{"as_of":"2026-08-15T15:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ca9ac5f784f2116f847fb011f3c7d9b256a97c9103de0f605a40d9d2d70a6f70","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T15:01:41.035394Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T15:01:40.772715Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-10T15:01:41.213473Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"cited_work":{"arxiv_id":"2501.14593","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.14593","snapshot_observed_at":"2026-08-10T15:01:41.213473Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","venue":"cs.CV","work_id":"92fd3b98-fe5a-40dc-b10a-a5907218ce0d","year":2025},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.772715Z"},"links":{"cited_paper":"/paper/2501.14593","citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:66e057c016f196039132bed78737bc78c1022b9d5c6bf820a1bfc2e57aa5c09d","observation_id":"c06b49d6-d5e2-42e4-9089-e49416a8983e","resolution":{"observed_at":"2026-08-10T15:01:41.224478Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.14593/citation-record","integrity":"/paper/2501.14593/integrity","json":"/paper/2501.14593/citation-record.json","paper":"/paper/2501.14593"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"cited_work":{"arxiv_id":"2501.14593","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.14593","snapshot_observed_at":"2026-08-10T15:01:41.213473Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","venue":"cs.CV","work_id":"92fd3b98-fe5a-40dc-b10a-a5907218ce0d","year":2025},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.772715Z"},"links":{"cited_paper":"/paper/2501.14593","citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:66e057c016f196039132bed78737bc78c1022b9d5c6bf820a1bfc2e57aa5c09d","observation_id":"c06b49d6-d5e2-42e4-9089-e49416a8983e","resolution":{"observed_at":"2026-08-10T15:01:41.224478Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.958549Z","title":"Notations","venue":null,"work_id":"70674b17-20e7-47ca-84ed-db42c0dd1ccc","year":null},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.779387Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:487d96628eb6bec9076e38ea8211406eeddd022743c3f8d935f58f84370043ac","observation_id":"7e51dd94-7b20-4afe-b1ab-d564c1e7ba10","resolution":{"observed_at":"2026-08-10T15:01:41.964089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.932414Z","title":"miniImageNet CIFAR-FS Fig","venue":null,"work_id":"a7e0eaea-6d44-47eb-a725-37d27cb2b865","year":null},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.787680Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:f229e0d4578038c9a4a12f744e6aa2a2ab1f695e47e4b010ef0e83e93cc16067","observation_id":"600d69cf-1530-466d-9f48-5c497e08ed67","resolution":{"observed_at":"2026-08-10T15:01:41.939610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.908896Z","title":null,"venue":null,"work_id":"3a58a6e2-db18-4133-8c76-5976900212c8","year":null},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.799507Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:dba5aa87349ebd3f3402c08fd63cdca5fd22785797ad65341666d7bc40e9a464","observation_id":"2e816f68-2ac5-45e6-9469-c696333a63f8","resolution":{"observed_at":"2026-08-10T15:01:41.915425Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.884968Z","title":"Generalizing from a few examples: A sur- vey on few-shot learning,","venue":null,"work_id":"523efa0c-c79d-49e1-8905-587d3472c0a8","year":2020},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.810678Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:e01ff21fc28012809a096be63eea1209595b90b897b52df19455b103e7796497","observation_id":"1cb49ee6-a763-4834-bc06-ba3335f24040","resolution":{"observed_at":"2026-08-10T15:01:41.890940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.844381Z","title":"Model-agnostic meta-learning for fast adaptation of deep networks,","venue":null,"work_id":"9b6ec8d8-8e84-40ba-97d4-d59836c80a70","year":2017},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.817397Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:5a19aca177731bc2263657dc55e1168adc1d54fe019ed3bf757856c1e27e848a","observation_id":"4929abef-601e-47bf-a549-6967a18662db","resolution":{"observed_at":"2026-08-10T15:01:41.863703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.02999","last_updated":"2018-10-22T16:11:14Z","snapshot_observed_at":"2026-08-14T19:38:16.450214Z","submitted_at":"2018-03-08T08:29:38Z","title":"On First-Order Meta-Learning Algorithms","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.02999","snapshot_observed_at":"2026-08-10T15:01:40.824576Z","title":"On first-order meta- learning algorithms,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.824576Z"},"links":{"cited_paper":"/paper/1803.02999","citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:394c900c174b733bdad34b356925748a9d3022f380a0c3615b8b727783790b7b","observation_id":"7ab2c538-4796-4fc9-a4d3-4335f0beede3","resolution":{"observed_at":"2026-08-10T15:01:40.824576Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.811650Z","title":"Siamese neural networks for one-shot image recognition,","venue":null,"work_id":"424957a2-5baa-4084-b196-9b531ba2f0f4","year":2015},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.833069Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:7d8e0765f3fd4f4531aeb4b9dde544bda21bdda4dfbb942c0fc82fdcb5b6a534","observation_id":"7c395ed9-9241-4c75-9147-fca04348b44a","resolution":{"observed_at":"2026-08-10T15:01:41.825414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.780482Z","title":"Pro- totypical networks for few-shot learning,","venue":null,"work_id":"8d2e9024-2df8-4cc4-b3b0-ce438e03b2e1","year":2017},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.842828Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:62c6f0a6a8e1c657510b3157e49181d79879657206583fc46b004f063dc309aa","observation_id":"1b74614b-a4e4-4fbe-b015-ac9b9976879f","resolution":{"observed_at":"2026-08-10T15:01:41.794662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.728007Z","title":"On episodes, pro- totypical networks, and few-shot learning,","venue":null,"work_id":"8cccc794-dd49-46ff-af22-4a5a13b335cc","year":2021},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.856069Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:a5837358a42a794322ebbc8ee4c4fb30391c0d510a3d19c343ce85e5df79bd3e","observation_id":"19c326cd-23c8-4f07-912a-16e33520a2d9","resolution":{"observed_at":"2026-08-10T15:01:41.752819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.696590Z","title":"Matching net- works for one shot learning,","venue":null,"work_id":"d5d7045e-3729-45c4-9031-e4ed328de36c","year":2016},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.866510Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:1bc270314a63d31eecc339f9482b95c4584237581a2f0f88ceac17e49ff141d6","observation_id":"3a2ab994-5020-4d00-b88b-df5311f0def6","resolution":{"observed_at":"2026-08-10T15:01:41.706218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.662700Z","title":"A discriminative feature learning approach for deep face recognition,","venue":null,"work_id":"916c4bd6-067b-4db2-a8d4-7d1f90d75df9","year":2016},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.875667Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:7719320c1368b921b21485b073d2226332b15a51472cd24b4cf6967bf7a5c2f1","observation_id":"ced7cb77-1087-43c9-bf43-cd5fdcbfdc14","resolution":{"observed_at":"2026-08-10T15:01:41.674345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.630915Z","title":"Neighbourhood components analy- sis,","venue":null,"work_id":"e147fa13-fc9a-4676-b018-63454c1e33ea","year":2005},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.883342Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:0d514091d778c60ce29132158a040fa9b47cbaec261b8ebf98666ec05a2be6b9","observation_id":"63a5c611-e132-4bfd-8fe7-1b51e32ad806","resolution":{"observed_at":"2026-08-10T15:01:41.640026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.600394Z","title":"Focal loss for dense object detec- tion,","venue":null,"work_id":"a9e117a4-ce12-4071-a42a-855ff01dfe89","year":2017},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.892895Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:c9646b01a48fa5a159a755027d574d93533e5a0cf871cd8234e2c8ad1e7d0230","observation_id":"82f7f3f1-56e1-4707-9315-a2a7d03cde89","resolution":{"observed_at":"2026-08-10T15:01:41.610206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.577939Z","title":"Mean shift: A robust approach toward feature space analysis,","venue":null,"work_id":"2e8015ed-543d-448d-83b2-2ab7258e03a1","year":2002},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.911075Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:914ecc2d12fc2ca68180444033e885443b6f3a422ec4b757b8dcc14844d03042","observation_id":"c4b17138-0882-4062-aa0f-d8292295e784","resolution":{"observed_at":"2026-08-10T15:01:41.586065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.542565Z","title":"Duda, Peter E","venue":null,"work_id":"e8001717-8c24-4f1d-bb10-ddf9195422ae","year":2001},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.916057Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:2f20df14b9c605d6ca86f6206821d58b5834e2897658baab9bbced01c30b9709","observation_id":"d349b23c-aebc-4bc6-8a23-87fee19640a3","resolution":{"observed_at":"2026-08-10T15:01:41.554749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.518070Z","title":"Multi-label classification: An overview,","venue":null,"work_id":"8ac33cdc-2d2b-4ece-96dc-21ee6531cfc6","year":2007},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.924619Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:8f0a3323b35e1e6c5134abc1aff929066d3ae8d6d7b2ba3e77c3d2e040c2a569","observation_id":"4fbed370-82a2-413e-b75d-2187ca2abef5","resolution":{"observed_at":"2026-08-10T15:01:41.528223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.483760Z","title":"Asymmetric loss for multi-label classification,","venue":null,"work_id":"190821f8-3db0-4e11-9193-353d209d7f7c","year":2021},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.934055Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:3007ac30e7491aa0f011f0e066ee5d5f51da26a7eede2e4a86894816610cbb65","observation_id":"a76cb2f6-d7fc-44dd-8446-eebd83d920b1","resolution":{"observed_at":"2026-08-10T15:01:41.492793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.449352Z","title":"Revisiting local descriptor based image-to-class measure for few-shot learning,","venue":null,"work_id":"93d34d49-4639-442a-94e0-857b5d693b2f","year":2019},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.939760Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:f893073374c6f2caf1f41071fd6c569ac87eee67347a14b5bb2e25a7b2894677","observation_id":"82ab321d-f9a3-42ee-84b1-06a44fe0dfaf","resolution":{"observed_at":"2026-08-10T15:01:41.458102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.426286Z","title":"Cross attention network for few- shot classification,","venue":null,"work_id":"fee6c8c3-23c0-45a4-8018-0fed569240de","year":2019},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.946423Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:7aca6d795602ab01dd00aeb91a5dd8688326988afd57b43eb2f8ccd34ed1ff9d","observation_id":"02f05d88-51a8-4942-833c-e381ba612f2c","resolution":{"observed_at":"2026-08-10T15:01:41.432864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.397880Z","title":"Meta-baseline: Exploring simple meta-learning for few-shot learning,","venue":null,"work_id":"0dbf2383-7dd9-4033-a5c1-90944f07d466","year":2021},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.954300Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:b872241d4c1ea3df51077df5cc3b8c1f03158bae20ec715185dcd1594865cf0e","observation_id":"f6d96436-c882-4d63-a312-0e2d6ac4080f","resolution":{"observed_at":"2026-08-10T15:01:41.403664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.374098Z","title":"Rethinking few-shot im- age classification: a good embedding is all you need?,","venue":null,"work_id":"19de478c-e1da-40b7-abfc-4f21273433ea","year":2020},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.963006Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:cfa546eda8334e4f7fae581acdfb0f10f6cb0a0bbc89949f1213fe5b5896abb3","observation_id":"416802d1-e36c-4471-b933-2169ebf4e1cb","resolution":{"observed_at":"2026-08-10T15:01:41.382059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.347556Z","title":"Meta-learning with differentiable closed-form solvers,","venue":null,"work_id":"de6bf2b0-daaf-472d-b896-e367a1f0504e","year":2019},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.975192Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:e157791608185fba4cd3af63356a96597f2c0b1588f7aead288e531840815bd8","observation_id":"c370a551-9c40-4d19-8d7c-af91e2780282","resolution":{"observed_at":"2026-08-10T15:01:41.356157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.325421Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":"cc08f6f1-ba62-42b7-830a-2c632146e8af","year":2009},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.988498Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:4e0507285c0d78364308c0f6eaa3ede8aceab6b839724a461f3f171e08826f53","observation_id":"43b055b5-ce0c-49e5-b166-8cbb8cc45a56","resolution":{"observed_at":"2026-08-10T15:01:41.331998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.293931Z","title":"Optimization as a model for few-shot learning,","venue":null,"work_id":"daa3f91d-2da1-4395-8334-cb4fac4a9655","year":2016},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:40.994337Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:1ce57c14b93e67f64a5fea596f5239c5a80da160112cd2bec592d864a30a6575","observation_id":"b2c99422-1119-4d1c-a25c-94c2ae1cb380","resolution":{"observed_at":"2026-08-10T15:01:41.303046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.267757Z","title":"Meta-learning for semi-supervised few-shot classification,","venue":null,"work_id":"ea44b3c2-c969-48ec-bf73-ecd73b1ebb9c","year":2018},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:41.002660Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:7c49b404378806cb9e91b9274d28d9c8a84b1dc78962aca308a682fe3dd9c3a3","observation_id":"74ba9f59-1615-49f2-846d-13bf227a03f7","resolution":{"observed_at":"2026-08-10T15:01:41.275390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.04623","last_updated":"2019-11-16T00:35:54Z","snapshot_observed_at":"2026-08-14T22:22:39.515577Z","submitted_at":"2019-11-12T00:44:10Z","title":"SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.04623","snapshot_observed_at":"2026-08-10T15:01:41.014562Z","title":"Simpleshot: Revisiting nearest-neighbor classification for few-shot learning,","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:41.014562Z"},"links":{"cited_paper":"/paper/1911.04623","citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:f4f3c3b1e9d447d74256c7ce600ba0bf856efe7dc4f01616653cd350fa8d7b99","observation_id":"5c4f4bdc-cf49-4b8c-92ed-33d3bfc33da9","resolution":{"observed_at":"2026-08-10T15:01:41.014562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.05960","last_updated":"2019-03-26T13:36:45Z","snapshot_observed_at":"2026-08-14T18:51:27.233335Z","submitted_at":"2018-07-16T16:35:29Z","title":"Meta-Learning with Latent Embedding Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.05960","snapshot_observed_at":"2026-08-10T15:01:41.022894Z","title":"Meta-learning with latent embedding opti- mization,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:41.022894Z"},"links":{"cited_paper":"/paper/1807.05960","citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:85892d5b27cc1b388a3ae459137c0d693f6f1c1c33e1b75329e34605d0d11d30","observation_id":"aac4e822-5a86-4319-8788-58f74aa9fc38","resolution":{"observed_at":"2026-08-10T15:01:41.022894Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T15:01:41.242443Z","title":"Meta-learning with differ- entiable convex optimization,","venue":null,"work_id":"4d27018e-d017-42a3-ad15-00214bda6c96","year":2019},"citing_paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T15:01:41.035394Z"},"links":{"citing_paper":"/paper/2501.14593"},"observation_digest":"sha256:85558a507dca95660f13a81eeae75352de4c2f1179c1192c3ca3fb0aa75ed607","observation_id":"456ffa19-49c2-4c81-a798-80f167c8dc23","resolution":{"observed_at":"2026-08-10T15:01:41.251200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.14593","last_updated":"2025-01-24T15:56:55Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-15T11:31:29.623259Z","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":1,"verified_fuzzy":24},"total_outbound_references":29},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2501.14593."}