{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ASVQYX3YYT7PMQZ6Y23BYZT5FG","short_pith_number":"pith:ASVQYX3Y","schema_version":"1.0","canonical_sha256":"04ab0c5f78c4fef6433ec6b61c667d29a617b2b2d2c45c445aa8ff74375df4e0","source":{"kind":"arxiv","id":"2605.20771","version":1},"attestation_state":"computed","paper":{"title":"Cumulative Meta-Learning from Active Learning Queries for Robustness to Spurious Correlations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jingxian Wang, Kin Whye Chew","submitted_at":"2026-05-20T06:14:12Z","abstract_excerpt":"Spurious correlations in real-world datasets cause machine learning models to rely on irrelevant patterns, undermining reliability, generalization, and fairness. Active learning offers a promising way to address this failure mode by querying informative samples that distinguish core features from spurious ones. However, standard active-learning methods simply append queried examples to the labeled set, effectively updating only the likelihood term. In deep learning regimes, the influence of these informative samples can be diluted by the larger labeled set and memorized by overparameterized mo"},"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":"2605.20771","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-20T06:14:12Z","cross_cats_sorted":[],"title_canon_sha256":"9398fabed5db0912fd746cc03583c33dcc7ad883b86a982267a31abc559a877c","abstract_canon_sha256":"5b94f4655fb41a9a8ac556afa4eecfeb77c0afc2f69a493cbc14a0d03e73abdb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T01:04:53.468462Z","signature_b64":"TgWUpVHkLTyzNyYRVMTJmK7iAqZ+cN/zMsvUHJyoO+uCVMHSPIZ7vl2zBdBQEtJuByrRjeRxHWq/rhWduilkDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"04ab0c5f78c4fef6433ec6b61c667d29a617b2b2d2c45c445aa8ff74375df4e0","last_reissued_at":"2026-05-21T01:04:53.467862Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T01:04:53.467862Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cumulative Meta-Learning from Active Learning Queries for Robustness to Spurious Correlations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jingxian Wang, Kin Whye Chew","submitted_at":"2026-05-20T06:14:12Z","abstract_excerpt":"Spurious correlations in real-world datasets cause machine learning models to rely on irrelevant patterns, undermining reliability, generalization, and fairness. Active learning offers a promising way to address this failure mode by querying informative samples that distinguish core features from spurious ones. However, standard active-learning methods simply append queried examples to the labeled set, effectively updating only the likelihood term. In deep learning regimes, the influence of these informative samples can be diluted by the larger labeled set and memorized by overparameterized mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.20771","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/2605.20771/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":"2605.20771","created_at":"2026-05-21T01:04:53.467941+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.20771v1","created_at":"2026-05-21T01:04:53.467941+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.20771","created_at":"2026-05-21T01:04:53.467941+00:00"},{"alias_kind":"pith_short_12","alias_value":"ASVQYX3YYT7P","created_at":"2026-05-21T01:04:53.467941+00:00"},{"alias_kind":"pith_short_16","alias_value":"ASVQYX3YYT7PMQZ6","created_at":"2026-05-21T01:04:53.467941+00:00"},{"alias_kind":"pith_short_8","alias_value":"ASVQYX3Y","created_at":"2026-05-21T01:04:53.467941+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ASVQYX3YYT7PMQZ6Y23BYZT5FG","json":"https://pith.science/pith/ASVQYX3YYT7PMQZ6Y23BYZT5FG.json","graph_json":"https://pith.science/api/pith-number/ASVQYX3YYT7PMQZ6Y23BYZT5FG/graph.json","events_json":"https://pith.science/api/pith-number/ASVQYX3YYT7PMQZ6Y23BYZT5FG/events.json","paper":"https://pith.science/paper/ASVQYX3Y"},"agent_actions":{"view_html":"https://pith.science/pith/ASVQYX3YYT7PMQZ6Y23BYZT5FG","download_json":"https://pith.science/pith/ASVQYX3YYT7PMQZ6Y23BYZT5FG.json","view_paper":"https://pith.science/paper/ASVQYX3Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.20771&json=true","fetch_graph":"https://pith.science/api/pith-number/ASVQYX3YYT7PMQZ6Y23BYZT5FG/graph.json","fetch_events":"https://pith.science/api/pith-number/ASVQYX3YYT7PMQZ6Y23BYZT5FG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ASVQYX3YYT7PMQZ6Y23BYZT5FG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ASVQYX3YYT7PMQZ6Y23BYZT5FG/action/storage_attestation","attest_author":"https://pith.science/pith/ASVQYX3YYT7PMQZ6Y23BYZT5FG/action/author_attestation","sign_citation":"https://pith.science/pith/ASVQYX3YYT7PMQZ6Y23BYZT5FG/action/citation_signature","submit_replication":"https://pith.science/pith/ASVQYX3YYT7PMQZ6Y23BYZT5FG/action/replication_record"}},"created_at":"2026-05-21T01:04:53.467941+00:00","updated_at":"2026-05-21T01:04:53.467941+00:00"}