{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:G4AYXAMFXXWMTK5EXQSL7QYFOM","short_pith_number":"pith:G4AYXAMF","canonical_record":{"source":{"id":"2405.07769","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-13T14:12:33Z","cross_cats_sorted":[],"title_canon_sha256":"bdfca2ee9fff8400e2fd87a7d8adee81e0a09e25488d73065867995ae72497f4","abstract_canon_sha256":"5925a2632f0921f40d87f29151bc4210b8e8fa6ec43e41f783d3e9ff40b7813d"},"schema_version":"1.0"},"canonical_sha256":"37018b8185bdecc9aba4bc24bfc305731409099d2e48976a2f6c874c8d26bfc9","source":{"kind":"arxiv","id":"2405.07769","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.07769","created_at":"2026-07-05T08:18:34Z"},{"alias_kind":"arxiv_version","alias_value":"2405.07769v1","created_at":"2026-07-05T08:18:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.07769","created_at":"2026-07-05T08:18:34Z"},{"alias_kind":"pith_short_12","alias_value":"G4AYXAMFXXWM","created_at":"2026-07-05T08:18:34Z"},{"alias_kind":"pith_short_16","alias_value":"G4AYXAMFXXWMTK5E","created_at":"2026-07-05T08:18:34Z"},{"alias_kind":"pith_short_8","alias_value":"G4AYXAMF","created_at":"2026-07-05T08:18:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:G4AYXAMFXXWMTK5EXQSL7QYFOM","target":"record","payload":{"canonical_record":{"source":{"id":"2405.07769","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-13T14:12:33Z","cross_cats_sorted":[],"title_canon_sha256":"bdfca2ee9fff8400e2fd87a7d8adee81e0a09e25488d73065867995ae72497f4","abstract_canon_sha256":"5925a2632f0921f40d87f29151bc4210b8e8fa6ec43e41f783d3e9ff40b7813d"},"schema_version":"1.0"},"canonical_sha256":"37018b8185bdecc9aba4bc24bfc305731409099d2e48976a2f6c874c8d26bfc9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:18:34.359816Z","signature_b64":"2PtilImTepyHX2NwGCyjz00VoC6rS4SHofSam42y0d7Rm9EJM0aJ+KSVKjjei0B2cSAjh8JqLE7msDLt+oOMAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37018b8185bdecc9aba4bc24bfc305731409099d2e48976a2f6c874c8d26bfc9","last_reissued_at":"2026-07-05T08:18:34.359330Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:18:34.359330Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.07769","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":"2026-07-05T08:18:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3falC0hNawPNGd/WlTFjGhvTGUVBtKAwcOTlueH5ACBicU2OWDXVDJSJdnn3ZgmV44CEO3td7PW7P4wP01v7AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:06:28.932067Z"},"content_sha256":"93059c96424e91ce48f2d083c936fd200a138f316136de35d5737aecce0cd6b1","schema_version":"1.0","event_id":"sha256:93059c96424e91ce48f2d083c936fd200a138f316136de35d5737aecce0cd6b1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:G4AYXAMFXXWMTK5EXQSL7QYFOM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"$\\alpha$VIL: Learning to Leverage Auxiliary Tasks for Multitask Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gabriel Gordon-Hall, Philip John Gorinski, Rafael Kourdis","submitted_at":"2024-05-13T14:12:33Z","abstract_excerpt":"Multitask Learning is a Machine Learning paradigm that aims to train a range of (usually related) tasks with the help of a shared model. While the goal is often to improve the joint performance of all training tasks, another approach is to focus on the performance of a specific target task, while treating the remaining ones as auxiliary data from which to possibly leverage positive transfer towards the target during training. In such settings, it becomes important to estimate the positive or negative influence auxiliary tasks will have on the target. While many ways have been proposed to estim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.07769","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/2405.07769/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":"2026-07-05T08:18:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hxa0CA2gmKOYDjihTqAV0PseKXvkX9AaQXQNCI/KIvAK2rAZiqcmvx/RiAcviMFzI8uxU8HR+yJZonHTxz98AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:06:28.932449Z"},"content_sha256":"6ce1a920c844a5258214c90ae5f2375e7f1254e45736ada5dbe28056f99fd7b1","schema_version":"1.0","event_id":"sha256:6ce1a920c844a5258214c90ae5f2375e7f1254e45736ada5dbe28056f99fd7b1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/G4AYXAMFXXWMTK5EXQSL7QYFOM/bundle.json","state_url":"https://pith.science/pith/G4AYXAMFXXWMTK5EXQSL7QYFOM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/G4AYXAMFXXWMTK5EXQSL7QYFOM/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":"2026-07-07T14:06:28Z","links":{"resolver":"https://pith.science/pith/G4AYXAMFXXWMTK5EXQSL7QYFOM","bundle":"https://pith.science/pith/G4AYXAMFXXWMTK5EXQSL7QYFOM/bundle.json","state":"https://pith.science/pith/G4AYXAMFXXWMTK5EXQSL7QYFOM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/G4AYXAMFXXWMTK5EXQSL7QYFOM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:G4AYXAMFXXWMTK5EXQSL7QYFOM","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":"5925a2632f0921f40d87f29151bc4210b8e8fa6ec43e41f783d3e9ff40b7813d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-13T14:12:33Z","title_canon_sha256":"bdfca2ee9fff8400e2fd87a7d8adee81e0a09e25488d73065867995ae72497f4"},"schema_version":"1.0","source":{"id":"2405.07769","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.07769","created_at":"2026-07-05T08:18:34Z"},{"alias_kind":"arxiv_version","alias_value":"2405.07769v1","created_at":"2026-07-05T08:18:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.07769","created_at":"2026-07-05T08:18:34Z"},{"alias_kind":"pith_short_12","alias_value":"G4AYXAMFXXWM","created_at":"2026-07-05T08:18:34Z"},{"alias_kind":"pith_short_16","alias_value":"G4AYXAMFXXWMTK5E","created_at":"2026-07-05T08:18:34Z"},{"alias_kind":"pith_short_8","alias_value":"G4AYXAMF","created_at":"2026-07-05T08:18:34Z"}],"graph_snapshots":[{"event_id":"sha256:6ce1a920c844a5258214c90ae5f2375e7f1254e45736ada5dbe28056f99fd7b1","target":"graph","created_at":"2026-07-05T08:18:34Z","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/2405.07769/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multitask Learning is a Machine Learning paradigm that aims to train a range of (usually related) tasks with the help of a shared model. While the goal is often to improve the joint performance of all training tasks, another approach is to focus on the performance of a specific target task, while treating the remaining ones as auxiliary data from which to possibly leverage positive transfer towards the target during training. In such settings, it becomes important to estimate the positive or negative influence auxiliary tasks will have on the target. While many ways have been proposed to estim","authors_text":"Gabriel Gordon-Hall, Philip John Gorinski, Rafael Kourdis","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-13T14:12:33Z","title":"$\\alpha$VIL: Learning to Leverage Auxiliary Tasks for Multitask Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.07769","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":"sha256:93059c96424e91ce48f2d083c936fd200a138f316136de35d5737aecce0cd6b1","target":"record","created_at":"2026-07-05T08:18:34Z","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":"5925a2632f0921f40d87f29151bc4210b8e8fa6ec43e41f783d3e9ff40b7813d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-13T14:12:33Z","title_canon_sha256":"bdfca2ee9fff8400e2fd87a7d8adee81e0a09e25488d73065867995ae72497f4"},"schema_version":"1.0","source":{"id":"2405.07769","kind":"arxiv","version":1}},"canonical_sha256":"37018b8185bdecc9aba4bc24bfc305731409099d2e48976a2f6c874c8d26bfc9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"37018b8185bdecc9aba4bc24bfc305731409099d2e48976a2f6c874c8d26bfc9","first_computed_at":"2026-07-05T08:18:34.359330Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:18:34.359330Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2PtilImTepyHX2NwGCyjz00VoC6rS4SHofSam42y0d7Rm9EJM0aJ+KSVKjjei0B2cSAjh8JqLE7msDLt+oOMAw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:18:34.359816Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.07769","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:93059c96424e91ce48f2d083c936fd200a138f316136de35d5737aecce0cd6b1","sha256:6ce1a920c844a5258214c90ae5f2375e7f1254e45736ada5dbe28056f99fd7b1"],"state_sha256":"bbc986d71ae2ceff30b1f3e02fed7f4546b83b87b928a771e1752801fdfbd9d4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JhcW5q3zQiQfguCvwnpEamwz30j7QGL3RWq4Hq6hec+42MIAdQRobln4QE799S2h8Yr4r4K3MSWeia0ZXDv7Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T14:06:28.934301Z","bundle_sha256":"502b39bf6c421c2174af39a399515d0b3f303c7e88464afaf53f0499281c974e"}}