{"as_of":"2026-08-20T19:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3cb87d112a8e53d4184c0458677eb8d06c8b93dc41fb937cbf8d4536f7c070de","coverage":[{"denominator":24,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T12:07:58.663896Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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":"2026-06-26T16:53:53.441274Z","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":"2026-07-04T04:39:34.368247Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"cited_work":{"arxiv_id":"2504.13586","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.13586","snapshot_observed_at":"2026-07-04T04:39:34.368247Z","title":"arXiv preprint arXiv:2504.13586 , year=","venue":null,"work_id":"a2cd5d6f-7154-4811-922c-b84595bcb51e","year":null},"citing_paper":{"arxiv_id":"2606.19998","last_updated":"2026-06-18T09:34:22Z","snapshot_observed_at":"2026-08-06T01:11:24.313445Z","submitted_at":"2026-06-18T09:34:22Z","title":"Tri-Info: Generalizable, Interpretable Failure Prediction for VLA Models via Information Theory","version":1},"reference_index":247,"source":"arxiv_source","source_observed_at":"2026-06-26T16:53:53.441274Z"},"links":{"cited_paper":"/paper/2504.13586","citing_paper":"/paper/2606.19998"},"observation_digest":"sha256:819d336ae9c69422a4567d01297e7d2987cd031fd731799f2a7b8acb4da36564","observation_id":"7b486d5d-cee4-4b60-9633-64c8496873a8","resolution":{"observed_at":"2026-07-04T04:39:34.369852Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2504.13586/citation-record","integrity":"/paper/2504.13586/integrity","json":"/paper/2504.13586/citation-record.json","paper":"/paper/2504.13586"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T12:07:58.929318Z","title":"Ahmad, R","venue":null,"work_id":"a481c10e-f2ec-402c-8618-9942d2abb349","year":2022},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.578571Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:31572d5592acabda4f29d9bdd48a0640f37c1a08d1daa67f8338d1927733e1b0","observation_id":"4e9c692f-398a-41bb-a0b4-4d7b6d51b551","resolution":{"observed_at":"2026-08-16T12:07:58.932784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T12:07:58.917459Z","title":"Green ai,","venue":null,"work_id":"cfdceec0-ff96-4a9e-a580-d45572cf6a1b","year":2020},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.582801Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:280db171307a83e630b645d60b680655cbc876a6daf950c46ab3d77ef14b6cab","observation_id":"27c68b36-d39c-43da-ac74-13c1e6b75c10","resolution":{"observed_at":"2026-08-16T12:07:58.921059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T12:07:58.906622Z","title":"A systematic review of green ai,","venue":null,"work_id":"8f323daf-bffe-4dbe-a936-2ac5a9868d84","year":2023},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.586603Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:6e9c51bd1282976a539291c486f48181639e9c26052a5d929456bc5967328c78","observation_id":"14cb5ba3-6045-4a25-b727-2cc45f24478d","resolution":{"observed_at":"2026-08-16T12:07:58.910261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T12:07:58.896669Z","title":"Toward green ai: A methodological survey of the scientiﬁc literature,","venue":null,"work_id":"63665aa9-ed25-4381-8cfe-e274b82f866c","year":2024},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.590329Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:34d31f5a122897e562e5a1148918d88553b6e0d4e6402d4eb4dae63b8f6d9c61","observation_id":"cf73320e-8527-4197-a860-021969299da6","resolution":{"observed_at":"2026-08-16T12:07:58.900058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.04128","last_updated":"2021-04-09T01:03:17Z","snapshot_observed_at":"2026-08-18T07:04:50.982806Z","submitted_at":"2021-04-09T01:03:17Z","title":"An Empirical Comparison of Instance Attribution Methods for NLP","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.04128","snapshot_observed_at":"2026-08-16T12:07:58.594433Z","title":"An empirical comparison of instance attribution methods for nlp,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.594433Z"},"links":{"cited_paper":"/paper/2104.04128","citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:9f1f456443a732f9382c25768145f4b08e9494ca2210d6759d79948d3a480352","observation_id":"04e93516-dbb8-4692-9871-5a66e6527cee","resolution":{"observed_at":"2026-08-16T12:07:58.594433Z","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":"2026-08-16T12:07:58.886613Z","title":"Iaeval: A comprehensive evaluation of in- stance attribution on natural language understanding,","venue":null,"work_id":"8e20a55b-7645-4aad-8630-d96d8d7307ee","year":2023},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.598563Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:9ca4748f078c6ec18a2213fb123039a5942072577f03966e16c2256760c0d99b","observation_id":"a7296619-0eab-4647-b420-35098e0e2051","resolution":{"observed_at":"2026-08-16T12:07:58.890071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T12:07:58.876101Z","title":"Machine learning explainability in ﬁnance: an application to default risk analysis,","venue":null,"work_id":"a8a3226b-be85-4083-9452-f5bd7d2b5b80","year":2019},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.602278Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:25592567ded32f28a3a4a04db0f04958586d78ad5053606a1c067ffbe51cf843","observation_id":"5d826adb-c8b8-4cd3-bc4e-8a0eb1251ad3","resolution":{"observed_at":"2026-08-16T12:07:58.880602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.14999","last_updated":"2023-07-19T17:09:07Z","snapshot_observed_at":"2026-08-16T19:02:10.689041Z","submitted_at":"2020-11-30T17:05:48Z","title":"An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference?","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.14999","snapshot_observed_at":"2026-08-16T12:07:58.606269Z","title":"An au- tomatic ﬁnite-sample robustness metric: When can dropping a little data make a big difference?","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.606269Z"},"links":{"cited_paper":"/paper/2011.14999","citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:b383bb61dea00e936f900178b9e76cb6d36cda549337232c4c74ec44d207d143","observation_id":"970f42ea-2efc-479e-9f11-87fb5b961d09","resolution":{"observed_at":"2026-08-16T12:07:58.606269Z","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":"2026-08-16T12:07:58.866071Z","title":"Explainable machine learning for pub- lic policy: Use cases, gaps, and research directions,","venue":null,"work_id":"b546f6e0-c615-4531-a181-412a2712090e","year":2023},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.610600Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:b648297b00bcfb137902cee1b8d41dc56e3c27f108d03bacc2c9799f9bde9a28","observation_id":"ca7bf6ca-c796-4dd6-9b79-7a3db855dac2","resolution":{"observed_at":"2026-08-16T12:07:58.869536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T12:07:58.855212Z","title":"Understanding black-box predictions via inﬂuence functions,","venue":null,"work_id":"970a0b9c-1bb7-422b-83f8-ac17e52777f9","year":2017},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.614422Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:084ad3d913165b82aa0aab65c0288bd421f4dd11be3e90da1e9b341ed55be2f5","observation_id":"02f46094-338b-4d78-8411-42c9db0cb4e4","resolution":{"observed_at":"2026-08-16T12:07:58.858832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.05429","last_updated":"2020-11-10T22:23:25Z","snapshot_observed_at":"2026-08-16T19:06:45.483849Z","submitted_at":"2020-11-10T22:23:25Z","title":"Debugging Tests for Model Explanations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.05429","snapshot_observed_at":"2026-08-16T12:07:58.618141Z","title":"De- bugging tests for model explanations,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.618141Z"},"links":{"cited_paper":"/paper/2011.05429","citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:7125065d7649ecc011d5691fa02101560c83d45da1627ca212092355bedf2754","observation_id":"754fb802-294c-40e2-90f0-ea58189ed365","resolution":{"observed_at":"2026-08-16T12:07:58.618141Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.06676","last_updated":"2020-05-14T00:45:23Z","snapshot_observed_at":"2026-08-12T03:16:24.297659Z","submitted_at":"2020-05-14T00:45:23Z","title":"Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.06676","snapshot_observed_at":"2026-08-16T12:07:58.621935Z","title":"Explain- ing black box predictions and unveiling data ar- tifacts through inﬂuence functions,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.621935Z"},"links":{"cited_paper":"/paper/2005.06676","citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:64c65e0b689e589c3b04a40a613b520965e0a61172326cdcc30fa713d66787a4","observation_id":"f0bd0200-501c-4931-bcbe-ea8674f3db22","resolution":{"observed_at":"2026-08-16T12:07:58.621935Z","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":"2026-08-16T12:07:58.844223Z","title":"Interactive label cleaning with example- based explanations,","venue":null,"work_id":"aaa31e97-6224-4307-906d-7938b01fc633","year":2021},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.626519Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:439194f1474388408dcac0a47ea4c57472751938d98ffd39aa6a7eb63b79b09d","observation_id":"7baed70b-9bce-4806-a7c3-3df897cad2c9","resolution":{"observed_at":"2026-08-16T12:07:58.847804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T12:07:58.833304Z","title":null,"venue":null,"work_id":"3ede55b5-eb9d-4129-814e-1830db888a96","year":1982},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.629981Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:a037b7f3fb0c792d9976c3649599f294a83bc699b8909679e9eefbc75934af20","observation_id":"5485e186-8419-4c1c-9bb0-96886be3223a","resolution":{"observed_at":"2026-08-16T12:07:58.836965Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T12:07:58.822471Z","title":"How many and which training points would need to be removed to ﬂip this prediction?","venue":null,"work_id":"c4ac32d1-6055-4698-a10a-598d2465cd0c","year":2023},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.633521Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:78f9245e29db368f00c0bfb4a52f96ab57b93239cc4e9c370228ac87fd9cbfb2","observation_id":"dae6e0cb-31d8-4bd9-b138-7331861d6691","resolution":{"observed_at":"2026-08-16T12:07:58.826076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T12:07:58.812047Z","title":"Relabeling minimal training subset to ﬂip a prediction,","venue":null,"work_id":"0996e09b-605c-40cc-aca5-ed454938c3de","year":2024},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.636824Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:39b40068ef20441e6108ba9cf43c6a68ebc3130a381959af434bfab977e929cf","observation_id":"54365429-f156-437c-abe0-f576f0f4e12f","resolution":{"observed_at":"2026-08-16T12:07:58.815741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T12:07:58.801787Z","title":"The inﬂuence curve and its role in ro- bust estimation,","venue":null,"work_id":"80d2fc80-1527-4e02-bbcf-c3a2d1f1bb63","year":1974},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.640037Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:3654d3cd05bcf4a98dbb018e17b96820a859afab97a0599d436c5aadfd696ee7","observation_id":"1bd74221-ece7-4439-a833-402dcffd6974","resolution":{"observed_at":"2026-08-16T12:07:58.805199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T12:07:58.791396Z","title":"Characterizations of an empirical inﬂuence function for detecting inﬂuential cases in regression,","venue":null,"work_id":"ca65ada7-cea0-414e-9531-ff66f8530989","year":1980},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.643150Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:487ad1e421ce234ce0d20559f9c6857038d6e24099d7ddd9dc51f9e7d12a2bf8","observation_id":"299fb4ad-de52-4cd7-9ada-b95a01d7d1b7","resolution":{"observed_at":"2026-08-16T12:07:58.795222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11577","last_updated":"2023-08-07T12:33:20Z","snapshot_observed_at":"2026-08-18T17:58:08.799481Z","submitted_at":"2021-08-26T04:42:24Z","title":"Machine Unlearning of Features and Labels","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.11577","snapshot_observed_at":"2026-08-16T12:07:58.646404Z","title":"Machine unlearning of features and labels,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.646404Z"},"links":{"cited_paper":"/paper/2108.11577","citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:1987140d5801ebaf2ea98f211351f78870a6b31c96d73653ba926055b090181c","observation_id":"eed6243c-2cc3-4641-91a1-2df66f22f3f4","resolution":{"observed_at":"2026-08-16T12:07:58.646404Z","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":"2026-08-16T12:07:58.780512Z","title":"Resolving training biases via inﬂuence-based data relabeling,","venue":null,"work_id":"cd12e203-0229-4dda-b288-6a341b17a3aa","year":2021},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.649941Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:a948480d24eb5ab4424ea170503e02cced07b5615a4a78110d5c8f02c077fe83","observation_id":"5e3c6b1b-baf1-4a43-b52e-b13d9a03caa8","resolution":{"observed_at":"2026-08-16T12:07:58.784495Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T12:07:58.769203Z","title":"Datamodels: Understanding predictions with data and data with predictions,","venue":null,"work_id":"cf390cbb-a721-4122-9e68-40793a20bbcd","year":2022},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.653260Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:9af77b146d838d52d33e0fccd04905a0fcce10aed256b9c180895af6af3d4597","observation_id":"c23739ec-d022-4be0-a038-d41f1fc6d41b","resolution":{"observed_at":"2026-08-16T12:07:58.772895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.09901","last_updated":"2023-05-02T14:46:44Z","snapshot_observed_at":"2026-08-16T16:59:01.582022Z","submitted_at":"2022-05-19T23:47:25Z","title":"Cardinality-Minimal Explanations for Monotonic Neural Networks","version":3},"cited_work":{"arxiv_id":"2205.09901","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.09901","snapshot_observed_at":"2026-08-16T12:07:58.700430Z","title":"Cardinality-Minimal Explanations for Monotonic Neural Networks","venue":"cs.LG","work_id":"3253a076-0205-4249-97fb-79072a56ddee","year":2022},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.656613Z"},"links":{"cited_paper":"/paper/2205.09901","citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:95be925c4afce26e38a88e2511273885d014c5b7a3d1983a3c55d4346edc567e","observation_id":"6e48b955-a865-4cb8-b73c-cbc1b8b1c9ef","resolution":{"observed_at":"2026-08-16T12:07:58.705693Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.12434","last_updated":"2020-02-14T22:32:46Z","snapshot_observed_at":"2026-07-06T08:25:01.425019Z","submitted_at":"2019-09-26T23:25:25Z","title":"Learning the Difference that Makes a Difference with Counterfactually-Augmented Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.12434","snapshot_observed_at":"2026-08-16T12:07:58.660399Z","title":"Learn- ing the difference that makes a difference with counterfactually-augmented data,","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.660399Z"},"links":{"cited_paper":"/paper/1909.12434","citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:7bd76b4fe9827a72328e60df254d027f376f2fee95ef67eeb96a1de8227b2d33","observation_id":"5d060ea3-66e5-4a43-94c2-cec3bf7bda19","resolution":{"observed_at":"2026-08-16T12:07:58.660399Z","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":"2026-08-16T12:07:58.757594Z","title":"Recursive deep models for semantic compositionality over a sentiment tree- bank,","venue":null,"work_id":"d6bb4bb6-1137-4a06-a834-c80ce3c4ec90","year":2013},"citing_paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T12:07:58.663896Z"},"links":{"citing_paper":"/paper/2504.13586"},"observation_digest":"sha256:285d705cd0eaa1d160e1b7a159ca8b750eab92aafd8cdc7960d92f535a5483f9","observation_id":"d0b24f80-2beb-450d-ab55-3f3d53338f17","resolution":{"observed_at":"2026-08-16T12:07:58.761383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2504.13586","last_updated":"2025-04-18T09:38:26Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-20T15:05:51.936038Z","submitted_at":"2025-04-18T09:38:26Z","title":"How to Achieve Higher Accuracy with Less Training Points?"},"reference_resolution":{"displayed":24,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":1,"verified_fuzzy":16},"total_outbound_references":24},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 1 inbound Pith citation observation for arXiv:2504.13586."}