{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JDB3JKE44YUPNSGWD25O5VVHNL","short_pith_number":"pith:JDB3JKE4","schema_version":"1.0","canonical_sha256":"48c3b4a89ce628f6c8d61ebaeed6a76af8cdd3c76214b41ed30f3597ee34a78e","source":{"kind":"arxiv","id":"2501.16751","version":3},"attestation_state":"computed","paper":{"title":"HiBug2: Efficient and Interpretable Error Slice Discovery for Comprehensive Model Debugging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chenchen Zhao, Muxi Chen, Qiang Xu","submitted_at":"2025-01-28T07:08:20Z","abstract_excerpt":"Despite the significant success of deep learning models in computer vision, they often exhibit systematic failures on specific data subsets, known as error slices. Identifying and mitigating these error slices is crucial to enhancing model robustness and reliability in real-world scenarios. In this paper, we introduce HiBug2, an automated framework for error slice discovery and model repair. HiBug2 first generates task-specific visual attributes to highlight instances prone to errors through an interpretable and structured process. It then employs an efficient slice enumeration algorithm to sy"},"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":"2501.16751","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-28T07:08:20Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f2a97a5c020872aee7ff278a6418508aa492570f3dff60ac96e082697bbe9d00","abstract_canon_sha256":"c4be1a567e023aeec81cfd1b28165ce1105d0cbcadbd500ced69f67c04270294"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:22:56.287481Z","signature_b64":"elL+GnojHEWIkDXOQ5n2hXz6wQr5dpYgazmwIVOdXUXIy+cOFQhNRe0FvxrV4rAvrfIIAdm3sQRzCysE2jutDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48c3b4a89ce628f6c8d61ebaeed6a76af8cdd3c76214b41ed30f3597ee34a78e","last_reissued_at":"2026-07-05T10:22:56.286749Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:22:56.286749Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HiBug2: Efficient and Interpretable Error Slice Discovery for Comprehensive Model Debugging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chenchen Zhao, Muxi Chen, Qiang Xu","submitted_at":"2025-01-28T07:08:20Z","abstract_excerpt":"Despite the significant success of deep learning models in computer vision, they often exhibit systematic failures on specific data subsets, known as error slices. Identifying and mitigating these error slices is crucial to enhancing model robustness and reliability in real-world scenarios. In this paper, we introduce HiBug2, an automated framework for error slice discovery and model repair. HiBug2 first generates task-specific visual attributes to highlight instances prone to errors through an interpretable and structured process. It then employs an efficient slice enumeration algorithm to sy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.16751","kind":"arxiv","version":3},"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/2501.16751/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":"2501.16751","created_at":"2026-07-05T10:22:56.286818+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.16751v3","created_at":"2026-07-05T10:22:56.286818+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.16751","created_at":"2026-07-05T10:22:56.286818+00:00"},{"alias_kind":"pith_short_12","alias_value":"JDB3JKE44YUP","created_at":"2026-07-05T10:22:56.286818+00:00"},{"alias_kind":"pith_short_16","alias_value":"JDB3JKE44YUPNSGW","created_at":"2026-07-05T10:22:56.286818+00:00"},{"alias_kind":"pith_short_8","alias_value":"JDB3JKE4","created_at":"2026-07-05T10:22:56.286818+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/JDB3JKE44YUPNSGWD25O5VVHNL","json":"https://pith.science/pith/JDB3JKE44YUPNSGWD25O5VVHNL.json","graph_json":"https://pith.science/api/pith-number/JDB3JKE44YUPNSGWD25O5VVHNL/graph.json","events_json":"https://pith.science/api/pith-number/JDB3JKE44YUPNSGWD25O5VVHNL/events.json","paper":"https://pith.science/paper/JDB3JKE4"},"agent_actions":{"view_html":"https://pith.science/pith/JDB3JKE44YUPNSGWD25O5VVHNL","download_json":"https://pith.science/pith/JDB3JKE44YUPNSGWD25O5VVHNL.json","view_paper":"https://pith.science/paper/JDB3JKE4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.16751&json=true","fetch_graph":"https://pith.science/api/pith-number/JDB3JKE44YUPNSGWD25O5VVHNL/graph.json","fetch_events":"https://pith.science/api/pith-number/JDB3JKE44YUPNSGWD25O5VVHNL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JDB3JKE44YUPNSGWD25O5VVHNL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JDB3JKE44YUPNSGWD25O5VVHNL/action/storage_attestation","attest_author":"https://pith.science/pith/JDB3JKE44YUPNSGWD25O5VVHNL/action/author_attestation","sign_citation":"https://pith.science/pith/JDB3JKE44YUPNSGWD25O5VVHNL/action/citation_signature","submit_replication":"https://pith.science/pith/JDB3JKE44YUPNSGWD25O5VVHNL/action/replication_record"}},"created_at":"2026-07-05T10:22:56.286818+00:00","updated_at":"2026-07-05T10:22:56.286818+00:00"}