{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IDTFZD5UANJEXC5MBOUMESTQPL","short_pith_number":"pith:IDTFZD5U","schema_version":"1.0","canonical_sha256":"40e65c8fb403524b8bac0ba8c24a707af927cd582e05e1e2b87b9ad8bbc2b97d","source":{"kind":"arxiv","id":"2508.08126","version":1},"attestation_state":"computed","paper":{"title":"OFAL: An Oracle-Free Active Learning Framework","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hadi Khorsand, Vahid Pourahmadi","submitted_at":"2025-08-11T16:04:29Z","abstract_excerpt":"In the active learning paradigm, using an oracle to label data has always been a complex and expensive task, and with the emersion of large unlabeled data pools, it would be highly beneficial If we could achieve better results without relying on an oracle. This research introduces OFAL, an oracle-free active learning scheme that utilizes neural network uncertainty. OFAL uses the model's own uncertainty to transform highly confident unlabeled samples into informative uncertain samples. First, we start with separating and quantifying different parts of uncertainty and introduce Monte Carlo Dropo"},"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":"2508.08126","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-11T16:04:29Z","cross_cats_sorted":[],"title_canon_sha256":"e32390f04eb9b422159cc378685b196489575be56e1bcf3761103873f0a96427","abstract_canon_sha256":"65665aebcda8dbc4f5e1cc5d986b69691bd88c019dec18edb0e5f7ad61bf958e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:52:08.673365Z","signature_b64":"1aJJWy0gwd9rjGr+LCcMFMSKc/6OFWPqusOOuPZkFtNxLddBTvhZ6FAu8HW/ZTPKdax+Vw7sB/2lZ/+knm04AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"40e65c8fb403524b8bac0ba8c24a707af927cd582e05e1e2b87b9ad8bbc2b97d","last_reissued_at":"2026-07-05T11:52:08.672866Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:52:08.672866Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OFAL: An Oracle-Free Active Learning Framework","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hadi Khorsand, Vahid Pourahmadi","submitted_at":"2025-08-11T16:04:29Z","abstract_excerpt":"In the active learning paradigm, using an oracle to label data has always been a complex and expensive task, and with the emersion of large unlabeled data pools, it would be highly beneficial If we could achieve better results without relying on an oracle. This research introduces OFAL, an oracle-free active learning scheme that utilizes neural network uncertainty. OFAL uses the model's own uncertainty to transform highly confident unlabeled samples into informative uncertain samples. First, we start with separating and quantifying different parts of uncertainty and introduce Monte Carlo Dropo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.08126","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/2508.08126/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":"2508.08126","created_at":"2026-07-05T11:52:08.672922+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.08126v1","created_at":"2026-07-05T11:52:08.672922+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.08126","created_at":"2026-07-05T11:52:08.672922+00:00"},{"alias_kind":"pith_short_12","alias_value":"IDTFZD5UANJE","created_at":"2026-07-05T11:52:08.672922+00:00"},{"alias_kind":"pith_short_16","alias_value":"IDTFZD5UANJEXC5M","created_at":"2026-07-05T11:52:08.672922+00:00"},{"alias_kind":"pith_short_8","alias_value":"IDTFZD5U","created_at":"2026-07-05T11:52:08.672922+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.08125","citing_title":"Czech Dataset for Complex Aspect-Based Sentiment Analysis Tasks","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IDTFZD5UANJEXC5MBOUMESTQPL","json":"https://pith.science/pith/IDTFZD5UANJEXC5MBOUMESTQPL.json","graph_json":"https://pith.science/api/pith-number/IDTFZD5UANJEXC5MBOUMESTQPL/graph.json","events_json":"https://pith.science/api/pith-number/IDTFZD5UANJEXC5MBOUMESTQPL/events.json","paper":"https://pith.science/paper/IDTFZD5U"},"agent_actions":{"view_html":"https://pith.science/pith/IDTFZD5UANJEXC5MBOUMESTQPL","download_json":"https://pith.science/pith/IDTFZD5UANJEXC5MBOUMESTQPL.json","view_paper":"https://pith.science/paper/IDTFZD5U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.08126&json=true","fetch_graph":"https://pith.science/api/pith-number/IDTFZD5UANJEXC5MBOUMESTQPL/graph.json","fetch_events":"https://pith.science/api/pith-number/IDTFZD5UANJEXC5MBOUMESTQPL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IDTFZD5UANJEXC5MBOUMESTQPL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IDTFZD5UANJEXC5MBOUMESTQPL/action/storage_attestation","attest_author":"https://pith.science/pith/IDTFZD5UANJEXC5MBOUMESTQPL/action/author_attestation","sign_citation":"https://pith.science/pith/IDTFZD5UANJEXC5MBOUMESTQPL/action/citation_signature","submit_replication":"https://pith.science/pith/IDTFZD5UANJEXC5MBOUMESTQPL/action/replication_record"}},"created_at":"2026-07-05T11:52:08.672922+00:00","updated_at":"2026-07-05T11:52:08.672922+00:00"}