{"as_of":"2026-08-10T01:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:246cb1b930034f11dd6e845a652a2e0dec44cdd364691328eca982e7aaaa03c2","coverage":[{"denominator":12,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T22:40:31.694390Z","state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.07726/citation-record","integrity":"/paper/2606.07726/integrity","json":"/paper/2606.07726/citation-record.json","paper":"/paper/2606.07726"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2109.08229","last_updated":"2021-11-24T10:10:34Z","snapshot_observed_at":"2026-08-05T11:56:58.286103Z","submitted_at":"2021-09-16T21:27:03Z","title":"Policy Choice and Best Arm Identification: Asymptotic Analysis of Exploration Sampling","version":5},"cited_work":{"arxiv_id":"2109.08229","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2109.08229","snapshot_observed_at":"2026-07-02T16:27:09.354247Z","title":"Adaptive Treatment Assignment in Experiments for Policy Choice","venue":null,"work_id":"0f6aee67-1661-43c5-b093-095bed51bab1","year":null},"citing_paper":{"arxiv_id":"2606.07726","last_updated":"2026-06-05T17:03:19Z","snapshot_observed_at":"2026-08-09T18:02:00.367701Z","submitted_at":"2026-06-05T17:03:19Z","title":"Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-27T22:40:31.694390Z"},"links":{"cited_paper":"/paper/2109.08229","citing_paper":"/paper/2606.07726"},"observation_digest":"sha256:c8172913187a0a912352d7395038f53bcefb1edc0d2bb0caaa8ac33d561ae44f","observation_id":"9a9d0253-96bf-4d23-aa73-2b8faf4273a2","resolution":{"observed_at":"2026-07-02T16:27:09.355678Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.15483","last_updated":"2021-02-22T22:58:38Z","snapshot_observed_at":"2026-08-08T20:12:22.293920Z","submitted_at":"2020-12-31T07:24:30Z","title":"Why do classifier accuracies show linear trends under distribution shift?","version":2},"cited_work":{"arxiv_id":"2012.15483","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2012.15483","snapshot_observed_at":"2026-07-02T16:27:09.356780Z","title":"and Sra, S","venue":null,"work_id":"e249b57d-ffd7-4a9d-abe9-565abf3066c9","year":2012},"citing_paper":{"arxiv_id":"2606.07726","last_updated":"2026-06-05T17:03:19Z","snapshot_observed_at":"2026-08-09T18:02:00.367701Z","submitted_at":"2026-06-05T17:03:19Z","title":"Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-27T22:40:31.694390Z"},"links":{"cited_paper":"/paper/2012.15483","citing_paper":"/paper/2606.07726"},"observation_digest":"sha256:61c8cbb7f9747571c0c3beb6aa7abd01ca6aab66a851a1caeec55779946fce76","observation_id":"2df2210b-fbb6-481a-a687-8bf8163e5616","resolution":{"observed_at":"2026-07-02T16:27:09.358283Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T22:40:31.694390Z","title":"18 B.2 Proof of Theorem 5.1","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.07726","last_updated":"2026-06-05T17:03:19Z","snapshot_observed_at":"2026-08-09T18:02:00.367701Z","submitted_at":"2026-06-05T17:03:19Z","title":"Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-27T22:40:31.694390Z"},"links":{"citing_paper":"/paper/2606.07726"},"observation_digest":"sha256:3659e2849d39d303e705344b95501efa4a47be0ef5d7fa64a52cf1e1b1fe8d76","observation_id":"d700c989-3a03-453b-a70c-0eb2fc7a68a7","resolution":{"observed_at":"2026-06-27T22:40:31.694390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T22:40:31.694390Z","title":"Note that the following bound is slightly sharper than Proposition 1.4 in Bardenet & Maillard (2015) in the denominator of the exponential","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.07726","last_updated":"2026-06-05T17:03:19Z","snapshot_observed_at":"2026-08-09T18:02:00.367701Z","submitted_at":"2026-06-05T17:03:19Z","title":"Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-27T22:40:31.694390Z"},"links":{"citing_paper":"/paper/2606.07726"},"observation_digest":"sha256:17f845582ce63d6e2187b6358d2294e5e4565b9224adcf73b2e6f6451e92fd00","observation_id":"47a853e0-5f28-476d-90fe-0eb81cc2f629","resolution":{"observed_at":"2026-06-27T22:40:31.694390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T22:40:31.694390Z","title":"We define the empirical mean bµZ,n := 1 n Pn t=1 Zt","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2606.07726","last_updated":"2026-06-05T17:03:19Z","snapshot_observed_at":"2026-08-09T18:02:00.367701Z","submitted_at":"2026-06-05T17:03:19Z","title":"Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-27T22:40:31.694390Z"},"links":{"citing_paper":"/paper/2606.07726"},"observation_digest":"sha256:77157d27e97afe626ed3e8c1f5088397f9ea3769d046e6e3d23939c12d8fbb8a","observation_id":"fb4d813d-302f-414a-8e74-b5cb01f30609","resolution":{"observed_at":"2026-06-27T22:40:31.694390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T22:40:31.694390Z","title":"exp λ nX t=1 Zt # =E πw","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.07726","last_updated":"2026-06-05T17:03:19Z","snapshot_observed_at":"2026-08-09T18:02:00.367701Z","submitted_at":"2026-06-05T17:03:19Z","title":"Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-27T22:40:31.694390Z"},"links":{"citing_paper":"/paper/2606.07726"},"observation_digest":"sha256:b387664c35c2a7fb0cb70b8e22792f3cb05fa3c343ab13cb5cb0fb13d643c54f","observation_id":"600bb69f-f261-4437-99c6-13f9c4e850f2","resolution":{"observed_at":"2026-06-27T22:40:31.694390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T22:40:31.694390Z","title":null,"venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2606.07726","last_updated":"2026-06-05T17:03:19Z","snapshot_observed_at":"2026-08-09T18:02:00.367701Z","submitted_at":"2026-06-05T17:03:19Z","title":"Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-27T22:40:31.694390Z"},"links":{"citing_paper":"/paper/2606.07726"},"observation_digest":"sha256:41d26d6b9b1863c5b0f8a267ba46312af4e1b43aeb48a54b8f2d43f76cea980b","observation_id":"8526c5cd-e352-4a63-b25f-afa8968d6de7","resolution":{"observed_at":"2026-06-27T22:40:31.694390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T22:40:31.694390Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.07726","last_updated":"2026-06-05T17:03:19Z","snapshot_observed_at":"2026-08-09T18:02:00.367701Z","submitted_at":"2026-06-05T17:03:19Z","title":"Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-27T22:40:31.694390Z"},"links":{"citing_paper":"/paper/2606.07726"},"observation_digest":"sha256:99760715636d427804f1781e121a81b4546ba072b09e4407058c34bf89bc2359","observation_id":"93bbd9fb-6227-4f59-bc14-fedd7f496524","resolution":{"observed_at":"2026-06-27T22:40:31.694390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T22:40:31.694390Z","title":"Best Acc and Mean Acc denote the accuracy of the best model and the mean accuracy across all models, respectively","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.07726","last_updated":"2026-06-05T17:03:19Z","snapshot_observed_at":"2026-08-09T18:02:00.367701Z","submitted_at":"2026-06-05T17:03:19Z","title":"Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-27T22:40:31.694390Z"},"links":{"citing_paper":"/paper/2606.07726"},"observation_digest":"sha256:f2c7e309257ad7ef9721cd6128e02d1d901d797209d3f2e92df14e542d7ed6ae","observation_id":"6a43b328-0c19-4391-bbb5-820151cd117f","resolution":{"observed_at":"2026-06-27T22:40:31.694390Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T22:40:31.694390Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.07726","last_updated":"2026-06-05T17:03:19Z","snapshot_observed_at":"2026-08-09T18:02:00.367701Z","submitted_at":"2026-06-05T17:03:19Z","title":"Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-27T22:40:31.694390Z"},"links":{"citing_paper":"/paper/2606.07726"},"observation_digest":"sha256:0e8cc90b998929591e386e3520a92e4c0fad6b242517f1c44ef02837cb241c20","observation_id":"bfb6cba4-a54f-460a-b86b-e69eaef265d8","resolution":{"observed_at":"2026-06-27T22:40:31.694390Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T22:40:31.694390Z","title":"(2010) No Free Seconds SyUCB-E This work (appendix C.3) Yesa∈ {0.1,1,10,100}Minutes UCB-E Audibert et al","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2606.07726","last_updated":"2026-06-05T17:03:19Z","snapshot_observed_at":"2026-08-09T18:02:00.367701Z","submitted_at":"2026-06-05T17:03:19Z","title":"Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-27T22:40:31.694390Z"},"links":{"citing_paper":"/paper/2606.07726"},"observation_digest":"sha256:535a4f39226de6ce2f4fa352284c03e9d01676a95382a930331645b41c872778","observation_id":"a7432fa4-43c8-44fd-af63-fa80cbeb0d45","resolution":{"observed_at":"2026-06-27T22:40:31.694390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T22:40:31.694390Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.07726","last_updated":"2026-06-05T17:03:19Z","snapshot_observed_at":"2026-08-09T18:02:00.367701Z","submitted_at":"2026-06-05T17:03:19Z","title":"Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-27T22:40:31.694390Z"},"links":{"citing_paper":"/paper/2606.07726"},"observation_digest":"sha256:7ae79e70dd5e49835afcbbaedc6147ee81d6178fe476e6b23cdd5a35b9b4539a","observation_id":"f4c7d5b1-7746-4e69-a7d9-eb9cd7c4f893","resolution":{"observed_at":"2026-06-27T22:40:31.694390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.07726","last_updated":"2026-06-05T17:03:19Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T18:02:00.367701Z","submitted_at":"2026-06-05T17:03:19Z","title":"Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity"},"reference_resolution":{"displayed":12,"state_counts":{"malformed_identifier":2,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":8,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":12},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2606.07726."}