{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:SCVYDR2QS37LDRCZNGVQB45CPG","short_pith_number":"pith:SCVYDR2Q","schema_version":"1.0","canonical_sha256":"90ab81c75096feb1c45969ab00f3a2799499d9ab49227ab7eb3ec99f1adcbe91","source":{"kind":"arxiv","id":"2605.17799","version":1},"attestation_state":"computed","paper":{"title":"Is Complex Training Necessary for Long-Tailed OOD Detection? A Re-think from Feature Geometry","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Ningkang Peng, Xuanming Chen, Yanhui Gu","submitted_at":"2026-05-18T03:21:57Z","abstract_excerpt":"Long-tailed out-of-distribution (LT-OOD) detection is often addressed with specialized training, including auxiliary out-of-distribution (OOD) data, abstention heads, contrastive objectives, energy losses, or gradient-conflict control. We show that these training mechanisms can obscure a simpler issue: frozen long-tailed representations may already contain useful OOD evidence, but raw Mahalanobis distance is distorted by frequency-coupled feature radius and poorly supported tail covariance. We propose Hyperspherical Pooled Mahalanobis (HPM), a post-hoc detector that normalizes features onto th"},"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":"2605.17799","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-18T03:21:57Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9d2a80955a1c5badc9c73ce0dc53bb83827da446df8bea36f46c25ebb2d83a5d","abstract_canon_sha256":"383ec2632765492fe19a11fecb4f58419246a51624ca22d325bab0f924983f54"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:58.935449Z","signature_b64":"o6GL+OFxjr8O868LQPxwSUmxXuWqbN+jBdN0WKWTVd0x+0sd98XqawUO+XN8n8z8XnJ8eG/bONWBi9sUyptdAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90ab81c75096feb1c45969ab00f3a2799499d9ab49227ab7eb3ec99f1adcbe91","last_reissued_at":"2026-05-20T00:04:58.934424Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:58.934424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Is Complex Training Necessary for Long-Tailed OOD Detection? A Re-think from Feature Geometry","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Ningkang Peng, Xuanming Chen, Yanhui Gu","submitted_at":"2026-05-18T03:21:57Z","abstract_excerpt":"Long-tailed out-of-distribution (LT-OOD) detection is often addressed with specialized training, including auxiliary out-of-distribution (OOD) data, abstention heads, contrastive objectives, energy losses, or gradient-conflict control. We show that these training mechanisms can obscure a simpler issue: frozen long-tailed representations may already contain useful OOD evidence, but raw Mahalanobis distance is distorted by frequency-coupled feature radius and poorly supported tail covariance. We propose Hyperspherical Pooled Mahalanobis (HPM), a post-hoc detector that normalizes features onto th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17799","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/2605.17799/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":"2605.17799","created_at":"2026-05-20T00:04:58.934565+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.17799v1","created_at":"2026-05-20T00:04:58.934565+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17799","created_at":"2026-05-20T00:04:58.934565+00:00"},{"alias_kind":"pith_short_12","alias_value":"SCVYDR2QS37L","created_at":"2026-05-20T00:04:58.934565+00:00"},{"alias_kind":"pith_short_16","alias_value":"SCVYDR2QS37LDRCZ","created_at":"2026-05-20T00:04:58.934565+00:00"},{"alias_kind":"pith_short_8","alias_value":"SCVYDR2Q","created_at":"2026-05-20T00:04:58.934565+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/SCVYDR2QS37LDRCZNGVQB45CPG","json":"https://pith.science/pith/SCVYDR2QS37LDRCZNGVQB45CPG.json","graph_json":"https://pith.science/api/pith-number/SCVYDR2QS37LDRCZNGVQB45CPG/graph.json","events_json":"https://pith.science/api/pith-number/SCVYDR2QS37LDRCZNGVQB45CPG/events.json","paper":"https://pith.science/paper/SCVYDR2Q"},"agent_actions":{"view_html":"https://pith.science/pith/SCVYDR2QS37LDRCZNGVQB45CPG","download_json":"https://pith.science/pith/SCVYDR2QS37LDRCZNGVQB45CPG.json","view_paper":"https://pith.science/paper/SCVYDR2Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.17799&json=true","fetch_graph":"https://pith.science/api/pith-number/SCVYDR2QS37LDRCZNGVQB45CPG/graph.json","fetch_events":"https://pith.science/api/pith-number/SCVYDR2QS37LDRCZNGVQB45CPG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SCVYDR2QS37LDRCZNGVQB45CPG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SCVYDR2QS37LDRCZNGVQB45CPG/action/storage_attestation","attest_author":"https://pith.science/pith/SCVYDR2QS37LDRCZNGVQB45CPG/action/author_attestation","sign_citation":"https://pith.science/pith/SCVYDR2QS37LDRCZNGVQB45CPG/action/citation_signature","submit_replication":"https://pith.science/pith/SCVYDR2QS37LDRCZNGVQB45CPG/action/replication_record"}},"created_at":"2026-05-20T00:04:58.934565+00:00","updated_at":"2026-05-20T00:04:58.934565+00:00"}