{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AJN75YBAAAM5EL6VQF7PY62LWV","short_pith_number":"pith:AJN75YBA","schema_version":"1.0","canonical_sha256":"025bfee0200019d22fd5817efc7b4bb550f8b6ca55b3845fc197493b0dfe75b6","source":{"kind":"arxiv","id":"2501.10945","version":3},"attestation_state":"computed","paper":{"title":"Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Baijiong Lin, Han Zhao, James T. Kwok, Qingfu Zhang, Weiyu Chen, Xiaoyuan Zhang, Xi Lin","submitted_at":"2025-01-19T04:56:55Z","abstract_excerpt":"Many modern deep learning applications require balancing multiple objectives that are often conflicting. Examples include multi-task learning, fairness-aware learning, and the alignment of Large Language Models (LLMs). This leads to multi-objective deep learning, which tries to find optimal trade-offs or Pareto-optimal solutions by adapting mathematical principles from the field of Multi-Objective Optimization (MOO). However, directly applying gradient-based MOO techniques to deep neural networks presents unique challenges, including high computational costs, optimization instability, and the "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.10945","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-19T04:56:55Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e6a4e7900c5b62c1fc83440f93c1efcc1bec479b7b44c8bac1d11313d9e6db97","abstract_canon_sha256":"dc2f623458452b7ec3fe25ae9e8323502eafbbd125f7c71018c52a4b75fc446a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:06.316523Z","signature_b64":"g78t7v7nOoNYFkA0SWAbGCu0MaoartKm284OXFmNX7D4co1+APDZtdhusa4FaaS64cyyPBqgxNXTQ0zm283bDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"025bfee0200019d22fd5817efc7b4bb550f8b6ca55b3845fc197493b0dfe75b6","last_reissued_at":"2026-07-05T11:49:06.316000Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:06.316000Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Baijiong Lin, Han Zhao, James T. Kwok, Qingfu Zhang, Weiyu Chen, Xiaoyuan Zhang, Xi Lin","submitted_at":"2025-01-19T04:56:55Z","abstract_excerpt":"Many modern deep learning applications require balancing multiple objectives that are often conflicting. Examples include multi-task learning, fairness-aware learning, and the alignment of Large Language Models (LLMs). This leads to multi-objective deep learning, which tries to find optimal trade-offs or Pareto-optimal solutions by adapting mathematical principles from the field of Multi-Objective Optimization (MOO). However, directly applying gradient-based MOO techniques to deep neural networks presents unique challenges, including high computational costs, optimization instability, and the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10945","kind":"arxiv","version":3},"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/2501.10945/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.10945","created_at":"2026-07-05T11:49:06.316063+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.10945v3","created_at":"2026-07-05T11:49:06.316063+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10945","created_at":"2026-07-05T11:49:06.316063+00:00"},{"alias_kind":"pith_short_12","alias_value":"AJN75YBAAAM5","created_at":"2026-07-05T11:49:06.316063+00:00"},{"alias_kind":"pith_short_16","alias_value":"AJN75YBAAAM5EL6V","created_at":"2026-07-05T11:49:06.316063+00:00"},{"alias_kind":"pith_short_8","alias_value":"AJN75YBA","created_at":"2026-07-05T11:49:06.316063+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01392","citing_title":"Multi-Objective Exploration and Preference Optimization via Mutual Information","ref_index":65,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03904","citing_title":"MAdam: Metric-Aware Multi-Objective Adam","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20619","citing_title":"SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2509.25414","citing_title":"Rethinking Parameter Sharing for LLM Fine-Tuning with Multiple LoRAs","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13875","citing_title":"Common-agency Games for Multi-Objective Test-Time Alignment","ref_index":191,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05660","citing_title":"Distributionally Robust Multi-Objective Optimization","ref_index":68,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18492","citing_title":"Barrier-enforced multi-objective optimization for direct point and sharp interval forecasting","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20685","citing_title":"MGDA-Decoupled: Geometry-Aware Multi-Objective Optimisation for DPO-based LLM Alignment","ref_index":56,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AJN75YBAAAM5EL6VQF7PY62LWV","json":"https://pith.science/pith/AJN75YBAAAM5EL6VQF7PY62LWV.json","graph_json":"https://pith.science/api/pith-number/AJN75YBAAAM5EL6VQF7PY62LWV/graph.json","events_json":"https://pith.science/api/pith-number/AJN75YBAAAM5EL6VQF7PY62LWV/events.json","paper":"https://pith.science/paper/AJN75YBA"},"agent_actions":{"view_html":"https://pith.science/pith/AJN75YBAAAM5EL6VQF7PY62LWV","download_json":"https://pith.science/pith/AJN75YBAAAM5EL6VQF7PY62LWV.json","view_paper":"https://pith.science/paper/AJN75YBA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.10945&json=true","fetch_graph":"https://pith.science/api/pith-number/AJN75YBAAAM5EL6VQF7PY62LWV/graph.json","fetch_events":"https://pith.science/api/pith-number/AJN75YBAAAM5EL6VQF7PY62LWV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AJN75YBAAAM5EL6VQF7PY62LWV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AJN75YBAAAM5EL6VQF7PY62LWV/action/storage_attestation","attest_author":"https://pith.science/pith/AJN75YBAAAM5EL6VQF7PY62LWV/action/author_attestation","sign_citation":"https://pith.science/pith/AJN75YBAAAM5EL6VQF7PY62LWV/action/citation_signature","submit_replication":"https://pith.science/pith/AJN75YBAAAM5EL6VQF7PY62LWV/action/replication_record"}},"created_at":"2026-07-05T11:49:06.316063+00:00","updated_at":"2026-07-05T11:49:06.316063+00:00"}