{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:LMYUTIHTS2GKYM55GU5ECQTD3W","short_pith_number":"pith:LMYUTIHT","canonical_record":{"source":{"id":"2504.09525","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2025-04-13T11:25:44Z","cross_cats_sorted":[],"title_canon_sha256":"22246b7c45db6e4a493402dfd7f0e53eab1b6c49243ee989fb138dc905abd5b8","abstract_canon_sha256":"dd633b75c943d6bc87849f084bf5d067336309b2c805da46b7d8ae74cd9ad380"},"schema_version":"1.0"},"canonical_sha256":"5b3149a0f3968cac33bd353a414263dd9f783b48206c57d498ac5a02209b42ae","source":{"kind":"arxiv","id":"2504.09525","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.09525","created_at":"2026-07-05T11:49:53Z"},{"alias_kind":"arxiv_version","alias_value":"2504.09525v2","created_at":"2026-07-05T11:49:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.09525","created_at":"2026-07-05T11:49:53Z"},{"alias_kind":"pith_short_12","alias_value":"LMYUTIHTS2GK","created_at":"2026-07-05T11:49:53Z"},{"alias_kind":"pith_short_16","alias_value":"LMYUTIHTS2GKYM55","created_at":"2026-07-05T11:49:53Z"},{"alias_kind":"pith_short_8","alias_value":"LMYUTIHT","created_at":"2026-07-05T11:49:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:LMYUTIHTS2GKYM55GU5ECQTD3W","target":"record","payload":{"canonical_record":{"source":{"id":"2504.09525","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2025-04-13T11:25:44Z","cross_cats_sorted":[],"title_canon_sha256":"22246b7c45db6e4a493402dfd7f0e53eab1b6c49243ee989fb138dc905abd5b8","abstract_canon_sha256":"dd633b75c943d6bc87849f084bf5d067336309b2c805da46b7d8ae74cd9ad380"},"schema_version":"1.0"},"canonical_sha256":"5b3149a0f3968cac33bd353a414263dd9f783b48206c57d498ac5a02209b42ae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:53.240877Z","signature_b64":"CFT9OjU5uBGuynGs9lNiL0P+HgTNCrbJAWtXeJ9jNaKKTjHlzcItqKjpSjGPn32AV/AZMez+F770Gcr4pgucDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b3149a0f3968cac33bd353a414263dd9f783b48206c57d498ac5a02209b42ae","last_reissued_at":"2026-07-05T11:49:53.240377Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:53.240377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.09525","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:49:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BQZHoZQIyAncz1nsxIxg2huy57w8nK7+ja0mKsM/LOylOnAfZPNfrhChnUqfe+KgZzsBokrf2HIywPhC2KUWAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T07:44:51.978553Z"},"content_sha256":"4c8127fd77bd9ed8f09e6573a9502ba7a6900a521582c89bb0c30186290b13c1","schema_version":"1.0","event_id":"sha256:4c8127fd77bd9ed8f09e6573a9502ba7a6900a521582c89bb0c30186290b13c1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:LMYUTIHTS2GKYM55GU5ECQTD3W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SimLabel: Similarity-Weighted Iterative Framework for Multi-annotator Learning with Missing Annotations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.MM","authors_text":"Hong Liu, Liyun Zhang, Takanori Takebe, Yuta Nakashima, Zheng Lian","submitted_at":"2025-04-13T11:25:44Z","abstract_excerpt":"Multi-annotator learning (MAL) aims to model annotator-specific labeling patterns. However, existing methods face a critical challenge: they simply skip updating annotator-specific model parameters when encountering missing labels, i.e., a common scenario in real-world crowdsourced datasets where each annotator labels only small subsets of samples. This leads to inefficient data utilization and overfitting risks. To this end, we propose a novel similarity-weighted semi-supervised learning framework (SimLabel) that leverages inter-annotator similarities to generate weighted soft labels for miss"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.09525","kind":"arxiv","version":2},"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/2504.09525/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":"2026-07-05T11:49:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RXe8OpV9lt0JGtX83bzqAc30BhIKpBFzdelV9JFrtirzkRvZOSIq36iwcmeb6cSf7DIsemYbophsMN9Y0DhYAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T07:44:51.979593Z"},"content_sha256":"2a46f72db70873ab2dc132d394f7b9ab0665a1b04b687b226979d59dcb4259aa","schema_version":"1.0","event_id":"sha256:2a46f72db70873ab2dc132d394f7b9ab0665a1b04b687b226979d59dcb4259aa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LMYUTIHTS2GKYM55GU5ECQTD3W/bundle.json","state_url":"https://pith.science/pith/LMYUTIHTS2GKYM55GU5ECQTD3W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LMYUTIHTS2GKYM55GU5ECQTD3W/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":"2026-08-14T07:44:51Z","links":{"resolver":"https://pith.science/pith/LMYUTIHTS2GKYM55GU5ECQTD3W","bundle":"https://pith.science/pith/LMYUTIHTS2GKYM55GU5ECQTD3W/bundle.json","state":"https://pith.science/pith/LMYUTIHTS2GKYM55GU5ECQTD3W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LMYUTIHTS2GKYM55GU5ECQTD3W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:LMYUTIHTS2GKYM55GU5ECQTD3W","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":"dd633b75c943d6bc87849f084bf5d067336309b2c805da46b7d8ae74cd9ad380","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2025-04-13T11:25:44Z","title_canon_sha256":"22246b7c45db6e4a493402dfd7f0e53eab1b6c49243ee989fb138dc905abd5b8"},"schema_version":"1.0","source":{"id":"2504.09525","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.09525","created_at":"2026-07-05T11:49:53Z"},{"alias_kind":"arxiv_version","alias_value":"2504.09525v2","created_at":"2026-07-05T11:49:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.09525","created_at":"2026-07-05T11:49:53Z"},{"alias_kind":"pith_short_12","alias_value":"LMYUTIHTS2GK","created_at":"2026-07-05T11:49:53Z"},{"alias_kind":"pith_short_16","alias_value":"LMYUTIHTS2GKYM55","created_at":"2026-07-05T11:49:53Z"},{"alias_kind":"pith_short_8","alias_value":"LMYUTIHT","created_at":"2026-07-05T11:49:53Z"}],"graph_snapshots":[{"event_id":"sha256:2a46f72db70873ab2dc132d394f7b9ab0665a1b04b687b226979d59dcb4259aa","target":"graph","created_at":"2026-07-05T11:49:53Z","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/2504.09525/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-annotator learning (MAL) aims to model annotator-specific labeling patterns. However, existing methods face a critical challenge: they simply skip updating annotator-specific model parameters when encountering missing labels, i.e., a common scenario in real-world crowdsourced datasets where each annotator labels only small subsets of samples. This leads to inefficient data utilization and overfitting risks. To this end, we propose a novel similarity-weighted semi-supervised learning framework (SimLabel) that leverages inter-annotator similarities to generate weighted soft labels for miss","authors_text":"Hong Liu, Liyun Zhang, Takanori Takebe, Yuta Nakashima, Zheng Lian","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2025-04-13T11:25:44Z","title":"SimLabel: Similarity-Weighted Iterative Framework for Multi-annotator Learning with Missing Annotations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.09525","kind":"arxiv","version":2},"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":"sha256:4c8127fd77bd9ed8f09e6573a9502ba7a6900a521582c89bb0c30186290b13c1","target":"record","created_at":"2026-07-05T11:49:53Z","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":"dd633b75c943d6bc87849f084bf5d067336309b2c805da46b7d8ae74cd9ad380","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2025-04-13T11:25:44Z","title_canon_sha256":"22246b7c45db6e4a493402dfd7f0e53eab1b6c49243ee989fb138dc905abd5b8"},"schema_version":"1.0","source":{"id":"2504.09525","kind":"arxiv","version":2}},"canonical_sha256":"5b3149a0f3968cac33bd353a414263dd9f783b48206c57d498ac5a02209b42ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5b3149a0f3968cac33bd353a414263dd9f783b48206c57d498ac5a02209b42ae","first_computed_at":"2026-07-05T11:49:53.240377Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:49:53.240377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CFT9OjU5uBGuynGs9lNiL0P+HgTNCrbJAWtXeJ9jNaKKTjHlzcItqKjpSjGPn32AV/AZMez+F770Gcr4pgucDw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:49:53.240877Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.09525","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4c8127fd77bd9ed8f09e6573a9502ba7a6900a521582c89bb0c30186290b13c1","sha256:2a46f72db70873ab2dc132d394f7b9ab0665a1b04b687b226979d59dcb4259aa"],"state_sha256":"be36bbce079fa8cfd31608bf22bc5ae33ae31d24c43dae31815e1b891a61e8cb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kw1TBep35z38VAwsK/BpgQvD0geG9A7h52dtRf5Ty1yLNjdsS/wTviHbqyz7UuDtR/klhPIC+fMhBbQ/7bQ1Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T07:44:51.987060Z","bundle_sha256":"4470ec4dd4a8c28889f81b0d88927683389a552e9712204aa2a4ce0e0ed69b89"}}