{"as_of":"2026-08-21T21:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8b0388ca70448855f6792e464104bb678043d559f6f57aa17248b9cccd86c73a","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:12:59.725083Z","state":"measured"},{"denominator":23,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":23,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T23:11:01.510667Z","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":"2026-06-29T09:03:15.951664Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.16277","snapshot_observed_at":"2026-08-02T23:11:01.510667Z","title":"H., et al","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.14696","last_updated":"2026-06-18T11:23:03Z","snapshot_observed_at":"2026-08-17T14:22:15.774101Z","submitted_at":"2026-02-16T12:33:05Z","title":"A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't)","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-02T23:11:01.510667Z"},"links":{"cited_paper":"/paper/2504.16277","citing_paper":"/paper/2602.14696"},"observation_digest":"sha256:328870b4675c4d95063aff0775d963d83200dcae559b37896a7bceade60d4f3a","observation_id":"1ec67ea5-43a0-4183-9063-6db0fc85e775","resolution":{"observed_at":"2026-08-02T23:11:01.510667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"cited_work":{"arxiv_id":"2504.16277","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.16277","snapshot_observed_at":"2026-06-29T09:03:15.951664Z","title":"DataS3: Dataset subset selection for specialization.arXiv preprint arXiv:2504.16277, 2025","venue":null,"work_id":"539857ee-bb34-43f1-a63d-1e10d300a75c","year":2025},"citing_paper":{"arxiv_id":"2605.30337","last_updated":"2026-05-28T17:59:01Z","snapshot_observed_at":"2026-08-14T12:33:00.626034Z","submitted_at":"2026-05-28T17:59:01Z","title":"Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-29T08:58:52.511363Z"},"links":{"cited_paper":"/paper/2504.16277","citing_paper":"/paper/2605.30337"},"observation_digest":"sha256:f6296dbd55fbffd7409f4350e6c24a6f8f12f3096aa06a05810c00640c3345ed","observation_id":"273cbdb5-5794-41e2-90b1-10073ee69bc6","resolution":{"observed_at":"2026-06-29T09:03:15.953204Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.16277","snapshot_observed_at":"2026-07-12T07:31:01.780245Z","title":"DataS3: Dataset subset selection for specialization.arXiv preprint arXiv:2504.16277, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.02721","last_updated":"2026-07-02T19:15:54Z","snapshot_observed_at":"2026-08-03T01:02:02.916095Z","submitted_at":"2026-07-02T19:15:54Z","title":"Provable Pruning for Efficient 3D Gaussian Splatting via Coresets","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-12T07:31:01.780245Z"},"links":{"cited_paper":"/paper/2504.16277","citing_paper":"/paper/2607.02721"},"observation_digest":"sha256:329502ab4e87b18eba68af1e27e937364c2a2fcebb619e19c30f1e50050e38a5","observation_id":"c1f1a7be-71d1-4a3b-94c2-16ed7e5cdff1","resolution":{"observed_at":"2026-07-12T07:31:01.780245Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2504.16277/citation-record","integrity":"/paper/2504.16277/integrity","json":"/paper/2504.16277/citation-record.json","paper":"/paper/2504.16277"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:12:59.926075Z","title":"low data quality","venue":null,"work_id":"bba781a9-4e60-4631-8cbc-5369be59c0f5","year":2009},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.710067Z"},"links":{"citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:2efdd4746859c42e0c5a117c05b523685d176040be9967cc9d648360ccefa4ea","observation_id":"722e5930-3e23-4e01-88dd-f26176daf3e3","resolution":{"observed_at":"2026-08-16T11:12:59.930440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.04431","last_updated":"2023-08-08T17:58:45Z","snapshot_observed_at":"2026-08-16T15:09:53.566648Z","submitted_at":"2023-08-08T17:58:45Z","title":"When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.04431","snapshot_observed_at":"2026-08-16T11:12:59.643113Z","title":"Zhang, Aahlad Manas Puli, and Rajesh Ranganath","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.643113Z"},"links":{"cited_paper":"/paper/2308.04431","citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:9b14b398fa29a491fed6ca53c692df3c919b63b57134d2d03d345ddbfd8acf6d","observation_id":"0d6b5fde-27a5-4c95-a940-3f7c27e914f5","resolution":{"observed_at":"2026-08-16T11:12:59.643113Z","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":"2026-08-16T11:13:00.039569Z","title":"Active Learning for BERT: An Empirical Study","venue":null,"work_id":"2c6baa49-188c-4877-a4eb-bf57b6401455","year":2020},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.661595Z"},"links":{"citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:9276705fdf8af54771de259b975d2d0d83915a23950b22975f21b08f670ffabf","observation_id":"f7e29eb6-2b99-4458-9bde-502048b26a33","resolution":{"observed_at":"2026-08-16T11:13:00.044737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.05057","last_updated":"2024-10-07T14:14:38Z","snapshot_observed_at":"2026-08-21T06:01:40.674281Z","submitted_at":"2024-10-07T14:14:38Z","title":"SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification","version":1},"cited_work":{"arxiv_id":"2410.05057","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.05057","snapshot_observed_at":"2026-08-16T11:12:59.805294Z","title":"SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification","venue":"cs.CV","work_id":"b7df98d0-dee6-463f-9589-a01317b444f4","year":2024},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.665698Z"},"links":{"cited_paper":"/paper/2410.05057","citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:9a16d87b90635a673246091b954d7317c3742db6aa87ee950beb97bbc92a5bc8","observation_id":"72d02356-3171-449f-9067-50d5aa710763","resolution":{"observed_at":"2026-08-16T11:12:59.813971Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.00644","last_updated":"2020-09-14T09:55:13Z","snapshot_observed_at":"2026-08-13T09:32:00.308636Z","submitted_at":"2020-07-01T17:53:26Z","title":"Measuring Robustness to Natural Distribution Shifts in Image Classification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.00644","snapshot_observed_at":"2026-08-16T11:12:59.683981Z","title":"Measuring robustness to natural distribution shifts in image classification","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.683981Z"},"links":{"cited_paper":"/paper/2007.00644","citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:d6ae17a67446d0188a3f9ce25dcb43838933adaad610fb77c8305130d803761e","observation_id":"8bfad4d4-d481-4dd7-85f9-692d9344176d","resolution":{"observed_at":"2026-08-16T11:12:59.683981Z","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":"2026-08-16T11:12:59.972139Z","title":"Drive anywhere: Generalizable end-to-end autonomous driving with multi-modal foundation models","venue":null,"work_id":"ee20db58-dda5-4f71-946d-0fc29f309956","year":2024},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.689209Z"},"links":{"citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:29429de25c59f09edb4a488ab442d184699a6f85bfd622d839b754eeddce9581","observation_id":"222fb1f7-73b0-456b-b242-29cee7ed5682","resolution":{"observed_at":"2026-08-16T11:12:59.976072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T11:12:59.955068Z","title":"Michelle Yuan, Hsuan-Tien Lin, and Jordan Boyd-Graber","venue":null,"work_id":"c2c7ffac-4f16-4a18-9fa2-d863ae129228","year":2020},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.693849Z"},"links":{"citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:01a6ed11d1081f5c0791ea720cb14ca177d20351303b0d4153b7b74b837187c0","observation_id":"6ec94220-d1db-466f-a918-0400823cac6a","resolution":{"observed_at":"2026-08-16T11:12:59.959109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T11:12:59.940617Z","title":"efficiency-style","venue":null,"work_id":"868344a4-df4f-4233-9e89-89cf167f694e","year":2015},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.700982Z"},"links":{"citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:32b874c76a1f5e1b5f533a42dd3f98b44908803e1edb56c12bb2109a6be6e661","observation_id":"8aa7a5c7-61aa-48a0-8346-5f5337425c4f","resolution":{"observed_at":"2026-08-16T11:12:59.944862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T11:12:59.911742Z","title":"in-distribution","venue":null,"work_id":"99fb9229-6a34-472e-9ea8-4430dc3046b5","year":2016},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.717714Z"},"links":{"citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:629cb2badb05fdd7cc7700dc6314aa52b7e6680b10f4b22271818a1698c158b8","observation_id":"f001f79e-eb31-4708-b397-c08bb7d2826a","resolution":{"observed_at":"2026-08-16T11:12:59.915520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T11:12:59.896834Z","title":"extraneous","venue":null,"work_id":"2402600b-cca5-4f27-870d-b8d194bb1cd5","year":2020},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.725083Z"},"links":{"citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:f45dd80d7c2592af46e9d164d8adebe73d40096b90d6ae3efb4011bf7a544028","observation_id":"2f2b843f-3d20-4bb2-b130-ec9e7cb3f670","resolution":{"observed_at":"2026-08-16T11:12:59.901982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T11:13:00.022646Z","title":"Zhang, Shaoqing Ren, and Jian Sun","venue":null,"work_id":"b899bed0-b528-451f-b7c1-a00162a787b2","year":2016},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":2004,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.670533Z"},"links":{"citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:90ecdd3a7738e11188167cf8a26085e7305fb19d1260c3edc1d3b930f5faaeb1","observation_id":"139aac4e-0964-4438-afdb-c64e2c426b6b","resolution":{"observed_at":"2026-08-16T11:13:00.028538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T11:13:00.072084Z","title":"An open-source platform for underwater image and video analytics","venue":null,"work_id":"b38bbda8-793b-45bb-95a8-c482453f3287","year":2017},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":2008,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.648014Z"},"links":{"citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:6d0c0f04d71e8e8e45e324b1b32083e653dfb7b3a3ea0034187612b3aea3f8e9","observation_id":"6b9a3b2c-d562-4f52-8cc8-9241965e964e","resolution":{"observed_at":"2026-08-16T11:13:00.076458Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T11:13:00.004530Z","title":null,"venue":null,"work_id":"d8a4119d-25f3-46ec-b604-4a934f44a08b","year":1956},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.675397Z"},"links":{"citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:83766420881078a26dba94d955a35348bab66779e6a67a591c4cef5bd685bda4","observation_id":"f361b2ef-f449-4b3b-a78a-21849a2e026d","resolution":{"observed_at":"2026-08-16T11:13:00.009818Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1612.00889","last_updated":"2022-09-18T13:42:11Z","snapshot_observed_at":"2026-08-14T21:27:20.200969Z","submitted_at":"2016-12-02T23:04:16Z","title":"New Frameworks for Offline and Streaming Coreset Constructions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.00889","snapshot_observed_at":"2026-08-16T11:12:59.631115Z","title":"New frameworks for offline and streaming coreset constructions","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.631115Z"},"links":{"cited_paper":"/paper/1612.00889","citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:c153c027d8514b7f5c25344de8e8780573c9f9cbf98b37893fcd7ac494fe486b","observation_id":"ffc13944-4bb3-49d7-a45f-02974818bce5","resolution":{"observed_at":"2026-08-16T11:12:59.631115Z","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":"2026-08-16T11:13:00.057117Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":"65cc421c-7fc2-4dcc-ac8e-f35d630e832d","year":2009},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.653229Z"},"links":{"citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:74c379353aa67a919c397f6a225f10a26f0244a1a4cd8f962eb3d33c41ed63f9","observation_id":"e0b6f37f-8d59-4371-9000-efc2eaf734ea","resolution":{"observed_at":"2026-08-16T11:13:00.063345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-16T11:13:00.100401Z","title":"The iwildcam 2021 competition dataset,","venue":null,"work_id":"2afaa64d-1bda-4706-9188-9bbdd55d28bc","year":2021},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.622039Z"},"links":{"citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:17b4cfba03eaac56fe00ae15456091f2247fba9ce31fb277e1485ee3bab2bb9b","observation_id":"1f05a677-7bf7-431d-8e8b-b1b9a7caa592","resolution":{"observed_at":"2026-08-16T11:13:00.104759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-16T11:12:59.636255Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.636255Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:62c833f0fd6c7020ae4a1ef30d6ec5e434ba420b7ce7c42d5faf5a14a72633fb","observation_id":"ac244540-d017-4716-b9c9-31daece11d9d","resolution":{"observed_at":"2026-08-16T11:12:59.636255Z","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":"2026-08-16T11:13:00.085560Z","title":"Majewski, Shreyasee Mukherjee, Stanley Chan, John Morgan, Vivek Rathod, and Jonathan Huang","venue":null,"work_id":"9f48d90b-85c1-4428-be64-1e7d72e78156","year":2022},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.626888Z"},"links":{"citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:518ceb3192ae320fe8dcfda5f9a8f549f023dc70bcde31cdb902d45bffe99061","observation_id":"0dbd61fa-096f-4bf5-9699-0f18b9b2831f","resolution":{"observed_at":"2026-08-16T11:13:00.089991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-16T09:25:53.087782Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-16T11:12:59.657095Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.657095Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:df74f111876df2909a850587c7bdfcd5626e431dcf4b2b5bd757acc866deaba8","observation_id":"9fa48ff3-55cb-4f05-8ccc-78b2fd7af2ec","resolution":{"observed_at":"2026-08-16T11:12:59.657095Z","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":"2026-08-16T11:12:59.987136Z","title":"Living planet report 2020: Bending the curve of biodiversity loss,","venue":null,"work_id":"50e49794-560a-4180-974b-c6ef59e563d7","year":2020},"citing_paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-16T11:12:59.679885Z"},"links":{"citing_paper":"/paper/2504.16277"},"observation_digest":"sha256:0a2512a7cd4a5ea1cb4abc2c6b9943c55f555e75c770403ee5be13adc3f69e6d","observation_id":"0af5b4a2-d719-4dde-aad0-2cc0d7801501","resolution":{"observed_at":"2026-08-16T11:12:59.992830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2504.16277","last_updated":"2025-04-22T21:25:14Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-19T17:41:21.787001Z","submitted_at":"2025-04-22T21:25:14Z","title":"DataS^3: Dataset Subset Selection for Specialization"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":13},"total_outbound_references":20},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 3 inbound Pith citation observations for arXiv:2504.16277."}