{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IKYRAAGMCE7BNGNPOGBF5SEHFC","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":"20826de7057be8ae01b1d106c2aab26dbe9c8f8344d59f90a4c13ff51c907a01","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-05-30T06:58:42Z","title_canon_sha256":"15cc48c09c5a6595413c6db4d69ff9de320ce1377ee6a13cf06425b458a2a79c"},"schema_version":"1.0","source":{"id":"2505.24281","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.24281","created_at":"2026-07-05T11:12:45Z"},{"alias_kind":"arxiv_version","alias_value":"2505.24281v1","created_at":"2026-07-05T11:12:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.24281","created_at":"2026-07-05T11:12:45Z"},{"alias_kind":"pith_short_12","alias_value":"IKYRAAGMCE7B","created_at":"2026-07-05T11:12:45Z"},{"alias_kind":"pith_short_16","alias_value":"IKYRAAGMCE7BNGNP","created_at":"2026-07-05T11:12:45Z"},{"alias_kind":"pith_short_8","alias_value":"IKYRAAGM","created_at":"2026-07-05T11:12:45Z"}],"graph_snapshots":[{"event_id":"sha256:08e00b1dbb3d23cf2c9dd38918d7618ecc15870976988961dca671fff660ed1e","target":"graph","created_at":"2026-07-05T11:12:45Z","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/2505.24281/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-task learning (MTL) has become an essential machine learning tool for addressing multiple learning tasks simultaneously and has been effectively applied across fields such as healthcare, marketing, and biomedical research. However, to enable efficient information sharing across tasks, it is crucial to leverage both shared and heterogeneous information. Despite extensive research on MTL, various forms of heterogeneity, including distribution and posterior heterogeneity, present significant challenges. Existing methods often fail to address these forms of heterogeneity within a unified fra","authors_text":"Annie Qu, Qi Xu, Yang Bai, Yang Sui","cross_cats":["cs.LG","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-05-30T06:58:42Z","title":"Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.24281","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":"sha256:03a7fd2de3b9e131fcae40feccb1e3740fc57a1eacca7a75ac8ef7c43b76f93a","target":"record","created_at":"2026-07-05T11:12:45Z","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":"20826de7057be8ae01b1d106c2aab26dbe9c8f8344d59f90a4c13ff51c907a01","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-05-30T06:58:42Z","title_canon_sha256":"15cc48c09c5a6595413c6db4d69ff9de320ce1377ee6a13cf06425b458a2a79c"},"schema_version":"1.0","source":{"id":"2505.24281","kind":"arxiv","version":1}},"canonical_sha256":"42b11000cc113e1699af71825ec8872885e70ff1d5fc752b7ac18bc1a43853ac","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"42b11000cc113e1699af71825ec8872885e70ff1d5fc752b7ac18bc1a43853ac","first_computed_at":"2026-07-05T11:12:45.871112Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:12:45.871112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YIZJmRNPff+iRZPFJGLVXBX8nPiCA0mfif3BLaDgCevsThZXmZudhiKzNuLHic7mU1wu5cbqDoL9f4gUm5a2Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:12:45.871726Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.24281","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:03a7fd2de3b9e131fcae40feccb1e3740fc57a1eacca7a75ac8ef7c43b76f93a","sha256:08e00b1dbb3d23cf2c9dd38918d7618ecc15870976988961dca671fff660ed1e"],"state_sha256":"f8225f58fddbb4ed43e1d3463f0e71e0273edf983bc49debd17efce075898329"}