{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:5KK3EWDFTQLC5PVJMFWYJHSQNR","short_pith_number":"pith:5KK3EWDF","canonical_record":{"source":{"id":"2409.01793","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-03T11:17:44Z","cross_cats_sorted":[],"title_canon_sha256":"1285b5be03f7007a1d9cfbf2d824bdf98b5979a939e041f2e425ca893c0ad7c3","abstract_canon_sha256":"cae1653e2ae06002783cf7e314be333c48ac03714e818dc7ee021709df29f801"},"schema_version":"1.0"},"canonical_sha256":"ea95b258659c162ebea9616d849e506c51d2f7c4f3658f9a85ba7d558dcb4d74","source":{"kind":"arxiv","id":"2409.01793","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.01793","created_at":"2026-07-05T11:49:04Z"},{"alias_kind":"arxiv_version","alias_value":"2409.01793v1","created_at":"2026-07-05T11:49:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.01793","created_at":"2026-07-05T11:49:04Z"},{"alias_kind":"pith_short_12","alias_value":"5KK3EWDFTQLC","created_at":"2026-07-05T11:49:04Z"},{"alias_kind":"pith_short_16","alias_value":"5KK3EWDFTQLC5PVJ","created_at":"2026-07-05T11:49:04Z"},{"alias_kind":"pith_short_8","alias_value":"5KK3EWDF","created_at":"2026-07-05T11:49:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:5KK3EWDFTQLC5PVJMFWYJHSQNR","target":"record","payload":{"canonical_record":{"source":{"id":"2409.01793","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-03T11:17:44Z","cross_cats_sorted":[],"title_canon_sha256":"1285b5be03f7007a1d9cfbf2d824bdf98b5979a939e041f2e425ca893c0ad7c3","abstract_canon_sha256":"cae1653e2ae06002783cf7e314be333c48ac03714e818dc7ee021709df29f801"},"schema_version":"1.0"},"canonical_sha256":"ea95b258659c162ebea9616d849e506c51d2f7c4f3658f9a85ba7d558dcb4d74","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:04.119690Z","signature_b64":"uZhJUa+qtZ9XZEe78NsVdMw/O81Y0VRz+JyNYs8/O3rFZWRtdHfhzddFn6QEOhtm4MHVs34lZ7JJsOJTxf99AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea95b258659c162ebea9616d849e506c51d2f7c4f3658f9a85ba7d558dcb4d74","last_reissued_at":"2026-07-05T11:49:04.119208Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:04.119208Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.01793","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:49:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DpSl1+GPwEW1AOnOPZbSvq/+K+iNduJyg4jzClALn9V78OfVUgxvp/49I1ezxkQH2jCGBIfJsZPdk+c6LP9JBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T08:53:05.653446Z"},"content_sha256":"5929e84798b4ca87fe1a0cd1dc515a2ffd402e61a09d232b1fe5ef3689d9e8d9","schema_version":"1.0","event_id":"sha256:5929e84798b4ca87fe1a0cd1dc515a2ffd402e61a09d232b1fe5ef3689d9e8d9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:5KK3EWDFTQLC5PVJMFWYJHSQNR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Task Weighting through Gradient Projection for Multitask Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Christian Bohn, Hasan Tercan, Ido Freeman, Tobias Meisen","submitted_at":"2024-09-03T11:17:44Z","abstract_excerpt":"In multitask learning, conflicts between task gradients are a frequent issue degrading a model's training performance. This is commonly addressed by using the Gradient Projection algorithm PCGrad that often leads to faster convergence and improved performance metrics. In this work, we present a method to adapt this algorithm to simultaneously also perform task prioritization. Our approach differs from traditional task weighting performed by scaling task losses in that our weighting scheme applies only in cases where tasks are in conflict, but lets the training proceed unhindered otherwise. We "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.01793","kind":"arxiv","version":1},"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/2409.01793/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":"2026-07-05T11:49:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G3tLWHlJK05ZQ0zjJ+PLybMyDxQbz2qtX1ybEZcrccbAHnigS6TwLYVIz+gaIvCSFiefKpDz4Emu4zzprxE9Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T08:53:05.654338Z"},"content_sha256":"6583ed2c50a31b134f985f14ed83a80059668a531c41184b2aafecff0f8137f6","schema_version":"1.0","event_id":"sha256:6583ed2c50a31b134f985f14ed83a80059668a531c41184b2aafecff0f8137f6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5KK3EWDFTQLC5PVJMFWYJHSQNR/bundle.json","state_url":"https://pith.science/pith/5KK3EWDFTQLC5PVJMFWYJHSQNR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5KK3EWDFTQLC5PVJMFWYJHSQNR/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":"2026-08-08T08:53:05Z","links":{"resolver":"https://pith.science/pith/5KK3EWDFTQLC5PVJMFWYJHSQNR","bundle":"https://pith.science/pith/5KK3EWDFTQLC5PVJMFWYJHSQNR/bundle.json","state":"https://pith.science/pith/5KK3EWDFTQLC5PVJMFWYJHSQNR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5KK3EWDFTQLC5PVJMFWYJHSQNR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:5KK3EWDFTQLC5PVJMFWYJHSQNR","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":"cae1653e2ae06002783cf7e314be333c48ac03714e818dc7ee021709df29f801","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-03T11:17:44Z","title_canon_sha256":"1285b5be03f7007a1d9cfbf2d824bdf98b5979a939e041f2e425ca893c0ad7c3"},"schema_version":"1.0","source":{"id":"2409.01793","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.01793","created_at":"2026-07-05T11:49:04Z"},{"alias_kind":"arxiv_version","alias_value":"2409.01793v1","created_at":"2026-07-05T11:49:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.01793","created_at":"2026-07-05T11:49:04Z"},{"alias_kind":"pith_short_12","alias_value":"5KK3EWDFTQLC","created_at":"2026-07-05T11:49:04Z"},{"alias_kind":"pith_short_16","alias_value":"5KK3EWDFTQLC5PVJ","created_at":"2026-07-05T11:49:04Z"},{"alias_kind":"pith_short_8","alias_value":"5KK3EWDF","created_at":"2026-07-05T11:49:04Z"}],"graph_snapshots":[{"event_id":"sha256:6583ed2c50a31b134f985f14ed83a80059668a531c41184b2aafecff0f8137f6","target":"graph","created_at":"2026-07-05T11:49:04Z","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/2409.01793/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In multitask learning, conflicts between task gradients are a frequent issue degrading a model's training performance. This is commonly addressed by using the Gradient Projection algorithm PCGrad that often leads to faster convergence and improved performance metrics. In this work, we present a method to adapt this algorithm to simultaneously also perform task prioritization. Our approach differs from traditional task weighting performed by scaling task losses in that our weighting scheme applies only in cases where tasks are in conflict, but lets the training proceed unhindered otherwise. We ","authors_text":"Christian Bohn, Hasan Tercan, Ido Freeman, Tobias Meisen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-03T11:17:44Z","title":"Task Weighting through Gradient Projection for Multitask Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.01793","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":"sha256:5929e84798b4ca87fe1a0cd1dc515a2ffd402e61a09d232b1fe5ef3689d9e8d9","target":"record","created_at":"2026-07-05T11:49:04Z","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":"cae1653e2ae06002783cf7e314be333c48ac03714e818dc7ee021709df29f801","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-03T11:17:44Z","title_canon_sha256":"1285b5be03f7007a1d9cfbf2d824bdf98b5979a939e041f2e425ca893c0ad7c3"},"schema_version":"1.0","source":{"id":"2409.01793","kind":"arxiv","version":1}},"canonical_sha256":"ea95b258659c162ebea9616d849e506c51d2f7c4f3658f9a85ba7d558dcb4d74","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ea95b258659c162ebea9616d849e506c51d2f7c4f3658f9a85ba7d558dcb4d74","first_computed_at":"2026-07-05T11:49:04.119208Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:49:04.119208Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uZhJUa+qtZ9XZEe78NsVdMw/O81Y0VRz+JyNYs8/O3rFZWRtdHfhzddFn6QEOhtm4MHVs34lZ7JJsOJTxf99AA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:49:04.119690Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.01793","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5929e84798b4ca87fe1a0cd1dc515a2ffd402e61a09d232b1fe5ef3689d9e8d9","sha256:6583ed2c50a31b134f985f14ed83a80059668a531c41184b2aafecff0f8137f6"],"state_sha256":"6ae421d34e6ae3fd46c94c63cb5309c029a8584371ca6431d2702279084fd8d0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+2I3HGA0F8KLLBUFoIecYbGIzkHqrpe2yz7pnhzdnUmwdUENX+HLp/dINqh2JgQ3ZhvkCTotvZ4n5t/RT8kbBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T08:53:05.661238Z","bundle_sha256":"c66e78d99b363c3d451d5be384e816f366944285a9a8f5593aa2a82c2ceb2c29"}}