{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:LVCBKI3RY4FKSD6M7JP2ULZFAT","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":"97badd419200140e5285ac3dd082604dd381f67423b9225265f8f87be49e61ff","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-24T18:19:40Z","title_canon_sha256":"ef1f312f15474da1dafaddcd7c0eac8d268d192166b629696979910d9d2a5cd4"},"schema_version":"1.0","source":{"id":"2605.25210","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.25210","created_at":"2026-05-26T02:04:23Z"},{"alias_kind":"arxiv_version","alias_value":"2605.25210v1","created_at":"2026-05-26T02:04:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.25210","created_at":"2026-05-26T02:04:23Z"},{"alias_kind":"pith_short_12","alias_value":"LVCBKI3RY4FK","created_at":"2026-05-26T02:04:23Z"},{"alias_kind":"pith_short_16","alias_value":"LVCBKI3RY4FKSD6M","created_at":"2026-05-26T02:04:23Z"},{"alias_kind":"pith_short_8","alias_value":"LVCBKI3R","created_at":"2026-05-26T02:04:23Z"}],"graph_snapshots":[{"event_id":"sha256:d8e169567d00a2a6882004474cd90a1caede71020c1e08a528ae7b93f5a3e1bc","target":"graph","created_at":"2026-05-26T02:04:23Z","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/2605.25210/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion models are increasingly used as powerful conditional generators, yet real deployments often involve multiple target distributions arising from different tasks, e.g., diverse prompt domains in text-to-image generation, or multiple environments in robotics with diffusion policies. This naturally leads to a multi-objective learning (MOL) problem. A key challenge is that achieving good Pareto trade-offs can require a generalist model class with substantially larger capacity than what suffices for solving any individual task, thereby increasing statistical cost since sample complexity typ","authors_text":"Hanlin Zhu, Haoran Geng, Jitendra Malik, Pieter Abbeel, Somayeh Sojoudi, Xin Guo, Yixiao Huang, Ziheng Cheng","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-24T18:19:40Z","title":"Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.25210","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":"sha256:7bc07b464ff1a4f34e77a4959eac3a4505d0f6765d113b5b802f51e6423b94eb","target":"record","created_at":"2026-05-26T02:04:23Z","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":"97badd419200140e5285ac3dd082604dd381f67423b9225265f8f87be49e61ff","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-24T18:19:40Z","title_canon_sha256":"ef1f312f15474da1dafaddcd7c0eac8d268d192166b629696979910d9d2a5cd4"},"schema_version":"1.0","source":{"id":"2605.25210","kind":"arxiv","version":1}},"canonical_sha256":"5d44152371c70aa90fccfa5faa2f2504e9e767c6f65d2dc44971aed3a40a5215","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5d44152371c70aa90fccfa5faa2f2504e9e767c6f65d2dc44971aed3a40a5215","first_computed_at":"2026-05-26T02:04:23.522646Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T02:04:23.522646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6zPUWpVjAaa4gMsHAmhKEl1xeFk6WQAT9fLxmuyhNbhQEhJ2sEwI5FJAM8+AqCL59zjB5BuaxZlMSoZTpvIEDw==","signature_status":"signed_v1","signed_at":"2026-05-26T02:04:23.523458Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.25210","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7bc07b464ff1a4f34e77a4959eac3a4505d0f6765d113b5b802f51e6423b94eb","sha256:d8e169567d00a2a6882004474cd90a1caede71020c1e08a528ae7b93f5a3e1bc"],"state_sha256":"6e0b8495bb3d297a809616687a3ae17a448b99dac27cbae6406c0cb0a381cf19"}