{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ONJH7JNDK3FSMTA2K5HMVPCET6","short_pith_number":"pith:ONJH7JND","schema_version":"1.0","canonical_sha256":"73527fa5a356cb264c1a574ecabc449fa25d1e7c8885d1df9962d1265945c179","source":{"kind":"arxiv","id":"2602.07008","version":2},"attestation_state":"computed","paper":{"title":"Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Hua Zhang, Kangwei Liu, Qunli Zhang, Ruoyu Chen, Sanyi Zhang, Shangquan Sun, Shiming Liu, Xiaochun Cao, Xiaoqing Guo, Zhangcheng Wang","submitted_at":"2026-01-30T10:29:27Z","abstract_excerpt":"Reliable models should not only predict correctly, but also justify decisions with acceptable evidence. Yet conventional supervised learning typically provides only class-level labels, allowing models to achieve high accuracy through shortcut correlations rather than the intended evidence. Human priors can help constrain such behavior, but aligning models to these priors remains challenging because learned representations often diverge from human perception. To address this challenge, we propose an attribution-based human prior alignment method. We encode human priors as input regions that the"},"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":"2602.07008","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-01-30T10:29:27Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2a3a6c47b1c5f21be1378f953db0ef0fcb1173cede2bc95a83246fa8e788ff0b","abstract_canon_sha256":"19675e9decdfbe47f3e009e9bd37f48ffeea97500784f7b6e0904e272f70d4ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T01:06:08.235566Z","signature_b64":"OGGvlOBD6KblDCkzvyU0toWjfwglVAYVjxn2mqVwNmDxnmsyTs/XAIVbyV20HBaEcd28OPkuO/on7nQT3dU3DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73527fa5a356cb264c1a574ecabc449fa25d1e7c8885d1df9962d1265945c179","last_reissued_at":"2026-05-20T01:06:08.234862Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T01:06:08.234862Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Hua Zhang, Kangwei Liu, Qunli Zhang, Ruoyu Chen, Sanyi Zhang, Shangquan Sun, Shiming Liu, Xiaochun Cao, Xiaoqing Guo, Zhangcheng Wang","submitted_at":"2026-01-30T10:29:27Z","abstract_excerpt":"Reliable models should not only predict correctly, but also justify decisions with acceptable evidence. Yet conventional supervised learning typically provides only class-level labels, allowing models to achieve high accuracy through shortcut correlations rather than the intended evidence. Human priors can help constrain such behavior, but aligning models to these priors remains challenging because learned representations often diverge from human perception. To address this challenge, we propose an attribution-based human prior alignment method. We encode human priors as input regions that the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.07008","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/2602.07008/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":"2602.07008","created_at":"2026-05-20T01:06:08.234957+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.07008v2","created_at":"2026-05-20T01:06:08.234957+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.07008","created_at":"2026-05-20T01:06:08.234957+00:00"},{"alias_kind":"pith_short_12","alias_value":"ONJH7JNDK3FS","created_at":"2026-05-20T01:06:08.234957+00:00"},{"alias_kind":"pith_short_16","alias_value":"ONJH7JNDK3FSMTA2","created_at":"2026-05-20T01:06:08.234957+00:00"},{"alias_kind":"pith_short_8","alias_value":"ONJH7JND","created_at":"2026-05-20T01:06:08.234957+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2604.21375","citing_title":"VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular Framework for GUI Automation","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ONJH7JNDK3FSMTA2K5HMVPCET6","json":"https://pith.science/pith/ONJH7JNDK3FSMTA2K5HMVPCET6.json","graph_json":"https://pith.science/api/pith-number/ONJH7JNDK3FSMTA2K5HMVPCET6/graph.json","events_json":"https://pith.science/api/pith-number/ONJH7JNDK3FSMTA2K5HMVPCET6/events.json","paper":"https://pith.science/paper/ONJH7JND"},"agent_actions":{"view_html":"https://pith.science/pith/ONJH7JNDK3FSMTA2K5HMVPCET6","download_json":"https://pith.science/pith/ONJH7JNDK3FSMTA2K5HMVPCET6.json","view_paper":"https://pith.science/paper/ONJH7JND","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.07008&json=true","fetch_graph":"https://pith.science/api/pith-number/ONJH7JNDK3FSMTA2K5HMVPCET6/graph.json","fetch_events":"https://pith.science/api/pith-number/ONJH7JNDK3FSMTA2K5HMVPCET6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ONJH7JNDK3FSMTA2K5HMVPCET6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ONJH7JNDK3FSMTA2K5HMVPCET6/action/storage_attestation","attest_author":"https://pith.science/pith/ONJH7JNDK3FSMTA2K5HMVPCET6/action/author_attestation","sign_citation":"https://pith.science/pith/ONJH7JNDK3FSMTA2K5HMVPCET6/action/citation_signature","submit_replication":"https://pith.science/pith/ONJH7JNDK3FSMTA2K5HMVPCET6/action/replication_record"}},"created_at":"2026-05-20T01:06:08.234957+00:00","updated_at":"2026-05-20T01:06:08.234957+00:00"}