{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:ATUJFIXRURWCLKS6TDZMU2BIIX","short_pith_number":"pith:ATUJFIXR","canonical_record":{"source":{"id":"2502.11986","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-17T16:26:05Z","cross_cats_sorted":[],"title_canon_sha256":"3966b46f837568fe8403a4e4b06728f0767e70a9741524217e17bf2ef9304139","abstract_canon_sha256":"fe0ee231c43061b74ee938c9c871909a8f05e9114c59cb1ec3445ebd49e9ad3a"},"schema_version":"1.0"},"canonical_sha256":"04e892a2f1a46c25aa5e98f2ca682845c64408cf178a0f0b221bdbbe962cfb67","source":{"kind":"arxiv","id":"2502.11986","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.11986","created_at":"2026-07-05T10:52:19Z"},{"alias_kind":"arxiv_version","alias_value":"2502.11986v2","created_at":"2026-07-05T10:52:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.11986","created_at":"2026-07-05T10:52:19Z"},{"alias_kind":"pith_short_12","alias_value":"ATUJFIXRURWC","created_at":"2026-07-05T10:52:19Z"},{"alias_kind":"pith_short_16","alias_value":"ATUJFIXRURWCLKS6","created_at":"2026-07-05T10:52:19Z"},{"alias_kind":"pith_short_8","alias_value":"ATUJFIXR","created_at":"2026-07-05T10:52:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:ATUJFIXRURWCLKS6TDZMU2BIIX","target":"record","payload":{"canonical_record":{"source":{"id":"2502.11986","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-17T16:26:05Z","cross_cats_sorted":[],"title_canon_sha256":"3966b46f837568fe8403a4e4b06728f0767e70a9741524217e17bf2ef9304139","abstract_canon_sha256":"fe0ee231c43061b74ee938c9c871909a8f05e9114c59cb1ec3445ebd49e9ad3a"},"schema_version":"1.0"},"canonical_sha256":"04e892a2f1a46c25aa5e98f2ca682845c64408cf178a0f0b221bdbbe962cfb67","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:52:19.706210Z","signature_b64":"vR7dl5NbIKJHIqMSQfnGUNyjXwS0+3Sr7TfkH2hlANYjC6NZkWOXp+oQTd6Wiwm4tXCumkNnhg6VDPPeSyazAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"04e892a2f1a46c25aa5e98f2ca682845c64408cf178a0f0b221bdbbe962cfb67","last_reissued_at":"2026-07-05T10:52:19.705725Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:52:19.705725Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.11986","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":"2026-07-05T10:52:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rj63dtGI/dkems/AUr/OkdYuAr3qiRAbARkISPLniSKnZzOpDovMV/3Pz/HwWO8cynVemJTrhcKjXBOZtubpDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T01:04:09.998053Z"},"content_sha256":"9ecd7d8e825a19bd6ac36c8f80400b1fedfa180d8c3d3ebad976e4d05eceb451","schema_version":"1.0","event_id":"sha256:9ecd7d8e825a19bd6ac36c8f80400b1fedfa180d8c3d3ebad976e4d05eceb451"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:ATUJFIXRURWCLKS6TDZMU2BIIX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Selective Task Group Updates for Multi-Task Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Kuk-Jin Yoon, Wooseong Jeong","submitted_at":"2025-02-17T16:26:05Z","abstract_excerpt":"Multi-task learning enables the acquisition of task-generic knowledge by training multiple tasks within a unified architecture. However, training all tasks together in a single architecture can lead to performance degradation, known as negative transfer, which is a main concern in multi-task learning. Previous works have addressed this issue by optimizing the multi-task network through gradient manipulation or weighted loss adjustments. However, their optimization strategy focuses on addressing task imbalance in shared parameters, neglecting the learning of task-specific parameters. As a resul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.11986","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/2502.11986/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":"2026-07-05T10:52:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2ImhOZVHkYUnx1mFnOrfYxrBAfPieKM+pfhVHjuxSnCPav3H9vq/EEbdELgPT+NxtaveZYveK4iD3mwDzV0pDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T01:04:09.998527Z"},"content_sha256":"9a5c73ddb3bc0fed6a22a5a7f4b7eea643966a5eecb05ca25adbd5da93ec3863","schema_version":"1.0","event_id":"sha256:9a5c73ddb3bc0fed6a22a5a7f4b7eea643966a5eecb05ca25adbd5da93ec3863"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ATUJFIXRURWCLKS6TDZMU2BIIX/bundle.json","state_url":"https://pith.science/pith/ATUJFIXRURWCLKS6TDZMU2BIIX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ATUJFIXRURWCLKS6TDZMU2BIIX/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":"2026-08-06T01:04:10Z","links":{"resolver":"https://pith.science/pith/ATUJFIXRURWCLKS6TDZMU2BIIX","bundle":"https://pith.science/pith/ATUJFIXRURWCLKS6TDZMU2BIIX/bundle.json","state":"https://pith.science/pith/ATUJFIXRURWCLKS6TDZMU2BIIX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ATUJFIXRURWCLKS6TDZMU2BIIX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ATUJFIXRURWCLKS6TDZMU2BIIX","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":"fe0ee231c43061b74ee938c9c871909a8f05e9114c59cb1ec3445ebd49e9ad3a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-17T16:26:05Z","title_canon_sha256":"3966b46f837568fe8403a4e4b06728f0767e70a9741524217e17bf2ef9304139"},"schema_version":"1.0","source":{"id":"2502.11986","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.11986","created_at":"2026-07-05T10:52:19Z"},{"alias_kind":"arxiv_version","alias_value":"2502.11986v2","created_at":"2026-07-05T10:52:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.11986","created_at":"2026-07-05T10:52:19Z"},{"alias_kind":"pith_short_12","alias_value":"ATUJFIXRURWC","created_at":"2026-07-05T10:52:19Z"},{"alias_kind":"pith_short_16","alias_value":"ATUJFIXRURWCLKS6","created_at":"2026-07-05T10:52:19Z"},{"alias_kind":"pith_short_8","alias_value":"ATUJFIXR","created_at":"2026-07-05T10:52:19Z"}],"graph_snapshots":[{"event_id":"sha256:9a5c73ddb3bc0fed6a22a5a7f4b7eea643966a5eecb05ca25adbd5da93ec3863","target":"graph","created_at":"2026-07-05T10:52:19Z","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/2502.11986/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-task learning enables the acquisition of task-generic knowledge by training multiple tasks within a unified architecture. However, training all tasks together in a single architecture can lead to performance degradation, known as negative transfer, which is a main concern in multi-task learning. Previous works have addressed this issue by optimizing the multi-task network through gradient manipulation or weighted loss adjustments. However, their optimization strategy focuses on addressing task imbalance in shared parameters, neglecting the learning of task-specific parameters. As a resul","authors_text":"Kuk-Jin Yoon, Wooseong Jeong","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-17T16:26:05Z","title":"Selective Task Group Updates for Multi-Task Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.11986","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":"sha256:9ecd7d8e825a19bd6ac36c8f80400b1fedfa180d8c3d3ebad976e4d05eceb451","target":"record","created_at":"2026-07-05T10:52:19Z","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":"fe0ee231c43061b74ee938c9c871909a8f05e9114c59cb1ec3445ebd49e9ad3a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-17T16:26:05Z","title_canon_sha256":"3966b46f837568fe8403a4e4b06728f0767e70a9741524217e17bf2ef9304139"},"schema_version":"1.0","source":{"id":"2502.11986","kind":"arxiv","version":2}},"canonical_sha256":"04e892a2f1a46c25aa5e98f2ca682845c64408cf178a0f0b221bdbbe962cfb67","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"04e892a2f1a46c25aa5e98f2ca682845c64408cf178a0f0b221bdbbe962cfb67","first_computed_at":"2026-07-05T10:52:19.705725Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:52:19.705725Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vR7dl5NbIKJHIqMSQfnGUNyjXwS0+3Sr7TfkH2hlANYjC6NZkWOXp+oQTd6Wiwm4tXCumkNnhg6VDPPeSyazAA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:52:19.706210Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.11986","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9ecd7d8e825a19bd6ac36c8f80400b1fedfa180d8c3d3ebad976e4d05eceb451","sha256:9a5c73ddb3bc0fed6a22a5a7f4b7eea643966a5eecb05ca25adbd5da93ec3863"],"state_sha256":"31689e203f2beb3544d55be00a1b8d758fa250289e38c06dea84a87c227abe87"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T8aLz+KQUhq9X6lZeY8SRg6UL2bcl3WNp2sdWpXFeKT4E2rV+hbGqtmRIVFi6hBailt/wgYIzS7Oc/IuynYECg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T01:04:10.001679Z","bundle_sha256":"c5801dbea9dea33a1d58ec1415cfc87fe76aef4258df7bb5536006aa506f1e78"}}