{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:A3W6DXSAIB2LKLQSPF6DMETQ2M","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":"3c1c1de4bdc5b2ef5ada6f0ab1c7ce04c3279a8c6c7d51a64d4b674cfe926446","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-30T15:15:18Z","title_canon_sha256":"124c3c6942cd554afaf55b5505c0d47f9077e89eb838ee14b6307cb2afd87d52"},"schema_version":"1.0","source":{"id":"2505.24692","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.24692","created_at":"2026-07-05T11:12:54Z"},{"alias_kind":"arxiv_version","alias_value":"2505.24692v1","created_at":"2026-07-05T11:12:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.24692","created_at":"2026-07-05T11:12:54Z"},{"alias_kind":"pith_short_12","alias_value":"A3W6DXSAIB2L","created_at":"2026-07-05T11:12:54Z"},{"alias_kind":"pith_short_16","alias_value":"A3W6DXSAIB2LKLQS","created_at":"2026-07-05T11:12:54Z"},{"alias_kind":"pith_short_8","alias_value":"A3W6DXSA","created_at":"2026-07-05T11:12:54Z"}],"graph_snapshots":[{"event_id":"sha256:3fde5e37c878492e250fa5b99327b9b112111026ec35181f67c0b639c6041568","target":"graph","created_at":"2026-07-05T11:12:54Z","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/2505.24692/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Canonical algorithms for multi-armed bandits typically assume a stationary reward environment where the size of the action space (number of arms) is small. More recently developed methods typically relax only one of these assumptions: existing non-stationary bandit policies are designed for a small number of arms, while Lipschitz, linear, and Gaussian process bandit policies are designed to handle a large (or infinite) number of arms in stationary reward environments under constraints on the reward function. In this manuscript, we propose a novel policy to learn reward environments over a cont","authors_text":"Derek Everett, Edward Raff, Fernando Camacho, Fred Lu, James Holt","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-30T15:15:18Z","title":"Quick-Draw Bandits: Quickly Optimizing in Nonstationary Environments with Extremely Many Arms"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.24692","kind":"arxiv","version":1},"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:ee566e2ece3db2a8fb9fb660939ae00b1eff14143dabc93841e77061f3a92ed6","target":"record","created_at":"2026-07-05T11:12:54Z","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":"3c1c1de4bdc5b2ef5ada6f0ab1c7ce04c3279a8c6c7d51a64d4b674cfe926446","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-30T15:15:18Z","title_canon_sha256":"124c3c6942cd554afaf55b5505c0d47f9077e89eb838ee14b6307cb2afd87d52"},"schema_version":"1.0","source":{"id":"2505.24692","kind":"arxiv","version":1}},"canonical_sha256":"06ede1de404074b52e12797c361270d33783851e3d1207d3c78f6568908b0b0b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"06ede1de404074b52e12797c361270d33783851e3d1207d3c78f6568908b0b0b","first_computed_at":"2026-07-05T11:12:54.813463Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:12:54.813463Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hrpqC8fiPSijvv6dDAn8UOORhX/5QiGSY24l/IKOPxWEHATy/juAFaESmjZGDfBam24ooHoK1Ovt5l6TAra9Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:12:54.814008Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.24692","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ee566e2ece3db2a8fb9fb660939ae00b1eff14143dabc93841e77061f3a92ed6","sha256:3fde5e37c878492e250fa5b99327b9b112111026ec35181f67c0b639c6041568"],"state_sha256":"81e2f3375d1443e9d5afaec332c3d608c6845f3b7b12df5290eb448e91180d58"}