{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NXYIHVBRDGSHTJXUABM4CJH6G4","short_pith_number":"pith:NXYIHVBR","schema_version":"1.0","canonical_sha256":"6df083d43119a479a6f40059c124fe37058ed53432ddeb335d3b8b2757e31197","source":{"kind":"arxiv","id":"2506.06048","version":1},"attestation_state":"computed","paper":{"title":"TRUST: Test-time Resource Utilization for Superior Trustworthiness","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Haripriya Harikumar, Santu Rana","submitted_at":"2025-06-06T12:52:32Z","abstract_excerpt":"Standard uncertainty estimation techniques, such as dropout, often struggle to clearly distinguish reliable predictions from unreliable ones. We attribute this limitation to noisy classifier weights, which, while not impairing overall class-level predictions, render finer-level statistics less informative. To address this, we propose a novel test-time optimization method that accounts for the impact of such noise to produce more reliable confidence estimates. This score defines a monotonic subset-selection function, where population accuracy consistently increases as samples with lower scores "},"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":"2506.06048","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-06T12:52:32Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"ad92038afe4612e5daf19e22b9e17325d1f6ab897905f952fce14e55cdb9bf24","abstract_canon_sha256":"9487ed0905d9faa9439e90c60b7af979bf77193da2c95b9c06ad651849426e6c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:16.534786Z","signature_b64":"Vu/nb8OZW+gGKegpYbBhxy2BJYDD36925gTzusdkPd2YuahBvP1hYexhd4JmjE+ekWuI+57jcrR286v0V4FjAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6df083d43119a479a6f40059c124fe37058ed53432ddeb335d3b8b2757e31197","last_reissued_at":"2026-07-05T11:17:16.534255Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:16.534255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TRUST: Test-time Resource Utilization for Superior Trustworthiness","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Haripriya Harikumar, Santu Rana","submitted_at":"2025-06-06T12:52:32Z","abstract_excerpt":"Standard uncertainty estimation techniques, such as dropout, often struggle to clearly distinguish reliable predictions from unreliable ones. We attribute this limitation to noisy classifier weights, which, while not impairing overall class-level predictions, render finer-level statistics less informative. To address this, we propose a novel test-time optimization method that accounts for the impact of such noise to produce more reliable confidence estimates. This score defines a monotonic subset-selection function, where population accuracy consistently increases as samples with lower scores "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06048","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/2506.06048/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":"2506.06048","created_at":"2026-07-05T11:17:16.534343+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.06048v1","created_at":"2026-07-05T11:17:16.534343+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06048","created_at":"2026-07-05T11:17:16.534343+00:00"},{"alias_kind":"pith_short_12","alias_value":"NXYIHVBRDGSH","created_at":"2026-07-05T11:17:16.534343+00:00"},{"alias_kind":"pith_short_16","alias_value":"NXYIHVBRDGSHTJXU","created_at":"2026-07-05T11:17:16.534343+00:00"},{"alias_kind":"pith_short_8","alias_value":"NXYIHVBR","created_at":"2026-07-05T11:17:16.534343+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/NXYIHVBRDGSHTJXUABM4CJH6G4","json":"https://pith.science/pith/NXYIHVBRDGSHTJXUABM4CJH6G4.json","graph_json":"https://pith.science/api/pith-number/NXYIHVBRDGSHTJXUABM4CJH6G4/graph.json","events_json":"https://pith.science/api/pith-number/NXYIHVBRDGSHTJXUABM4CJH6G4/events.json","paper":"https://pith.science/paper/NXYIHVBR"},"agent_actions":{"view_html":"https://pith.science/pith/NXYIHVBRDGSHTJXUABM4CJH6G4","download_json":"https://pith.science/pith/NXYIHVBRDGSHTJXUABM4CJH6G4.json","view_paper":"https://pith.science/paper/NXYIHVBR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.06048&json=true","fetch_graph":"https://pith.science/api/pith-number/NXYIHVBRDGSHTJXUABM4CJH6G4/graph.json","fetch_events":"https://pith.science/api/pith-number/NXYIHVBRDGSHTJXUABM4CJH6G4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NXYIHVBRDGSHTJXUABM4CJH6G4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NXYIHVBRDGSHTJXUABM4CJH6G4/action/storage_attestation","attest_author":"https://pith.science/pith/NXYIHVBRDGSHTJXUABM4CJH6G4/action/author_attestation","sign_citation":"https://pith.science/pith/NXYIHVBRDGSHTJXUABM4CJH6G4/action/citation_signature","submit_replication":"https://pith.science/pith/NXYIHVBRDGSHTJXUABM4CJH6G4/action/replication_record"}},"created_at":"2026-07-05T11:17:16.534343+00:00","updated_at":"2026-07-05T11:17:16.534343+00:00"}