{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:TG4OYIY4W2CDG4YLEGGBT37UOP","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1b493d17795c4d0a6ba8247edc62c75024c2812fbd7c2ed5e8c4642a14e5305b","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-16T15:36:49Z","title_canon_sha256":"83b51ed67ca608e0c81b861b5307000d1604019b29162c2a56f22405e71000b2"},"schema_version":"1.0","source":{"id":"2306.09910","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.09910","created_at":"2026-07-05T07:51:09Z"},{"alias_kind":"arxiv_version","alias_value":"2306.09910v4","created_at":"2026-07-05T07:51:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.09910","created_at":"2026-07-05T07:51:09Z"},{"alias_kind":"pith_short_12","alias_value":"TG4OYIY4W2CD","created_at":"2026-07-05T07:51:09Z"},{"alias_kind":"pith_short_16","alias_value":"TG4OYIY4W2CDG4YL","created_at":"2026-07-05T07:51:09Z"},{"alias_kind":"pith_short_8","alias_value":"TG4OYIY4","created_at":"2026-07-05T07:51:09Z"}],"graph_snapshots":[{"event_id":"sha256:2433af2756dc11ac00451e6452e2ce8a8b403ab8027e2bca2710554d7f47b1ce","target":"graph","created_at":"2026-07-05T07:51:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2306.09910/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Labeled data are critical to modern machine learning applications, but obtaining labels can be expensive. To mitigate this cost, machine learning methods, such as transfer learning, semi-supervised learning and active learning, aim to be label-efficient: achieving high predictive performance from relatively few labeled examples. While obtaining the best label-efficiency in practice often requires combinations of these techniques, existing benchmark and evaluation frameworks do not capture a concerted combination of all such techniques. This paper addresses this deficiency by introducing LabelB","authors_text":"Arnav M. Das, Gantavya Bhatt, Gregory Canal, Jeffrey Bilmes, Jifan Zhang, Kevin Jamieson, Robert D Nowak, Simon Shaolei Du, Stephen Mussmann, Yifang Chen, Yinglun Zhu","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-16T15:36:49Z","title":"LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.09910","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:70835d2a443acb168a2cc4304fc8d0b24eb6b3926a9fcf049b0bd2ec2448bfba","target":"record","created_at":"2026-07-05T07:51:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1b493d17795c4d0a6ba8247edc62c75024c2812fbd7c2ed5e8c4642a14e5305b","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-16T15:36:49Z","title_canon_sha256":"83b51ed67ca608e0c81b861b5307000d1604019b29162c2a56f22405e71000b2"},"schema_version":"1.0","source":{"id":"2306.09910","kind":"arxiv","version":4}},"canonical_sha256":"99b8ec231cb68433730b218c19eff473d4a18091ea19ccb9a2b31f5b4a8030ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"99b8ec231cb68433730b218c19eff473d4a18091ea19ccb9a2b31f5b4a8030ae","first_computed_at":"2026-07-05T07:51:09.498483Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:51:09.498483Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"r7zpBYantinNa0Wl5ijH72NAAqBE3iMGHtf3sr+5th4Ofp6ABX3s13LV1XtsCueC30PSrYAkkFWeO00095pFDg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:51:09.498999Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.09910","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:70835d2a443acb168a2cc4304fc8d0b24eb6b3926a9fcf049b0bd2ec2448bfba","sha256:2433af2756dc11ac00451e6452e2ce8a8b403ab8027e2bca2710554d7f47b1ce"],"state_sha256":"117b09b5b6d7f38baf802554cd4b3dc1462824c7daf526d83037f2188d6ccbb6"}