{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HXXPEEMIG4WHB2T6INPOUL2MWR","short_pith_number":"pith:HXXPEEMI","schema_version":"1.0","canonical_sha256":"3deef21188372c70ea7e435eea2f4cb4650b6995033d029f0368545e80c7450c","source":{"kind":"arxiv","id":"2510.06505","version":2},"attestation_state":"computed","paper":{"title":"Medix: Out-of-Distribution Detection from Unlabeled Wild Data via Robust Gradient Statistics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ali Falahati, Hossein Goli, Mohammad Mohammadi Amiri, Momin Abbas","submitted_at":"2025-10-07T22:43:57Z","abstract_excerpt":"Out-of-distribution (OOD) detection plays a crucial role in ensuring the robustness of machine learning systems deployed in real-world applications. Recent approaches have explored the use of unlabeled data, showing potential for enhancing OOD detection capabilities. However, effectively utilizing unlabeled in-the-wild data remains challenging due to the mixed nature of both in-distribution (InD) and OOD samples. The lack of a distinct set of OOD samples complicates the task of training an optimal OOD classifier. In this work, we introduce Medix, a novel framework designed to identify potentia"},"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":"2510.06505","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-10-07T22:43:57Z","cross_cats_sorted":["cs.AI","math.OC","stat.ML"],"title_canon_sha256":"5a2d4dc6c3aecf2a70503e7bd94be7e13cd834a47c9b93605a4e61bf8d8692ac","abstract_canon_sha256":"13cf5cf11c23e730a73b6e2b45674bd658df7307afcd4d69bc1a1dc7b3c1cfa1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-08T01:18:20.792901Z","signature_b64":"eIlBGfzmqEo61vD4kfO9AehFp61SiNP+1FdH3A3523cafXBndeGBEEoQ+TAWP0cZUQfS5avfv3l8Nol2KwOPCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3deef21188372c70ea7e435eea2f4cb4650b6995033d029f0368545e80c7450c","last_reissued_at":"2026-07-08T01:18:20.792347Z","signature_status":"signed_v1","first_computed_at":"2026-07-08T01:18:20.792347Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Medix: Out-of-Distribution Detection from Unlabeled Wild Data via Robust Gradient Statistics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ali Falahati, Hossein Goli, Mohammad Mohammadi Amiri, Momin Abbas","submitted_at":"2025-10-07T22:43:57Z","abstract_excerpt":"Out-of-distribution (OOD) detection plays a crucial role in ensuring the robustness of machine learning systems deployed in real-world applications. Recent approaches have explored the use of unlabeled data, showing potential for enhancing OOD detection capabilities. However, effectively utilizing unlabeled in-the-wild data remains challenging due to the mixed nature of both in-distribution (InD) and OOD samples. The lack of a distinct set of OOD samples complicates the task of training an optimal OOD classifier. In this work, we introduce Medix, a novel framework designed to identify potentia"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.06505","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/2510.06505/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":"2510.06505","created_at":"2026-07-08T01:18:20.792417+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.06505v2","created_at":"2026-07-08T01:18:20.792417+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.06505","created_at":"2026-07-08T01:18:20.792417+00:00"},{"alias_kind":"pith_short_12","alias_value":"HXXPEEMIG4WH","created_at":"2026-07-08T01:18:20.792417+00:00"},{"alias_kind":"pith_short_16","alias_value":"HXXPEEMIG4WHB2T6","created_at":"2026-07-08T01:18:20.792417+00:00"},{"alias_kind":"pith_short_8","alias_value":"HXXPEEMI","created_at":"2026-07-08T01:18:20.792417+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/HXXPEEMIG4WHB2T6INPOUL2MWR","json":"https://pith.science/pith/HXXPEEMIG4WHB2T6INPOUL2MWR.json","graph_json":"https://pith.science/api/pith-number/HXXPEEMIG4WHB2T6INPOUL2MWR/graph.json","events_json":"https://pith.science/api/pith-number/HXXPEEMIG4WHB2T6INPOUL2MWR/events.json","paper":"https://pith.science/paper/HXXPEEMI"},"agent_actions":{"view_html":"https://pith.science/pith/HXXPEEMIG4WHB2T6INPOUL2MWR","download_json":"https://pith.science/pith/HXXPEEMIG4WHB2T6INPOUL2MWR.json","view_paper":"https://pith.science/paper/HXXPEEMI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.06505&json=true","fetch_graph":"https://pith.science/api/pith-number/HXXPEEMIG4WHB2T6INPOUL2MWR/graph.json","fetch_events":"https://pith.science/api/pith-number/HXXPEEMIG4WHB2T6INPOUL2MWR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HXXPEEMIG4WHB2T6INPOUL2MWR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HXXPEEMIG4WHB2T6INPOUL2MWR/action/storage_attestation","attest_author":"https://pith.science/pith/HXXPEEMIG4WHB2T6INPOUL2MWR/action/author_attestation","sign_citation":"https://pith.science/pith/HXXPEEMIG4WHB2T6INPOUL2MWR/action/citation_signature","submit_replication":"https://pith.science/pith/HXXPEEMIG4WHB2T6INPOUL2MWR/action/replication_record"}},"created_at":"2026-07-08T01:18:20.792417+00:00","updated_at":"2026-07-08T01:18:20.792417+00:00"}