{"as_of":"2026-08-10T17:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3be252ec3c599dca002d9932da84b75174d405cf7c9280580c981d3256f6164e","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T01:13:15.449012Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2501.18012/citation-record","integrity":"/paper/2501.18012/integrity","json":"/paper/2501.18012/citation-record.json","paper":"/paper/2501.18012"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:32.682440Z","title":"This requires careful design of differentiable transitions between network sizes","venue":null,"work_id":"7a5c977f-8760-4b54-a78e-0f9301ec70be","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.107137Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:cdde1c97c90133e010211bc650a23eaf148e8444cced8f59c108b5653ce3cd0e","observation_id":"8f71e704-cfa5-4834-bd9b-3f6242bbbded","resolution":{"observed_at":"2026-08-10T01:13:32.710736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:32.464644Z","title":"Both approaches require a smooth transition function ψ(x) that approximates a step function while maintain- ing differentiability","venue":null,"work_id":"aaad63fc-8f38-446c-8e2f-8819cdc50c6c","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.115278Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:e69403d8899a14ff2501a03831f719f124fcbf0b77e79131500e40d2faac88b8","observation_id":"5fd71507-d9e8-4b84-8122-db67ad8b2765","resolution":{"observed_at":"2026-08-10T01:13:32.569436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:32.363973Z","title":"on” (white squares), one partially “on","venue":null,"work_id":"134cd7d3-e3ae-419f-84f2-72ccd8d1fd0b","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.122000Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:a8e23f190dbf05c060d8f00fd28e877f49fe9ec91f96a51e48b4482aa14dd64d","observation_id":"cc4cfd25-6b6c-43c5-8ebc-2beaab720887","resolution":{"observed_at":"2026-08-10T01:13:32.369330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:32.339434Z","title":null,"venue":null,"work_id":"fa2b2bec-46e2-466e-8847-5357ea05636a","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.128018Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:a40ea2f54f65fc219dd3c65de81121a7167ad970c9acfb3f1a73848dd7f932d2","observation_id":"bb4d4a92-a679-4c96-aeb3-e3a5fb91079b","resolution":{"observed_at":"2026-08-10T01:13:32.346994Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:32.297405Z","title":null,"venue":null,"work_id":"33966443-52d2-48ca-ad19-1a9c15f317b2","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.134932Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:0db1569eb11e50ecdedc225a4531bbf536385cf112487d89de2d7d6cfa811b5b","observation_id":"735cce40-5449-4849-9c94-6d544eb1a496","resolution":{"observed_at":"2026-08-10T01:13:32.325820Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:32.144212Z","title":null,"venue":null,"work_id":"3462165d-5382-4ae6-9e92-64d25e8dddbd","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.140581Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:47a5b832d09cf75bf96f790d74e46aae13b57c31648b335a032372237275ed9f","observation_id":"87b67cd6-caaa-4709-93e6-3032eee4c827","resolution":{"observed_at":"2026-08-10T01:13:32.244149Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:31.987623Z","title":null,"venue":null,"work_id":"116de6c5-41c3-4d66-bc34-b551b9ddaf2b","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.160146Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:e56b10e80932a469dcb7841f905f6a3161258e7c2d9a7af56714a68338559ef7","observation_id":"d7b29cae-d710-444f-ba26-09f20789fdd1","resolution":{"observed_at":"2026-08-10T01:13:32.022749Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:31.433458Z","title":null,"venue":null,"work_id":"bfa89b0e-cc45-4c81-a5f3-0fd185c5c199","year":2004},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.283933Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:af5d7784a1dc34962d50bfc3da15ad11b47d5c7fe3c4c28e2e653d02ef5d79ad","observation_id":"4f394f5f-dba0-48dc-9c9b-505cec9709e1","resolution":{"observed_at":"2026-08-10T01:13:31.483211Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:31.780108Z","title":null,"venue":null,"work_id":"4dbc8f08-c05e-443a-9b67-4cd6884affd7","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.175177Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:843ff7904a29425f0991eaf5e4498d12231d07161a9605bca1c3b1936d5240dc","observation_id":"6f581d36-34de-4a91-ac02-6c06dd59d7fb","resolution":{"observed_at":"2026-08-10T01:13:31.830410Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:31.716033Z","title":null,"venue":null,"work_id":"f70f1949-44bd-4020-b623-98f451c3a2c8","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.182841Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:50a272deef3f7f01f864ebede3714e6d18924656acd17e7c5eb1fa6348f9810f","observation_id":"df6d7c43-ba29-4ed7-a92c-2e695e0310bb","resolution":{"observed_at":"2026-08-10T01:13:31.721664Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:31.904652Z","title":"This technique of using an auxiliary weight to set neural network constraints was first used by Jin et al","venue":null,"work_id":"ac1ce771-41c6-4998-8238-5919bcc5767e","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.168257Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:b061dc248b068370987ea3636e586df97bc8ca773600c91a257939d608749606","observation_id":"05fe98ef-4d24-4219-8a99-0107b7f13370","resolution":{"observed_at":"2026-08-10T01:13:31.972985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:31.696419Z","title":null,"venue":null,"work_id":"40d22fdb-b8dc-408c-b265-ddcbc4009993","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.190550Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:486423a2a65a87aff7a951e20a047d4dd10f49a11c91943a8b01168aaf78a752","observation_id":"ed5b0959-c034-4659-b951-745e7bfe25ed","resolution":{"observed_at":"2026-08-10T01:13:31.703197Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:31.679384Z","title":"(22) As N increases, new neurons smoothly activate and join the computation, as shown in figure 1","venue":null,"work_id":"27e8e388-0384-4032-8671-3840794b486d","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.197090Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:bdd321914b170fc5fc2cace8e54643d70154cc7707ec852620031c3f8e00e1d0","observation_id":"ad7e9ca8-22eb-46eb-be38-e67fc766fe3d","resolution":{"observed_at":"2026-08-10T01:13:31.685774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:31.659442Z","title":null,"venue":null,"work_id":"bc93459a-876e-4734-a8fc-b988d49a413e","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.212095Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:11f2b66ca695f22c582573bcb65df8a83ecec229a25b1629821cd2951ea46bfa","observation_id":"12484037-2b64-47d7-8c0b-83445a1003d1","resolution":{"observed_at":"2026-08-10T01:13:31.665164Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:31.641192Z","title":"bigger is better","venue":null,"work_id":"ef08ca0a-f40f-4821-9c9c-d0b9b7485e77","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.223769Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:d5acaba2de75f5d2bcdfc5022a87620a0950ede3d683cdab9fd314f1bb53237d","observation_id":"f96bc5ee-0804-4b00-a403-7afcb0152d0b","resolution":{"observed_at":"2026-08-10T01:13:31.646948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"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":"2026-08-10T01:13:15.230609Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.230609Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:ccdbcd38131facac5a87f6458e825e6f05092197ba0eb432626bbdfe6d8eca60","observation_id":"3dce20bf-a6f3-4203-8e31-0b7fd9aaf11d","resolution":{"observed_at":"2026-08-10T01:13:15.230609Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.02243","last_updated":"2019-06-05T18:40:53Z","snapshot_observed_at":"2026-08-08T05:13:40.118908Z","submitted_at":"2019-06-05T18:40:53Z","title":"Energy and Policy Considerations for Deep Learning in NLP","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.02243","snapshot_observed_at":"2026-08-10T01:13:15.237816Z","title":"Strubell, A","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.237816Z"},"links":{"cited_paper":"/paper/1906.02243","citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:50d6aeebd53059381193e6c700989c57ccbb86d62d32e532a555ff1263bcedb9","observation_id":"11d52dd7-1975-4382-8c40-fc232c9599a0","resolution":{"observed_at":"2026-08-10T01:13:15.237816Z","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":"2026-08-10T01:13:31.622979Z","title":"Ai and compute,","venue":null,"work_id":"5fa9f156-0ca3-4eac-8334-cd3c57fa20da","year":2018},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.244249Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:5febd41a96aba54927aa47fe9477493f84925a6625497480c0b9575e999db824","observation_id":"9c12a7aa-e4ac-4ff8-b168-067ac940b242","resolution":{"observed_at":"2026-08-10T01:13:31.628847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.03635","last_updated":"2019-03-04T15:51:11Z","snapshot_observed_at":"2026-08-05T23:54:27.386622Z","submitted_at":"2018-03-09T18:51:28Z","title":"The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.03635","snapshot_observed_at":"2026-08-10T01:13:15.250799Z","title":"Frankle and M","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.250799Z"},"links":{"cited_paper":"/paper/1803.03635","citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:89adf9220bc1fb137aa9d78b95b7c68c9829097f944c212402f6d2b6fcec5622","observation_id":"f4752ad8-341a-471a-86f7-0115620aabb8","resolution":{"observed_at":"2026-08-10T01:13:15.250799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.09274","last_updated":"2016-06-29T20:36:23Z","snapshot_observed_at":"2026-08-08T23:05:07.027492Z","submitted_at":"2016-06-29T20:36:23Z","title":"Compression of Neural Machine Translation Models via Pruning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.09274","snapshot_observed_at":"2026-08-10T01:13:15.258005Z","title":"See, M.-T","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.258005Z"},"links":{"cited_paper":"/paper/1606.09274","citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:0125487b311793bfe09012e74af92acb77fcd7580cef92073ffd9e87aa01f05f","observation_id":"ab07f22f-8929-4368-8702-a9837140da90","resolution":{"observed_at":"2026-08-10T01:13:15.258005Z","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":"2026-08-10T01:13:31.563600Z","title":null,"venue":null,"work_id":"2efaf445-fa50-47bb-b177-6fdc240cdc05","year":2024},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.264430Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:1403041c71060a0e141ac66eee91e9ffd969e3c3bbc57b5a62584fece4371597","observation_id":"574ca022-04ec-4e1a-b9e4-6899a6f436b7","resolution":{"observed_at":"2026-08-10T01:13:31.610957Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.12200","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:30.826121Z","title":null,"venue":null,"work_id":"ab575bff-04ff-40b4-8603-5029f8fae1a0","year":2023},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.273845Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:72bd4707d108122617adfe848b2b48ca4a679c3b491deef638fd6ef84640e5b1","observation_id":"620c0fe3-900d-4f4d-999b-84f00d547ee8","resolution":{"observed_at":"2026-08-10T01:13:30.845632Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/2601412","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Aggarwal and K","venue":"ACM Computing Surveys","work_id":"f83996d7-46e4-4d17-9f0e-3f65e9e5996f","year":2014},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.289709Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:65c5337350cba018d46edda2eb73e530f00be1105624bfa59ec798f935a06a68","observation_id":"4e3cc05a-9d30-47b8-870c-996b9c033183","resolution":{"observed_at":"2026-08-10T01:13:15.544228Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1103/physreve.90.022801","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Tunc and L","venue":"Physical Review E","work_id":"0f19b3b1-7508-4a52-a606-37d092abf855","year":2014},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.296521Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:4bd30deaa5f6d3b641d24490c4107c9a0959f42c55d96af55604e39bb2a95465","observation_id":"504225b2-0626-4531-81ae-6e625b651399","resolution":{"observed_at":"2026-08-10T01:13:15.509965Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1808.05377","last_updated":"2019-04-26T09:50:47Z","snapshot_observed_at":"2026-07-06T06:55:54.705302Z","submitted_at":"2018-08-16T08:45:01Z","title":"Neural Architecture Search: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1808.05377","snapshot_observed_at":"2026-08-10T01:13:15.304999Z","title":"Elsken, J","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.304999Z"},"links":{"cited_paper":"/paper/1808.05377","citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:a430d37324ae8b48ef94744b25f3ae6269d61009eabe95c4124ea906ea22c6bf","observation_id":"1d958f3a-2779-4464-9c4f-ff6a41bbcaee","resolution":{"observed_at":"2026-08-10T01:13:15.304999Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.03033","last_updated":"2020-03-06T05:06:12Z","snapshot_observed_at":"2026-08-07T07:38:33.089333Z","submitted_at":"2020-03-06T05:06:12Z","title":"What is the State of Neural Network Pruning?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.03033","snapshot_observed_at":"2026-08-10T01:13:15.312961Z","title":"Blalock, J","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.312961Z"},"links":{"cited_paper":"/paper/2003.03033","citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:87f385963ca9b776c0a0f52a8a112d166ae314beb324c4bd15baf47d8caf7ef3","observation_id":"f6ab3b8b-d180-4a93-9bc0-da25697fcb31","resolution":{"observed_at":"2026-08-10T01:13:15.312961Z","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":"2026-08-10T01:13:31.293051Z","title":null,"venue":null,"work_id":"6921594f-cbcd-4c28-b3c1-c7c2b28d6316","year":2002},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.319361Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:73d5f528402bec39b9ea1694b527ed0173a721cab7855dabbe893a67f016aa81","observation_id":"3adb772e-d5b9-4c63-99ab-1bf3b7bfc1b5","resolution":{"observed_at":"2026-08-10T01:13:31.350495Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:31.273598Z","title":"Fahlman and C","venue":null,"work_id":"5a59ce3a-6717-4530-ad88-abaa8877ba9e","year":1989},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.325612Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:6f66758608c29c35ce1748c1f0a5573a383f0a7352001b05edcf5d063c355378","observation_id":"eb8090b1-77ca-4e79-a11b-13f7835d773b","resolution":{"observed_at":"2026-08-10T01:13:31.278990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.04838","last_updated":"2018-02-08T20:40:22Z","snapshot_observed_at":"2026-08-09T09:43:11.255145Z","submitted_at":"2016-06-15T16:15:53Z","title":"Optimization Methods for Large-Scale Machine Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.04838","snapshot_observed_at":"2026-08-10T01:13:15.331410Z","title":"Bottou, F","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.331410Z"},"links":{"cited_paper":"/paper/1606.04838","citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:6768b9cdfe4cce9496fcfc7aa74183017d8aadccc3b2f6864f98bda1b189ed08","observation_id":"de83aef9-a7b7-4be6-8cf2-5f02b53f3068","resolution":{"observed_at":"2026-08-10T01:13:15.331410Z","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":"2026-08-10T01:13:31.241417Z","title":"Allen-Zhu, Y","venue":null,"work_id":"0ac7ef16-2938-43a7-b67a-c3f82ab764cf","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.339183Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:9530c328de941c48bdc5298d7015a8d3b9999f484d8b00f641b0af91eac6f81f","observation_id":"5905fdaf-88ae-4c47-bc79-8c1bdf762871","resolution":{"observed_at":"2026-08-10T01:13:31.249463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1309.5549","last_updated":"2013-09-22T01:35:43Z","snapshot_observed_at":"2026-07-06T03:23:37.989040Z","submitted_at":"2013-09-22T01:35:43Z","title":"Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1309.5549","snapshot_observed_at":"2026-08-10T01:13:15.347461Z","title":"Ghadimi and G","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.347461Z"},"links":{"cited_paper":"/paper/1309.5549","citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:5972a3208212a8e5d262e588e7ce6b4a16bf5625ffe9a860697b990eacc24017","observation_id":"8d72bf8a-4bbf-41de-b861-2ad8af6182fc","resolution":{"observed_at":"2026-08-10T01:13:15.347461Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.04326","last_updated":"2020-02-21T04:48:07Z","snapshot_observed_at":"2026-08-09T01:00:45.702375Z","submitted_at":"2019-04-08T19:43:09Z","title":"A Comparative Analysis of the Optimization and Generalization Property of Two-layer Neural Network and Random Feature Models Under Gradient Descent Dynamics","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.04326","snapshot_observed_at":"2026-08-10T01:13:15.355179Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.355179Z"},"links":{"cited_paper":"/paper/1904.04326","citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:cc4c8715555ce3a11e78e13d904c41db3749b6d688158c72f2cb7f4f1d6eefdd","observation_id":"b716871e-5b7c-4718-bcdc-76d2667bb706","resolution":{"observed_at":"2026-08-10T01:13:15.355179Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.00451","last_updated":"2022-02-24T18:29:39Z","snapshot_observed_at":"2026-08-10T14:10:39.799472Z","submitted_at":"2022-02-24T18:29:39Z","title":"Physics-Informed Neural Networks for Quantum Eigenvalue Problems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.00451","snapshot_observed_at":"2026-08-10T01:13:15.362660Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.362660Z"},"links":{"cited_paper":"/paper/2203.00451","citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:45ca043698edf11b814dd079ae5811e73bea3857a34d76b3486408cc1ea2fa5c","observation_id":"50a180f8-3ef6-4a0a-b26c-05ecb0fd1365","resolution":{"observed_at":"2026-08-10T01:13:15.362660Z","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":"2026-08-10T01:13:31.223216Z","title":"Wolfram Research, Inc., Mathematica,","venue":null,"work_id":"b9a62c97-16cb-4974-88d7-98a93f1d33f6","year":2024},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.377448Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:2b4daa608756f7b2190a7686df059e9dba43872f245be95dd1c7b51a5f16090a","observation_id":"c485027d-ad7c-4ac8-af18-1fa0e02225a9","resolution":{"observed_at":"2026-08-10T01:13:31.229083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:15.387268Z","title":"JAX: com- posable transformations of Python+NumPy programs,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.387268Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:540f9e14864f5221e5e904a31a71cb0d8d488ded2433c07345f6543f517f7d75","observation_id":"c1e3d931-58ec-4fa4-bbe0-6bcdbfb81696","resolution":{"observed_at":"2026-08-10T01:13:15.387268Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:15.395527Z","title":"Kidger and C","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.395527Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:2c688bc10f0c457b1f7c55974751deb1ec8bea581b9a4faac4bed52ac285c858","observation_id":"053d1113-7261-4851-ae80-245695dfb549","resolution":{"observed_at":"2026-08-10T01:13:15.395527Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:15.404568Z","title":"The DeepMind JAX Ecosystem,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.404568Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:190d023b8d64611648b8bfa8fe9b2d6d028575eb71a9948ffa87b7c088a5dba7","observation_id":"5a634493-ee1f-4d96-9ea5-375b2f0f8a70","resolution":{"observed_at":"2026-08-10T01:13:15.404568Z","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":"2026-08-10T01:13:31.015834Z","title":null,"venue":null,"work_id":"7ac02548-6980-4d3f-adf0-ac7640d481fe","year":null},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.412339Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:228a500ca551f63656c5d67df0c71e4ffcbe82bf56b31205d96e4cebde233cc4","observation_id":"2b8022d9-cec7-484f-8e71-5f1af4a297a2","resolution":{"observed_at":"2026-08-10T01:13:31.097235Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:30.964886Z","title":null,"venue":null,"work_id":"b188a6eb-a78d-48f6-bf49-fdec99ef547a","year":2007},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.422978Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:c11fafd86d493d16aa5e85c7f032623d7ebfb648d93f27540d2d249476521cd9","observation_id":"000914fe-30b4-4d89-9c36-2c22c4b9de7f","resolution":{"observed_at":"2026-08-10T01:13:30.970973Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T01:13:30.945368Z","title":"Sarao Mannelli, Y","venue":null,"work_id":"3cd4cb1e-afff-4373-bcb2-0bee30712f5c","year":2024},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.430806Z"},"links":{"citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:6ef4998249388d6571daf6a0f31858b61e951d6763d30a05b928faa137fa28e7","observation_id":"a72cd15a-0736-445f-8236-703e35d861cd","resolution":{"observed_at":"2026-08-10T01:13:30.950682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.14160","last_updated":"2025-03-01T12:02:55Z","snapshot_observed_at":"2026-08-09T16:06:55.397757Z","submitted_at":"2024-09-21T14:43:54Z","title":"Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.14160","snapshot_observed_at":"2026-08-10T01:13:15.440696Z","title":"Varoquaux, A","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.440696Z"},"links":{"cited_paper":"/paper/2409.14160","citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:dd2ac54599d58eed432b9989d5325a9d5e8a01149c6bfab10ba2c1cccac4eb59","observation_id":"d368004d-aecc-4120-8bf5-55451a16e8de","resolution":{"observed_at":"2026-08-10T01:13:15.440696Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.05125","last_updated":"2022-06-07T15:29:01Z","snapshot_observed_at":"2026-08-07T08:07:45.534113Z","submitted_at":"2022-01-13T18:30:18Z","title":"GradMax: Growing Neural Networks using Gradient Information","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.05125","snapshot_observed_at":"2026-08-10T01:13:15.449012Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T01:13:15.449012Z"},"links":{"cited_paper":"/paper/2201.05125","citing_paper":"/paper/2501.18012"},"observation_digest":"sha256:6b144a239431c0a148d1a1f6cbd3f40589b1a6f4b26ee04d3ede1fe416a6da08","observation_id":"18ab6f87-14c5-48b3-94e2-cd7596422772","resolution":{"observed_at":"2026-08-10T01:13:15.449012Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.18012","last_updated":"2025-07-25T20:18:00Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T14:11:45.607582Z","submitted_at":"2025-01-29T21:56:38Z","title":"Growing Neural Networks: Dynamic Evolution through Gradient Descent"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":28,"verified_exact":2,"verified_fuzzy":11},"total_outbound_references":42},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2501.18012."}