{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:AJN75YBAAAM5EL6VQF7PY62LWV","short_pith_number":"pith:AJN75YBA","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"},"canonical_sha256":"025bfee0200019d22fd5817efc7b4bb550f8b6ca55b3845fc197493b0dfe75b6","source":{"kind":"arxiv","id":"2501.10945","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.10945","created_at":"2026-07-05T11:49:06Z"},{"alias_kind":"arxiv_version","alias_value":"2501.10945v3","created_at":"2026-07-05T11:49:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10945","created_at":"2026-07-05T11:49:06Z"},{"alias_kind":"pith_short_12","alias_value":"AJN75YBAAAM5","created_at":"2026-07-05T11:49:06Z"},{"alias_kind":"pith_short_16","alias_value":"AJN75YBAAAM5EL6V","created_at":"2026-07-05T11:49:06Z"},{"alias_kind":"pith_short_8","alias_value":"AJN75YBA","created_at":"2026-07-05T11:49:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:AJN75YBAAAM5EL6VQF7PY62LWV","target":"record","payload":{"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"},"canonical_sha256":"025bfee0200019d22fd5817efc7b4bb550f8b6ca55b3845fc197493b0dfe75b6","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"},"source_kind":"arxiv","source_id":"2501.10945","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:49:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nLHTJApleACXvH4soUmvFdz+Zaay4NXBqtlMVGmYViu08kCSuINjz9mJdeSYsZzrZWrahIbin68fIhD+RpB4DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T13:29:40.677743Z"},"content_sha256":"3cd794bf95649af973c8c8318e76c15b4d07d3f6e156e393bc26b3d4e31bcc6f","schema_version":"1.0","event_id":"sha256:3cd794bf95649af973c8c8318e76c15b4d07d3f6e156e393bc26b3d4e31bcc6f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:AJN75YBAAAM5EL6VQF7PY62LWV","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:49:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XCbTz4H+BMR52/Z8LAWCbad7HLhSKhdy95J5AXDPg8dIRUMwzswGxa69+wOJDpifBPqA66T4JbhfbkAtVZfhCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T13:29:40.678265Z"},"content_sha256":"66bd46988dd6467c67b41d8de46aa2e3b8662ae0d9a71f2cae8de6940aa5424a","schema_version":"1.0","event_id":"sha256:66bd46988dd6467c67b41d8de46aa2e3b8662ae0d9a71f2cae8de6940aa5424a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AJN75YBAAAM5EL6VQF7PY62LWV/bundle.json","state_url":"https://pith.science/pith/AJN75YBAAAM5EL6VQF7PY62LWV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AJN75YBAAAM5EL6VQF7PY62LWV/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":"2026-08-09T13:29:40Z","links":{"resolver":"https://pith.science/pith/AJN75YBAAAM5EL6VQF7PY62LWV","bundle":"https://pith.science/pith/AJN75YBAAAM5EL6VQF7PY62LWV/bundle.json","state":"https://pith.science/pith/AJN75YBAAAM5EL6VQF7PY62LWV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AJN75YBAAAM5EL6VQF7PY62LWV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:AJN75YBAAAM5EL6VQF7PY62LWV","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":"dc2f623458452b7ec3fe25ae9e8323502eafbbd125f7c71018c52a4b75fc446a","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-19T04:56:55Z","title_canon_sha256":"e6a4e7900c5b62c1fc83440f93c1efcc1bec479b7b44c8bac1d11313d9e6db97"},"schema_version":"1.0","source":{"id":"2501.10945","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.10945","created_at":"2026-07-05T11:49:06Z"},{"alias_kind":"arxiv_version","alias_value":"2501.10945v3","created_at":"2026-07-05T11:49:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10945","created_at":"2026-07-05T11:49:06Z"},{"alias_kind":"pith_short_12","alias_value":"AJN75YBAAAM5","created_at":"2026-07-05T11:49:06Z"},{"alias_kind":"pith_short_16","alias_value":"AJN75YBAAAM5EL6V","created_at":"2026-07-05T11:49:06Z"},{"alias_kind":"pith_short_8","alias_value":"AJN75YBA","created_at":"2026-07-05T11:49:06Z"}],"graph_snapshots":[{"event_id":"sha256:66bd46988dd6467c67b41d8de46aa2e3b8662ae0d9a71f2cae8de6940aa5424a","target":"graph","created_at":"2026-07-05T11:49:06Z","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/2501.10945/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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 ","authors_text":"Baijiong Lin, Han Zhao, James T. Kwok, Qingfu Zhang, Weiyu Chen, Xiaoyuan Zhang, Xi Lin","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-19T04:56:55Z","title":"Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10945","kind":"arxiv","version":3},"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":"sha256:3cd794bf95649af973c8c8318e76c15b4d07d3f6e156e393bc26b3d4e31bcc6f","target":"record","created_at":"2026-07-05T11:49:06Z","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":"dc2f623458452b7ec3fe25ae9e8323502eafbbd125f7c71018c52a4b75fc446a","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-19T04:56:55Z","title_canon_sha256":"e6a4e7900c5b62c1fc83440f93c1efcc1bec479b7b44c8bac1d11313d9e6db97"},"schema_version":"1.0","source":{"id":"2501.10945","kind":"arxiv","version":3}},"canonical_sha256":"025bfee0200019d22fd5817efc7b4bb550f8b6ca55b3845fc197493b0dfe75b6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"025bfee0200019d22fd5817efc7b4bb550f8b6ca55b3845fc197493b0dfe75b6","first_computed_at":"2026-07-05T11:49:06.316000Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:49:06.316000Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"g78t7v7nOoNYFkA0SWAbGCu0MaoartKm284OXFmNX7D4co1+APDZtdhusa4FaaS64cyyPBqgxNXTQ0zm283bDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:49:06.316523Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.10945","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3cd794bf95649af973c8c8318e76c15b4d07d3f6e156e393bc26b3d4e31bcc6f","sha256:66bd46988dd6467c67b41d8de46aa2e3b8662ae0d9a71f2cae8de6940aa5424a"],"state_sha256":"10da8004b015ec22d01d795e1b59907d2c8c772b314bc8b1a76a3e36da3817bc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0IumsR2w/9HuhwAVByS7u72AUhRdLqhOMgCMxoVcf1GLSp/ra37yArkWPvn9+x8j7KrhbCiHpDXJ2JbTK055Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T13:29:40.681706Z","bundle_sha256":"fab010fcac8422efdbdd75987f2e3592092714eb4cba3ba11e76a31f06cb4388"}}