{"as_of":"2026-08-06T06:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1c489f99c4038722381c2c1d488399dd13062beabeb67b5e79e0c30335ab7224","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T02:48:56.663253Z","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":"2026-07-09T21:56:38.427283Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1509.08101","last_updated":"2015-09-29T13:44:37Z","snapshot_observed_at":"2026-08-03T18:23:00.117102Z","submitted_at":"2015-09-27T15:26:58Z","title":"Representation Benefits of Deep Feedforward Networks","version":2},"cited_work":{"arxiv_id":"1509.08101","doi":null,"metadata_source":"pith","pith_arxiv_id":"1509.08101","snapshot_observed_at":"2026-07-09T21:56:38.427283Z","title":"Representation Benefits of Deep Feedforward Networks","venue":"cs.LG","work_id":"f576ebc3-a678-428d-881e-7e29f926d7a1","year":2015},"citing_paper":{"arxiv_id":"2401.01335","last_updated":"2024-06-14T21:17:17Z","snapshot_observed_at":"2026-07-06T17:10:56.398607Z","submitted_at":"2024-01-02T18:53:13Z","title":"Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models","version":3},"reference_index":189,"source":"arxiv_source","source_observed_at":"2026-05-14T23:00:20.720030Z"},"links":{"cited_paper":"/paper/1509.08101","citing_paper":"/paper/2401.01335"},"observation_digest":"sha256:98c7153e008b0b807b744e021ae871928040131f039c552d273d407991122d27","observation_id":"bdb41f68-0554-4543-bf03-8457e7adcddd","resolution":{"observed_at":"2026-05-14T23:00:21.238791Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1509.08101","last_updated":"2015-09-29T13:44:37Z","snapshot_observed_at":"2026-08-03T18:23:00.117102Z","submitted_at":"2015-09-27T15:26:58Z","title":"Representation Benefits of Deep Feedforward Networks","version":2},"cited_work":{"arxiv_id":"1509.08101","doi":null,"metadata_source":"pith","pith_arxiv_id":"1509.08101","snapshot_observed_at":"2026-07-09T21:56:38.427283Z","title":"Representation Benefits of Deep Feedforward Networks","venue":"cs.LG","work_id":"f576ebc3-a678-428d-881e-7e29f926d7a1","year":2015},"citing_paper":{"arxiv_id":"2604.05929","last_updated":"2026-04-07T14:31:19Z","snapshot_observed_at":"2026-07-06T22:54:30.567988Z","submitted_at":"2026-04-07T14:31:19Z","title":"ReLU Networks for Exact Generation of Similar Graphs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-10T18:38:54.623816Z"},"links":{"cited_paper":"/paper/1509.08101","citing_paper":"/paper/2604.05929"},"observation_digest":"sha256:60261cc0903bcb6946aff04847b85c2bf51557b5b9eb1729099716ae04e94ed1","observation_id":"182acd69-f219-49d4-8b02-11204011a320","resolution":{"observed_at":"2026-05-11T00:15:51.077355Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1509.08101","last_updated":"2015-09-29T13:44:37Z","snapshot_observed_at":"2026-08-03T18:23:00.117102Z","submitted_at":"2015-09-27T15:26:58Z","title":"Representation Benefits of Deep Feedforward Networks","version":2},"cited_work":{"arxiv_id":"1509.08101","doi":null,"metadata_source":"pith","pith_arxiv_id":"1509.08101","snapshot_observed_at":"2026-07-09T21:56:38.427283Z","title":"Representation Benefits of Deep Feedforward Networks","venue":"cs.LG","work_id":"f576ebc3-a678-428d-881e-7e29f926d7a1","year":2015},"citing_paper":{"arxiv_id":"2605.21451","last_updated":"2026-05-20T17:42:34Z","snapshot_observed_at":"2026-07-06T23:31:56.199912Z","submitted_at":"2026-05-20T17:42:34Z","title":"Approximation Theory for Neural Networks: Old and New","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-05-21T05:38:34.370264Z"},"links":{"cited_paper":"/paper/1509.08101","citing_paper":"/paper/2605.21451"},"observation_digest":"sha256:c29d135fbadb1d6b3df7b05af15c9fa582d3b8c99196477a82226a1b5baeb9d6","observation_id":"d3d4245c-508e-41d5-899b-d14e22286017","resolution":{"observed_at":"2026-05-21T05:39:40.427436Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1509.08101","last_updated":"2015-09-29T13:44:37Z","snapshot_observed_at":"2026-08-03T18:23:00.117102Z","submitted_at":"2015-09-27T15:26:58Z","title":"Representation Benefits of Deep Feedforward Networks","version":2},"cited_work":{"arxiv_id":"1509.08101","doi":null,"metadata_source":"pith","pith_arxiv_id":"1509.08101","snapshot_observed_at":"2026-07-09T21:56:38.427283Z","title":"Representation Benefits of Deep Feedforward Networks","venue":"cs.LG","work_id":"f576ebc3-a678-428d-881e-7e29f926d7a1","year":2015},"citing_paper":{"arxiv_id":"2606.10089","last_updated":"2026-06-08T19:16:32Z","snapshot_observed_at":"2026-08-02T05:36:24.589418Z","submitted_at":"2026-06-08T19:16:32Z","title":"A Theory on Flow Matching with Neural Networks","version":1},"reference_index":215,"source":"arxiv_source","source_observed_at":"2026-06-27T16:59:34.084575Z"},"links":{"cited_paper":"/paper/1509.08101","citing_paper":"/paper/2606.10089"},"observation_digest":"sha256:3d9a82d97b9af3828684e917084832eaa7983f87eea56c58038f779b064ee34e","observation_id":"c4e476b4-a490-4d8a-b755-f85658db17a4","resolution":{"observed_at":"2026-07-03T00:47:30.836559Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1509.08101","last_updated":"2015-09-29T13:44:37Z","snapshot_observed_at":"2026-08-03T18:23:00.117102Z","submitted_at":"2015-09-27T15:26:58Z","title":"Representation Benefits of Deep Feedforward Networks","version":2},"cited_work":{"arxiv_id":"1509.08101","doi":null,"metadata_source":"pith","pith_arxiv_id":"1509.08101","snapshot_observed_at":"2026-07-09T21:56:38.427283Z","title":"Representation Benefits of Deep Feedforward Networks","venue":"cs.LG","work_id":"f576ebc3-a678-428d-881e-7e29f926d7a1","year":2015},"citing_paper":{"arxiv_id":"2606.26705","last_updated":"2026-06-25T07:34:20Z","snapshot_observed_at":"2026-08-06T04:49:54.659745Z","submitted_at":"2026-06-25T07:34:20Z","title":"Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-26T05:19:56.528337Z"},"links":{"cited_paper":"/paper/1509.08101","citing_paper":"/paper/2606.26705"},"observation_digest":"sha256:019a132bf13a8ed8ba7eb1ad66bca455263b5d10585155e3632292196f34bace","observation_id":"13295d5b-952b-4ae8-84fe-f20c5656872d","resolution":{"observed_at":"2026-07-04T13:19:50.632516Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1509.08101","last_updated":"2015-09-29T13:44:37Z","snapshot_observed_at":"2026-08-03T18:23:00.117102Z","submitted_at":"2015-09-27T15:26:58Z","title":"Representation Benefits of Deep Feedforward Networks","version":2},"cited_work":{"arxiv_id":"1509.08101","doi":null,"metadata_source":"pith","pith_arxiv_id":"1509.08101","snapshot_observed_at":"2026-07-09T21:56:38.427283Z","title":"Representation Benefits of Deep Feedforward Networks","venue":"cs.LG","work_id":"f576ebc3-a678-428d-881e-7e29f926d7a1","year":2015},"citing_paper":{"arxiv_id":"2607.07014","last_updated":"2026-07-08T05:14:19Z","snapshot_observed_at":"2026-07-11T23:18:49.353945Z","submitted_at":"2026-07-08T05:14:19Z","title":"Local large deviations for linear-region growth in random piecewise-linear networks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-09T21:48:15.229103Z"},"links":{"cited_paper":"/paper/1509.08101","citing_paper":"/paper/2607.07014"},"observation_digest":"sha256:76889c8e4d812ab44bb73977b909f32b7e9d5fd780227ff85c6443bd34d3026a","observation_id":"a2c15abd-12fc-4bb5-aad0-41e6f4f18923","resolution":{"observed_at":"2026-07-09T21:56:38.428382Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1509.08101","last_updated":"2015-09-29T13:44:37Z","snapshot_observed_at":"2026-08-03T18:23:00.117102Z","submitted_at":"2015-09-27T15:26:58Z","title":"Representation Benefits of Deep Feedforward Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1509.08101","snapshot_observed_at":"2026-07-13T03:41:36.654380Z","title":"2015 , journal =","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.09350","last_updated":"2026-07-10T12:29:58Z","snapshot_observed_at":"2026-08-02T18:48:52.800985Z","submitted_at":"2026-07-10T12:29:58Z","title":"The Cost of Discretization in Functional Linear Regression: Minimax Rates and Adaptation","version":1},"reference_index":113,"source":"arxiv_source","source_observed_at":"2026-07-13T03:41:36.654380Z"},"links":{"cited_paper":"/paper/1509.08101","citing_paper":"/paper/2607.09350"},"observation_digest":"sha256:f402bdf12841dea3b1efac35cdbb516bab99fb01ad2f6d0ec61112aa27ab4ece","observation_id":"32974cc6-3126-4e44-8849-afa0273615ed","resolution":{"observed_at":"2026-07-13T03:41:36.654380Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1509.08101","last_updated":"2015-09-29T13:44:37Z","snapshot_observed_at":"2026-08-03T18:23:00.117102Z","submitted_at":"2015-09-27T15:26:58Z","title":"Representation Benefits of Deep Feedforward Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1509.08101","snapshot_observed_at":"2026-07-14T10:37:37.440024Z","title":"Representation benefits of deep feedforward networks.arXiv preprint arXiv:1509.08101, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.10589","last_updated":"2026-07-12T05:59:12Z","snapshot_observed_at":"2026-08-02T20:25:47.505767Z","submitted_at":"2026-07-12T05:59:12Z","title":"Approximation of Analytic Functions by ReLU Neural Networks with Adjustable Depth and Width","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-14T10:37:37.440024Z"},"links":{"cited_paper":"/paper/1509.08101","citing_paper":"/paper/2607.10589"},"observation_digest":"sha256:c977c136087da058120d06e4091d7ba1a305b143e5796232f91026c987095c71","observation_id":"60dc5fc2-39ff-4e12-bd76-292730487ad4","resolution":{"observed_at":"2026-07-14T10:37:37.440024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1509.08101","last_updated":"2015-09-29T13:44:37Z","snapshot_observed_at":"2026-08-03T18:23:00.117102Z","submitted_at":"2015-09-27T15:26:58Z","title":"Representation Benefits of Deep Feedforward Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1509.08101","snapshot_observed_at":"2026-08-02T02:48:56.663253Z","title":"arXiv preprint arXiv:1509.08101 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.14233","last_updated":"2026-07-15T18:00:44Z","snapshot_observed_at":"2026-08-02T02:47:45.946019Z","submitted_at":"2026-07-15T18:00:44Z","title":"LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks","version":1},"reference_index":106,"source":"arxiv_source","source_observed_at":"2026-08-02T02:48:56.663253Z"},"links":{"cited_paper":"/paper/1509.08101","citing_paper":"/paper/2607.14233"},"observation_digest":"sha256:27f59e9b5787ef7649953825e6486debdea8d7d7c72410324705fa08a7a4b762","observation_id":"9b9a9e38-0e38-4c2c-b320-d3f9da8a1a17","resolution":{"observed_at":"2026-08-02T02:48:56.663253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1509.08101/citation-record","integrity":"/paper/1509.08101/integrity","json":"/paper/1509.08101/citation-record.json","paper":"/paper/1509.08101"},"outbound":[],"paper":{"arxiv_id":"1509.08101","last_updated":"2015-09-29T13:44:37Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T18:23:00.117102Z","submitted_at":"2015-09-27T15:26:58Z","title":"Representation Benefits of Deep Feedforward Networks"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1509.08101."}