc813f60c926f1e70a"},"schema_version":"1.0","source":{"id":"2505.13900","kind":"arxiv","version":1}},"canonical_sha256":"a7e571041cc6bc6604012332d4815908bf18c89b6f42bc0108cda9a1730a2852","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a7e571041cc6bc6604012332d4815908bf18c89b6f42bc0108cda9a1730a2852","first_computed_at":"2026-07-05T11:05:52.625588Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:05:52.625588Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NV4Yemr2IM3U/OJaUMojj4VbLFdgdTWJ9cl/85yKtXno/8aKNIT6h2u5EzE+s9SNcB16aqdrY9qJbEYfHB8zDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:05:52.626020Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.13900","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:83e1333fdaeace3143e89da1692be53443d08f12a3f6d342babe03263b894f14","sha256:42ef8df7da3ed29a8fb2c85c060c49bb61afd3a9abc53f4b8d4e4ae0a89b7b19"],"state_sha256":"41b75daa6a6b374784ddd0cf55787c3596e62520a89aea2daec00d64d1bb5f8c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"56o6DSw++e/NTp+KMtQTlaGBMj3ZJr8v6crnR3CQ2JNqavEavkJBREjstf9C90aZ4j9ah3iqUXTjb3oEzWT4DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T11:07:41.076368Z","bundle_sha256":"bdaa16f0d9d4a370042f726d727f98b143328df01ed368c54e3973c16394a15a"}}