{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:RSRZBGTUHQD5KSD5CMSLFEWDXV","short_pith_number":"pith:RSRZBGTU","canonical_record":{"source":{"id":"2203.02485","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-04T18:31:23Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8417db0ac0b49dc6fd8b85d93d06888eb433adf277f1ad6f217742647b10fa46","abstract_canon_sha256":"34ebb46f6d31d4c682abb2bbf7b3adc5b671de21314c14c804cd53b209f50da5"},"schema_version":"1.0"},"canonical_sha256":"8ca3909a743c07d5487d1324b292c3bd52557dcc0be45c77a33d474ed4605696","source":{"kind":"arxiv","id":"2203.02485","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.02485","created_at":"2026-07-05T04:02:11Z"},{"alias_kind":"arxiv_version","alias_value":"2203.02485v1","created_at":"2026-07-05T04:02:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.02485","created_at":"2026-07-05T04:02:11Z"},{"alias_kind":"pith_short_12","alias_value":"RSRZBGTUHQD5","created_at":"2026-07-05T04:02:11Z"},{"alias_kind":"pith_short_16","alias_value":"RSRZBGTUHQD5KSD5","created_at":"2026-07-05T04:02:11Z"},{"alias_kind":"pith_short_8","alias_value":"RSRZBGTU","created_at":"2026-07-05T04:02:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:RSRZBGTUHQD5KSD5CMSLFEWDXV","target":"record","payload":{"canonical_record":{"source":{"id":"2203.02485","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-04T18:31:23Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8417db0ac0b49dc6fd8b85d93d06888eb433adf277f1ad6f217742647b10fa46","abstract_canon_sha256":"34ebb46f6d31d4c682abb2bbf7b3adc5b671de21314c14c804cd53b209f50da5"},"schema_version":"1.0"},"canonical_sha256":"8ca3909a743c07d5487d1324b292c3bd52557dcc0be45c77a33d474ed4605696","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:02:11.138815Z","signature_b64":"8gmQxp4qxVxuWRM5HQnvYUdkbIxsaXGvSCR65OEwSYUsluUv1JlM4yM6bYIXc1chv8v0mkhkIDgBs1mZZCT4Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ca3909a743c07d5487d1324b292c3bd52557dcc0be45c77a33d474ed4605696","last_reissued_at":"2026-07-05T04:02:11.138369Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:02:11.138369Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.02485","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":"2026-07-05T04:02:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JKNK5it/tK9+teeNeW6Z62YxW6IpVSo12ijQAQLGSyjzSx4pQThWjEQzP/W9zG8S6db6OaN81N+Od03VhArRAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T16:18:34.251374Z"},"content_sha256":"125b8811cb1c3e132c2ef02240280615ec2d21a102aaa808fbc6d58d077c0c46","schema_version":"1.0","event_id":"sha256:125b8811cb1c3e132c2ef02240280615ec2d21a102aaa808fbc6d58d077c0c46"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:RSRZBGTUHQD5KSD5CMSLFEWDXV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Better Supervisory Signals by Observing Learning Paths","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Danica J. Sutherland, Shangmin Guo, Yi Ren","submitted_at":"2022-03-04T18:31:23Z","abstract_excerpt":"Better-supervised models might have better performance. In this paper, we first clarify what makes for good supervision for a classification problem, and then explain two existing label refining methods, label smoothing and knowledge distillation, in terms of our proposed criterion. To further answer why and how better supervision emerges, we observe the learning path, i.e., the trajectory of the model's predictions during training, for each training sample. We find that the model can spontaneously refine \"bad\" labels through a \"zig-zag\" learning path, which occurs on both toy and real dataset"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.02485","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/2203.02485/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":"2026-07-05T04:02:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7hDMzYoCSL6kgWm/+SP5mJkaDnMRlmSN5p/TYQh0i++yncCMAt5kNEiLwQRWLANdcik1KbVgweQxGAY01XoSDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T16:18:34.251886Z"},"content_sha256":"80e9e9dcb8cf77540d1d274c0ad3b725844620c25344f24dde06997743ff5a02","schema_version":"1.0","event_id":"sha256:80e9e9dcb8cf77540d1d274c0ad3b725844620c25344f24dde06997743ff5a02"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RSRZBGTUHQD5KSD5CMSLFEWDXV/bundle.json","state_url":"https://pith.science/pith/RSRZBGTUHQD5KSD5CMSLFEWDXV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RSRZBGTUHQD5KSD5CMSLFEWDXV/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":"2026-08-23T16:18:34Z","links":{"resolver":"https://pith.science/pith/RSRZBGTUHQD5KSD5CMSLFEWDXV","bundle":"https://pith.science/pith/RSRZBGTUHQD5KSD5CMSLFEWDXV/bundle.json","state":"https://pith.science/pith/RSRZBGTUHQD5KSD5CMSLFEWDXV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RSRZBGTUHQD5KSD5CMSLFEWDXV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:RSRZBGTUHQD5KSD5CMSLFEWDXV","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":"34ebb46f6d31d4c682abb2bbf7b3adc5b671de21314c14c804cd53b209f50da5","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-04T18:31:23Z","title_canon_sha256":"8417db0ac0b49dc6fd8b85d93d06888eb433adf277f1ad6f217742647b10fa46"},"schema_version":"1.0","source":{"id":"2203.02485","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.02485","created_at":"2026-07-05T04:02:11Z"},{"alias_kind":"arxiv_version","alias_value":"2203.02485v1","created_at":"2026-07-05T04:02:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.02485","created_at":"2026-07-05T04:02:11Z"},{"alias_kind":"pith_short_12","alias_value":"RSRZBGTUHQD5","created_at":"2026-07-05T04:02:11Z"},{"alias_kind":"pith_short_16","alias_value":"RSRZBGTUHQD5KSD5","created_at":"2026-07-05T04:02:11Z"},{"alias_kind":"pith_short_8","alias_value":"RSRZBGTU","created_at":"2026-07-05T04:02:11Z"}],"graph_snapshots":[{"event_id":"sha256:80e9e9dcb8cf77540d1d274c0ad3b725844620c25344f24dde06997743ff5a02","target":"graph","created_at":"2026-07-05T04:02:11Z","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/2203.02485/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Better-supervised models might have better performance. In this paper, we first clarify what makes for good supervision for a classification problem, and then explain two existing label refining methods, label smoothing and knowledge distillation, in terms of our proposed criterion. To further answer why and how better supervision emerges, we observe the learning path, i.e., the trajectory of the model's predictions during training, for each training sample. We find that the model can spontaneously refine \"bad\" labels through a \"zig-zag\" learning path, which occurs on both toy and real dataset","authors_text":"Danica J. Sutherland, Shangmin Guo, Yi Ren","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-04T18:31:23Z","title":"Better Supervisory Signals by Observing Learning Paths"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.02485","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":"sha256:125b8811cb1c3e132c2ef02240280615ec2d21a102aaa808fbc6d58d077c0c46","target":"record","created_at":"2026-07-05T04:02:11Z","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":"34ebb46f6d31d4c682abb2bbf7b3adc5b671de21314c14c804cd53b209f50da5","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-04T18:31:23Z","title_canon_sha256":"8417db0ac0b49dc6fd8b85d93d06888eb433adf277f1ad6f217742647b10fa46"},"schema_version":"1.0","source":{"id":"2203.02485","kind":"arxiv","version":1}},"canonical_sha256":"8ca3909a743c07d5487d1324b292c3bd52557dcc0be45c77a33d474ed4605696","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8ca3909a743c07d5487d1324b292c3bd52557dcc0be45c77a33d474ed4605696","first_computed_at":"2026-07-05T04:02:11.138369Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:02:11.138369Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8gmQxp4qxVxuWRM5HQnvYUdkbIxsaXGvSCR65OEwSYUsluUv1JlM4yM6bYIXc1chv8v0mkhkIDgBs1mZZCT4Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:02:11.138815Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.02485","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:125b8811cb1c3e132c2ef02240280615ec2d21a102aaa808fbc6d58d077c0c46","sha256:80e9e9dcb8cf77540d1d274c0ad3b725844620c25344f24dde06997743ff5a02"],"state_sha256":"9fde53c904ede8654a371a4d3532c84fc1d3b062eb0b09f630f7c78bcdb28585"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nXUshniAPJc5qE4Nli13/zbGsp/N92BuXG68FkAhrmn40SC94ujy8IccVGY2QbbNr9zddUGVyn5xRiK+mSzWAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T16:18:34.256022Z","bundle_sha256":"9c2605cb45ef872d3cb1b2447a91ef1b5c0cec2aa65267305cf9cfe119c92308"}}