{"as_of":"2026-08-11T04:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:623b09c518e0d03c362e02bb456d88a044e72b86efa4570e0fa3412eb882ab82","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":15,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T15:03:33.048835Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T17:08:43.677845Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14103","snapshot_observed_at":"2026-08-10T15:03:33.048835Z","title":"Braun, A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.14662","last_updated":"2025-07-21T09:07:34Z","snapshot_observed_at":"2026-08-10T14:53:32.432269Z","submitted_at":"2025-01-24T17:35:07Z","title":"Efficient Sparse Flow Decomposition Methods for RNA Multi-Assembly","version":5},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T15:03:33.048835Z"},"links":{"cited_paper":"/paper/2211.14103","citing_paper":"/paper/2501.14662"},"observation_digest":"sha256:8440ec812b3caedc6fe13b2b28aec1f70234cb9ce6ba96f46d12ad720021d141","observation_id":"01227b41-3532-49a8-8d53-4b9aa895214f","resolution":{"observed_at":"2026-08-10T15:03:33.048835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14103","snapshot_observed_at":"2026-08-09T22:38:03.390112Z","title":"W., Hassani, H., Karbasi, A., Mokhtari, A., and Pokutta, S","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.18775","last_updated":"2025-06-10T14:05:20Z","snapshot_observed_at":"2026-08-10T00:48:55.608522Z","submitted_at":"2025-01-30T21:59:06Z","title":"Secant Line Search for Frank-Wolfe Algorithms","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-09T22:38:03.390112Z"},"links":{"cited_paper":"/paper/2211.14103","citing_paper":"/paper/2501.18775"},"observation_digest":"sha256:e4c00c0bb39573997367460a3efed36e116d5f7103ea6b08497ddd338df3d402","observation_id":"cfa8d589-7dcc-491f-86ec-8ff947e097d6","resolution":{"observed_at":"2026-08-09T22:38:03.390112Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14103","snapshot_observed_at":"2026-08-07T10:58:29.073737Z","title":"Conditional gradient methods","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.04192","last_updated":"2026-07-24T19:09:15Z","snapshot_observed_at":"2026-08-07T10:43:49.592801Z","submitted_at":"2025-06-04T17:39:03Z","title":"Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noise","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T10:58:29.073737Z"},"links":{"cited_paper":"/paper/2211.14103","citing_paper":"/paper/2506.04192"},"observation_digest":"sha256:fe539033033071661560c5ec3e62a77835af87f48719f83fc92cf2f8313585f7","observation_id":"fc58a937-be54-4702-967b-7e3cc0f1c768","resolution":{"observed_at":"2026-08-07T10:58:29.073737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods","version":5},"cited_work":{"arxiv_id":"2211.14103","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.14103","snapshot_observed_at":"2026-07-03T17:08:43.677845Z","title":"arXiv preprint arXiv:2211.14103 (2022)","venue":null,"work_id":"d6e88c68-158c-4485-ba9a-5c20dc721781","year":2022},"citing_paper":{"arxiv_id":"2506.12876","last_updated":"2026-05-13T04:56:46Z","snapshot_observed_at":"2026-08-01T16:23:25.586402Z","submitted_at":"2025-06-15T15:02:59Z","title":"MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on LLMs","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-19T09:01:16.991413Z"},"links":{"cited_paper":"/paper/2211.14103","citing_paper":"/paper/2506.12876"},"observation_digest":"sha256:87c39a9e09481b7e3ea030126264a2e3f13b9e93721b40acb02a60bd9208c04a","observation_id":"2b846821-4029-47fd-8e49-53ca091ada16","resolution":{"observed_at":"2026-05-19T09:02:14.502332Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14103","snapshot_observed_at":"2026-08-06T22:50:44.322025Z","title":"Braun , author A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.20796","last_updated":"2025-07-14T10:21:21Z","snapshot_observed_at":"2026-08-06T22:39:21.210074Z","submitted_at":"2025-06-25T19:40:23Z","title":"Observing High-dimensional Bell Inequality Violations using Multi-Outcome Spectral Measurements","version":3},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-06T22:50:44.322025Z"},"links":{"cited_paper":"/paper/2211.14103","citing_paper":"/paper/2506.20796"},"observation_digest":"sha256:5c94844b12f8c3cc4ba74b9d246992832edbcd9f3ee788cbb0714b741713c79c","observation_id":"76b1ff8f-5f2c-486c-a26e-7bb0441c23f0","resolution":{"observed_at":"2026-08-06T22:50:44.322025Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14103","snapshot_observed_at":"2026-08-06T19:33:23.853600Z","title":"arXiv preprint arXiv:2211.14103 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05669","last_updated":"2025-08-26T01:06:01Z","snapshot_observed_at":"2026-08-09T09:05:45.208077Z","submitted_at":"2025-07-08T04:53:21Z","title":"A Fully Adaptive Frank-Wolfe Algorithm for Relatively Smooth Problems and Its Application to Centralized Distributed Optimization","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T19:33:23.853600Z"},"links":{"cited_paper":"/paper/2211.14103","citing_paper":"/paper/2507.05669"},"observation_digest":"sha256:2fd8bead9886251313f08a8bedd329fc3a83fcfaed469b0141b82ccfde7081c3","observation_id":"23590aa4-78db-4c2b-b624-3f349486421e","resolution":{"observed_at":"2026-08-06T19:33:23.853600Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14103","snapshot_observed_at":"2026-08-06T14:55:48.912989Z","title":"Braun, A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.17435","last_updated":"2025-08-01T13:09:50Z","snapshot_observed_at":"2026-08-10T00:48:54.980233Z","submitted_at":"2025-07-23T11:50:47Z","title":"A Unified Toolbox for Multipartite Entanglement Certification","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T14:55:48.912989Z"},"links":{"cited_paper":"/paper/2211.14103","citing_paper":"/paper/2507.17435"},"observation_digest":"sha256:9c1a9f0867b81d6b1d92ca98c71687e6a62b187d06d99dfbbdc90f10e3d59a98","observation_id":"88bac3c9-395c-40ea-b7f1-270aad9ebd9c","resolution":{"observed_at":"2026-08-06T14:55:48.912989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods","version":5},"cited_work":{"arxiv_id":"2211.14103","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.14103","snapshot_observed_at":"2026-07-03T17:08:43.677845Z","title":"arXiv preprint arXiv:2211.14103 (2022)","venue":null,"work_id":"d6e88c68-158c-4485-ba9a-5c20dc721781","year":2022},"citing_paper":{"arxiv_id":"2510.12886","last_updated":"2026-04-09T18:31:20Z","snapshot_observed_at":"2026-08-02T13:53:46.823140Z","submitted_at":"2025-10-14T18:01:14Z","title":"Can outcome communication explain Bell nonlocality?","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-18T07:19:37.835227Z"},"links":{"cited_paper":"/paper/2211.14103","citing_paper":"/paper/2510.12886"},"observation_digest":"sha256:c227638fd5e6daf64e63b2c0acf291878568a18707b2429c01d301271e14fdd2","observation_id":"182ba31c-e369-42e7-9d3c-c383e5059f2c","resolution":{"observed_at":"2026-05-18T07:21:04.813705Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods","version":5},"cited_work":{"arxiv_id":"2211.14103","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.14103","snapshot_observed_at":"2026-07-03T17:08:43.677845Z","title":"arXiv preprint arXiv:2211.14103 (2022)","venue":null,"work_id":"d6e88c68-158c-4485-ba9a-5c20dc721781","year":2022},"citing_paper":{"arxiv_id":"2510.16468","last_updated":"2026-05-20T09:46:29Z","snapshot_observed_at":"2026-08-09T23:22:53.996244Z","submitted_at":"2025-10-18T12:26:28Z","title":"Frank-Wolfe Algorithms for (L0, L1)-smooth functions","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-18T06:18:52.290660Z"},"links":{"cited_paper":"/paper/2211.14103","citing_paper":"/paper/2510.16468"},"observation_digest":"sha256:9239387a58baceec6ec5f6517013817e87ab660e3dcb3b8e403eeb5a20489cb2","observation_id":"cc99af0b-cab7-433f-837c-b6699f0ddb8c","resolution":{"observed_at":"2026-05-18T06:20:58.625885Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods","version":5},"cited_work":{"arxiv_id":"2211.14103","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.14103","snapshot_observed_at":"2026-07-03T17:08:43.677845Z","title":"arXiv preprint arXiv:2211.14103 (2022)","venue":null,"work_id":"d6e88c68-158c-4485-ba9a-5c20dc721781","year":2022},"citing_paper":{"arxiv_id":"2510.16468","last_updated":"2026-05-20T09:46:29Z","snapshot_observed_at":"2026-08-09T23:22:53.996244Z","submitted_at":"2025-10-18T12:26:28Z","title":"Frank-Wolfe Algorithms for (L0, L1)-smooth functions","version":4},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-21T20:19:34.726206Z"},"links":{"cited_paper":"/paper/2211.14103","citing_paper":"/paper/2510.16468"},"observation_digest":"sha256:ca7650c1c8f0a7604d2b87541e3ca13fa12667df85043d36ad9e92b8b5df07bf","observation_id":"11ae1b19-2798-4d8b-85ef-6df01f4e634e","resolution":{"observed_at":"2026-05-21T20:20:34.582013Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods","version":5},"cited_work":{"arxiv_id":"2211.14103","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.14103","snapshot_observed_at":"2026-07-03T17:08:43.677845Z","title":"arXiv preprint arXiv:2211.14103 (2022)","venue":null,"work_id":"d6e88c68-158c-4485-ba9a-5c20dc721781","year":2022},"citing_paper":{"arxiv_id":"2511.11237","last_updated":"2026-05-11T15:41:23Z","snapshot_observed_at":"2026-07-06T22:35:51.341917Z","submitted_at":"2025-11-14T12:41:25Z","title":"An Efficient Algorithm for Minimizing Ordered Norms in Fractional Load Balancing","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-17T22:36:32.050347Z"},"links":{"cited_paper":"/paper/2211.14103","citing_paper":"/paper/2511.11237"},"observation_digest":"sha256:94fe04f39a67b642d11b775f5f53693352fd29761063f4d1df7271bf00b96151","observation_id":"1ec5a784-14ba-45e6-a581-ddc6f3feafa9","resolution":{"observed_at":"2026-05-17T22:40:23.742679Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods","version":5},"cited_work":{"arxiv_id":"2211.14103","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.14103","snapshot_observed_at":"2026-07-03T17:08:43.677845Z","title":"arXiv preprint arXiv:2211.14103 (2022)","venue":null,"work_id":"d6e88c68-158c-4485-ba9a-5c20dc721781","year":2022},"citing_paper":{"arxiv_id":"2605.08850","last_updated":"2026-05-09T10:03:24Z","snapshot_observed_at":"2026-07-06T23:21:02.177557Z","submitted_at":"2026-05-09T10:03:24Z","title":"Local LMO: Constrained Gradient Optimization via a Local Linear Minimization Oracle","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-05-12T01:18:15.063363Z"},"links":{"cited_paper":"/paper/2211.14103","citing_paper":"/paper/2605.08850"},"observation_digest":"sha256:4c8baa4680bcf35115e03b90fd1eb065b3d1dd63905de0a3a882400ea22b6221","observation_id":"2bcd79ac-8361-4a8b-878a-f109b5842a99","resolution":{"observed_at":"2026-05-12T08:06:29.127081Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods","version":5},"cited_work":{"arxiv_id":"2211.14103","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.14103","snapshot_observed_at":"2026-07-03T17:08:43.677845Z","title":"arXiv preprint arXiv:2211.14103 (2022)","venue":null,"work_id":"d6e88c68-158c-4485-ba9a-5c20dc721781","year":2022},"citing_paper":{"arxiv_id":"2605.09209","last_updated":"2026-05-09T23:00:31Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T23:00:31Z","title":"Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets","version":1},"reference_index":150,"source":"arxiv_source","source_observed_at":"2026-05-12T03:10:43.367020Z"},"links":{"cited_paper":"/paper/2211.14103","citing_paper":"/paper/2605.09209"},"observation_digest":"sha256:61332e00423e54356508c909bceefd7bbb693c80a7ab22ff81eb39cb38b66e42","observation_id":"3b306b65-5d35-4813-83b0-ce78aedc07ed","resolution":{"observed_at":"2026-05-12T03:11:18.799878Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods","version":5},"cited_work":{"arxiv_id":"2211.14103","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.14103","snapshot_observed_at":"2026-07-03T17:08:43.677845Z","title":"arXiv preprint arXiv:2211.14103 (2022)","venue":null,"work_id":"d6e88c68-158c-4485-ba9a-5c20dc721781","year":2022},"citing_paper":{"arxiv_id":"2606.14970","last_updated":"2026-06-22T08:40:27Z","snapshot_observed_at":"2026-07-06T23:52:28.557859Z","submitted_at":"2026-06-12T21:46:54Z","title":"Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-06-27T04:33:10.554853Z"},"links":{"cited_paper":"/paper/2211.14103","citing_paper":"/paper/2606.14970"},"observation_digest":"sha256:67c7ee7edbda7245a9d7315336267bafc785e63c759992fc3557f411a3ef73ac","observation_id":"4fb5775b-c4d4-4fcd-84f7-73026783909e","resolution":{"observed_at":"2026-07-03T17:08:43.679287Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14103","snapshot_observed_at":"2026-07-31T21:45:20.364664Z","title":"Conditional gradient methods.arXiv preprint arXiv:2211.14103, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.24197","last_updated":"2026-08-04T07:04:45Z","snapshot_observed_at":"2026-08-10T00:48:24.779825Z","submitted_at":"2026-07-27T09:18:47Z","title":"Minimum enclosing Bregman balls made easy","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-31T21:45:20.364664Z"},"links":{"cited_paper":"/paper/2211.14103","citing_paper":"/paper/2607.24197"},"observation_digest":"sha256:0f08917136030493cc2095b71f6dfbf0967ea678a02ac51f4a800a22ac957b56","observation_id":"12f2fe60-2ae7-4649-a3a7-82a1937977cf","resolution":{"observed_at":"2026-07-31T21:45:20.364664Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2211.14103/citation-record","integrity":"/paper/2211.14103/integrity","json":"/paper/2211.14103/citation-record.json","paper":"/paper/2211.14103"},"outbound":[],"paper":{"arxiv_id":"2211.14103","last_updated":"2025-08-01T13:28:50Z","latest_version":5,"primary_category":"math.OC","snapshot_observed_at":"2026-08-10T00:47:54.799223Z","submitted_at":"2022-11-25T13:36:11Z","title":"Conditional Gradient Methods"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2211.14103."}