{"as_of":"2026-08-05T00:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7c8cfcf7334883daa8e2c93e7b5af0d47044c7a6675dc2da68e1a9336881c7b3","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T22:56:43.393302Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+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/2605.26248/citation-record","integrity":"/paper/2605.26248/integrity","json":"/paper/2605.26248/citation-record.json","paper":"/paper/2605.26248"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:56:43.393302Z","title":"Revisiting neural scaling laws in language and vision","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:bf7c488b8320868c58472fde9e768cdc28a4cacca47c2642626461f6bb82f8bc","observation_id":"92720e21-d356-46a4-870c-bb0438a3add5","resolution":{"observed_at":"2026-06-29T22:56:43.393302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.06701","last_updated":"2024-04-29T00:55:09Z","snapshot_observed_at":"2026-07-06T10:40:56.959075Z","submitted_at":"2021-02-12T18:57:46Z","title":"Explaining Neural Scaling Laws","version":2},"cited_work":{"arxiv_id":"2102.06701","doi":"10.48550/arxiv.2102.06701","metadata_source":"arxiv_reference","pith_arxiv_id":"2102.06701","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"arXiv preprint arXiv:2102.06701 , year=","venue":null,"work_id":"0fa16ccb-6598-43b8-b692-f0d5c0c1eb14","year":2024},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/2102.06701","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:9ac0c7ecf8df78477220c72e76ce996808f15b9bc32bfe00b703f0386acf98a1","observation_id":"ded477fb-e46d-4881-ab10-1252003f3a14","resolution":{"observed_at":"2026-06-29T23:44:02.780941Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-29T22:56:43.393302Z","title":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:5314c2383c6502bb67b60d01d7bd659adfded6636522d1d855eb198a3700ea90","observation_id":"7f1b0def-28c0-4f73-bb57-b737af7f5a81","resolution":{"observed_at":"2026-06-29T22:56:43.393302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.14891","last_updated":"2023-07-24T00:05:04Z","snapshot_observed_at":"2026-07-06T14:10:47.816961Z","submitted_at":"2022-10-26T17:45:01Z","title":"Broken Neural Scaling Laws","version":17},"cited_work":{"arxiv_id":"2210.14891","doi":"10.48550/arxiv.2210.14891","metadata_source":"arxiv_reference","pith_arxiv_id":"2210.14891","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"arXiv preprint arXiv:2210.14891 , year=","venue":null,"work_id":"a38cac17-c32c-446e-bd52-c243853b456e","year":2019},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/2210.14891","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:fe92928b10bb6ce97f347d07be195e7dfffac24ecc381cccc06b0e3650cf2480","observation_id":"e4469700-8701-48ee-9602-bd35c4b850b2","resolution":{"observed_at":"2026-06-29T23:44:02.768783Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-29T22:56:43.393302Z","title":"Leveraging procedural generation to benchmark reinforcement learning","venue":null,"work_id":null,"year":2048},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:05408f7c9fff0a9834da7027f2ee9c1110d551cc7b95ee190137de9000ca4477","observation_id":"1e97d7bf-66f6-4c69-b567-d5e9d555c644","resolution":{"observed_at":"2026-06-29T22:56:43.393302Z","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":"10.1007/bf02551274","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T15:24:49.578172Z","title":"Karin Dahmen and James P","venue":null,"work_id":"0d9cf313-0575-4a7e-8284-01825c04bdca","year":1989},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:372de902e6fa6a0c573425ee74dbdf7fd335878e9478fe788d433d81741cdc62","observation_id":"9020ff1e-9aa4-4b80-9ed9-ec61b328e730","resolution":{"observed_at":"2026-06-29T23:04:00.783898Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-07-11T20:19:03.937296+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T20:19:03.937296+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":"2010.11929","doi":"10.1175/jcli-d-22-0357.1","metadata_source":"pith","pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","venue":"cs.CV","work_id":"e96730e3-129b-4db6-b981-15ab7932e297","year":2020},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:cfffecbac8100ac5f5b78a4231d316d9af0fd505ddcab4f667d68bf67785867b","observation_id":"30d1807a-4009-402c-b7f9-35eb2aca00c2","resolution":{"observed_at":"2026-06-29T23:44:02.802271Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.02677","last_updated":"2018-04-30T21:53:41Z","snapshot_observed_at":"2026-07-06T05:46:07.424788Z","submitted_at":"2017-06-08T16:51:53Z","title":"Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour","version":2},"cited_work":{"arxiv_id":"1706.02677","doi":"10.48550/arxiv.1706.02677","metadata_source":"pith","pith_arxiv_id":"1706.02677","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour","venue":"cs.CV","work_id":"f3dc32a4-cf81-467b-8ff4-3b2f21d3bf1f","year":2017},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/1706.02677","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:9a65d81f9400d6266b2403dd3153816e09f217add4f678e9caf1e5eb4222d353","observation_id":"961d062b-3897-41a6-9c47-7218239ea025","resolution":{"observed_at":"2026-06-29T23:44:03.376455Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-07-13T20:21:09.485311+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T20:21:09.485311+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.14701","last_updated":"2020-11-06T04:16:36Z","snapshot_observed_at":"2026-07-06T10:09:17.078776Z","submitted_at":"2020-10-28T02:17:24Z","title":"Scaling Laws for Autoregressive Generative Modeling","version":2},"cited_work":{"arxiv_id":"2010.14701","doi":"10.48550/arxiv.2010.14701","metadata_source":"pith","pith_arxiv_id":"2010.14701","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Scaling Laws for Autoregressive Generative Modeling","venue":"cs.LG","work_id":"1f180c21-02d6-4b11-9dfc-08d7f0d8fc81","year":2020},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/2010.14701","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:d26faeab17663f55cce1bd3a75ae93888e300688f99300379a56f9c19fc30929","observation_id":"3506b622-c8cd-4c65-96e3-74f37500e5d6","resolution":{"observed_at":"2026-06-29T23:44:02.790523Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.01293","last_updated":"2021-02-02T04:07:38Z","snapshot_observed_at":"2026-08-01T22:46:19.170916Z","submitted_at":"2021-02-02T04:07:38Z","title":"Scaling Laws for Transfer","version":1},"cited_work":{"arxiv_id":"2102.01293","doi":"10.48550/arxiv.2102.01293","metadata_source":"pith","pith_arxiv_id":"2102.01293","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Scaling Laws for Transfer","venue":"cs.LG","work_id":"c58fc635-4090-4314-adf6-b45af9da58e5","year":2021},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/2102.01293","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:a9c685df80349527367b6ed16ba043f92c17bcb707bc30be0dc0ade99faedb1e","observation_id":"8d1a6a52-e7e7-41e4-9998-83daf80bc95c","resolution":{"observed_at":"2026-06-29T23:44:03.370701Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.00409","last_updated":"2017-12-01T17:13:14Z","snapshot_observed_at":"2026-07-06T06:12:18.811024Z","submitted_at":"2017-12-01T17:13:14Z","title":"Deep Learning Scaling is Predictable, Empirically","version":1},"cited_work":{"arxiv_id":"1712.00409","doi":null,"metadata_source":"pith","pith_arxiv_id":"1712.00409","snapshot_observed_at":"2026-07-04T18:40:03.351817Z","title":"Deep Learning Scaling is Predictable, Empirically","venue":"cs.LG","work_id":"3638ccb4-3a4f-460e-8b6f-867a65922801","year":2017},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/1712.00409","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:d81f3136190b286922d8aad1c78c2843ce5a26519d8c1dc886d18c26f8f82f46","observation_id":"2b27d33d-9f65-4844-8fcf-11af0c013ebe","resolution":{"observed_at":"2026-06-29T23:44:03.365102Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.13442","last_updated":"2023-02-19T01:24:51Z","snapshot_observed_at":"2026-08-03T11:18:05.491482Z","submitted_at":"2023-01-31T06:38:53Z","title":"Scaling laws for single-agent reinforcement learning","version":2},"cited_work":{"arxiv_id":"2301.13442","doi":"10.48550/arxiv.2301.13442","metadata_source":"arxiv_reference","pith_arxiv_id":"2301.13442","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Scaling laws for single-agent reinforcement learning","venue":null,"work_id":"1e0ec60d-c971-4dc2-97f0-93b6e25e8490","year":2023},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/2301.13442","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:8aeddaa69cc6d53066a82db6670d28ad28b391d24ddc980c20f3d32f675ff41f","observation_id":"719437c9-6b3a-4e62-b126-ab6bf2194b83","resolution":{"observed_at":"2026-06-29T23:44:03.368224Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.15556","last_updated":"2022-03-29T13:38:03Z","snapshot_observed_at":"2026-07-06T12:54:11.616335Z","submitted_at":"2022-03-29T13:38:03Z","title":"Training Compute-Optimal Large Language Models","version":1},"cited_work":{"arxiv_id":"2203.15556","doi":"10.1098/rsta.2024.0522","metadata_source":"pith","pith_arxiv_id":"2203.15556","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Training Compute-Optimal Large Language Models","venue":"cs.CL","work_id":"b2faf28d-86b7-429c-bc42-469458efc246","year":2022},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/2203.15556","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:314c205a92897aea881be94017e2c3d04f2d5fb75fbb2d88a8cad932e156b402","observation_id":"9b28b091-ad3a-4904-a22d-ad9f1815654f","resolution":{"observed_at":"2026-06-29T23:44:03.356896Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/0893-6080(91)90009-t","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T07:25:28.594565Z","title":"Approximation capabilities of multilayer feedforward networks","venue":null,"work_id":"77c2745d-6710-49f3-b1b0-6a2a4402b327","year":1991},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:df133506fc2a266e28a290fd0ccddcb79a38f95d157fe1830270dd135ad8818c","observation_id":"e3e46e52-6736-4dd7-8fcc-e7d3c4b11412","resolution":{"observed_at":"2026-06-29T23:04:00.777385Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-05-22T19:53:03.296906+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-22T19:53:03.296906+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-07-06T08:52:12.656082Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":"2001.08361","doi":"10.1145/3616855.3635845","metadata_source":"pith","pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Scaling Laws for Neural Language Models","venue":"cs.LG","work_id":"b7dd8749-9c45-4977-ab9b-64478dce1ae8","year":2020},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:2895acefc36e88aff77941d76f5739ea4e39f4c1bf7399ac1a5fcf12abcf22df","observation_id":"b21272ef-64df-4c33-a38b-aba0c7ebce38","resolution":{"observed_at":"2026-06-29T23:44:03.359341Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/s0893-6080(05)80131-5","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:14:10.473737Z","title":"doi: https://doi.org/10.1016/S0893-6080(05)80131-5","venue":null,"work_id":"6eff5fc9-a882-44f3-8bee-0ff27f7eed4e","year":1993},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:74ddd77a0040ae55dc34a23e407f7c33c4e8adb9ad047218f6b1fa41c257fa13","observation_id":"76144c07-6680-4004-9394-3ee5b506337c","resolution":{"observed_at":"2026-06-29T23:04:00.781047Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2410.08184","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T03:49:29.493719Z","title":"Scaling laws for diffusion transformers","venue":null,"work_id":"c63ab8f7-e19b-480c-9b0f-2ac18549ef65","year":2024},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:06a3cfb8c9819570ddf74f63c03a6b6a503b302e5be81ea2465553c1cc8c353a","observation_id":"6d9c276a-fec6-4380-9b93-ddce28c31189","resolution":{"observed_at":"2026-06-29T23:44:03.350471Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.06162","last_updated":"2018-12-14T20:49:09Z","snapshot_observed_at":"2026-07-06T07:21:19.842962Z","submitted_at":"2018-12-14T20:49:09Z","title":"An Empirical Model of Large-Batch Training","version":1},"cited_work":{"arxiv_id":"1812.06162","doi":"10.48550/arxiv.1812.06162","metadata_source":"pith","pith_arxiv_id":"1812.06162","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"An Empirical Model of Large-Batch Training","venue":"cs.LG","work_id":"f3989b96-1ee9-403f-a0e2-0342ac16bcb7","year":2018},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/1812.06162","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:f6155934161a61c219e231044827bb5ba5cca0d70954a2c52042b6ee3facda07","observation_id":"1685a797-8d5c-4414-b686-15cddf1620ce","resolution":{"observed_at":"2026-06-29T23:44:03.353422Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-29T22:56:43.393302Z","title":"Can a suit of armor conduct electricity? a new dataset for open book question answering","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:2af3178fc09887f02a01fba153e9192c0ea7a22d1a855e7374a672bee56c0c34","observation_id":"5f900871-077e-479c-aeb0-8242b59f06d7","resolution":{"observed_at":"2026-06-29T22:56:43.393302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.16264","last_updated":"2025-06-28T00:00:06Z","snapshot_observed_at":"2026-07-06T15:33:27.761070Z","submitted_at":"2023-05-25T17:18:55Z","title":"Scaling Data-Constrained Language Models","version":5},"cited_work":{"arxiv_id":"2305.16264","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.16264","snapshot_observed_at":"2026-07-04T21:10:09.117208Z","title":"Scaling Data-Constrained Language Models","venue":"cs.CL","work_id":"e79ca454-d3a2-4c0f-8010-9d81c75bf2d8","year":2023},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/2305.16264","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:608571882162bb775647894bccad45526d817f032b97966fe63c4eaa53cdf622","observation_id":"76eef3de-6115-4309-bbfe-86524d69924e","resolution":{"observed_at":"2026-06-29T23:44:03.373589Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.02292","last_updated":"2019-12-04T22:47:31Z","snapshot_observed_at":"2026-07-06T08:42:09.483153Z","submitted_at":"2019-12-04T22:47:31Z","title":"Deep Double Descent: Where Bigger Models and More Data Hurt","version":1},"cited_work":{"arxiv_id":"1912.02292","doi":"10.48550/arxiv.1912.02292","metadata_source":"arxiv_reference","pith_arxiv_id":"1912.02292","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"International Conference on Learning Representations (ICLR) , year =","venue":null,"work_id":"145aa003-0690-44ba-8b20-fc6ba4a3e4a0","year":1912},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/1912.02292","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:9bb7935ea90e9516baa54c607f0d69669b8ca37116305d8e1eaa286e58862439","observation_id":"5840e183-b397-4edb-8f43-19014c9fa0ef","resolution":{"observed_at":"2026-06-29T23:44:03.362215Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.00849","last_updated":"2023-02-13T15:33:31Z","snapshot_observed_at":"2026-07-06T13:58:56.329236Z","submitted_at":"2022-09-29T19:08:51Z","title":"Scaling Laws for a Multi-Agent Reinforcement Learning Model","version":2},"cited_work":{"arxiv_id":"2210.00849","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2210.00849","snapshot_observed_at":"2026-07-02T12:36:56.118010Z","title":"Mergebench/llama-3.2-3b-instruct_coding","venue":null,"work_id":"827893fa-7689-4b97-9d76-74334caa25f8","year":null},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/2210.00849","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:56201b4d364a3d6b78a5055123e649dc5f9629ee263fab68cb5580d6a65d73bf","observation_id":"4a5ac36a-c1b4-4282-a76d-88a346a594a7","resolution":{"observed_at":"2026-06-30T00:14:04.514809Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.02177","last_updated":"2022-01-06T18:43:37Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-01-06T18:43:37Z","title":"Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets","version":1},"cited_work":{"arxiv_id":"2201.02177","doi":"10.1109/tit.2005.851722","metadata_source":"pith","pith_arxiv_id":"2201.02177","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets","venue":"cs.LG","work_id":"a3c30ead-1625-4c18-a9c1-e4928dcd0da6","year":2022},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/2201.02177","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:ed1f96c880c3da35c4fed1c9e6fc8a3c8c0afa2928a8d679d423796fafc55864","observation_id":"24dc3aec-bcc9-45f0-a29f-7f212d0efa55","resolution":{"observed_at":"2026-06-29T23:44:02.758784Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.12673","last_updated":"2019-12-20T18:20:34Z","snapshot_observed_at":"2026-07-06T08:25:10.762497Z","submitted_at":"2019-09-27T13:27:53Z","title":"A Constructive Prediction of the Generalization Error Across Scales","version":2},"cited_work":{"arxiv_id":"1909.12673","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.12673","snapshot_observed_at":"2026-07-02T16:07:08.680944Z","title":"arXiv preprint arXiv:1909.12673 , year=","venue":null,"work_id":"2e5959fb-8538-47c0-9bd0-989b1bc5ac44","year":2019},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/1909.12673","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:b00d027071e9ecdbc731ba590444993878600c487fca752b1b348b4f31f78c4a","observation_id":"2a6d89ca-88a1-4caa-aa8a-1d9ff501c6db","resolution":{"observed_at":"2026-06-30T00:14:04.517837Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16690","last_updated":"2024-06-24T14:51:31Z","snapshot_observed_at":"2026-08-02T22:50:01.292090Z","submitted_at":"2024-06-24T14:51:31Z","title":"Scaling Laws for Linear Complexity Language Models","version":1},"cited_work":{"arxiv_id":"2406.16690","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.16690","snapshot_observed_at":"2026-06-29T23:44:02.760568Z","title":"Scaling laws for linear complexity language models.arXiv preprint arXiv:2406.16690,","venue":null,"work_id":"22390b5f-3f6a-4a58-aa09-e94f76ef336c","year":null},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/2406.16690","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:ef1eaf8df6463d7b56d869a50081e3d10716f3f39ff6e0aa3d795ec1b7f1996f","observation_id":"20af7bd4-ff35-4eb5-8d5c-9b8d2dfd48a6","resolution":{"observed_at":"2026-06-29T23:44:02.762246Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.00489","last_updated":"2018-02-24T00:16:12Z","snapshot_observed_at":"2026-07-06T06:07:16.807271Z","submitted_at":"2017-11-01T18:04:31Z","title":"Don't Decay the Learning Rate, Increase the Batch Size","version":2},"cited_work":{"arxiv_id":"1711.00489","doi":null,"metadata_source":"pith","pith_arxiv_id":"1711.00489","snapshot_observed_at":"2026-07-03T17:18:43.523542Z","title":"Don't Decay the Learning Rate, Increase the Batch Size","venue":"cs.LG","work_id":"04ddda12-c77f-444d-88b2-5f4786276d69","year":2017},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/1711.00489","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:f19cdf70b85b6fb88f400361e6058dcfc4214e558d922f10b89e71c895dddced","observation_id":"0f4df87e-8c42-4df3-b728-3e9396d021c4","resolution":{"observed_at":"2026-06-29T23:44:03.339083Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1406.3896","last_updated":"2014-06-16T03:43:20Z","snapshot_observed_at":"2026-07-06T03:46:22.871924Z","submitted_at":"2014-06-16T03:43:20Z","title":"Freeze-Thaw Bayesian Optimization","version":1},"cited_work":{"arxiv_id":"1406.3896","doi":null,"metadata_source":"pith","pith_arxiv_id":"1406.3896","snapshot_observed_at":"2026-06-30T19:15:00.585260Z","title":"Freeze-Thaw Bayesian Optimization","venue":"stat.ML","work_id":"65bebb6e-0009-4683-8818-b5688ac87cb5","year":2014},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/1406.3896","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:8d400b8eccf7638a8a009f7335071cd5b20ae1b2460ff6b5f2b2c3f137ab135d","observation_id":"ea870fa7-ecef-4878-9cb3-c56f69aa402e","resolution":{"observed_at":"2026-06-29T23:44:03.341940Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.03466","last_updated":"2022-03-28T08:12:14Z","snapshot_observed_at":"2026-08-03T21:15:21.591567Z","submitted_at":"2022-03-07T15:37:35Z","title":"Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer","version":2},"cited_work":{"arxiv_id":"2203.03466","doi":"10.48550/arxiv.2203.03466","metadata_source":"arxiv_reference","pith_arxiv_id":"2203.03466","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Tensor programs v: Tuning large neural networks via zero-shot hyperparameter transfer","venue":null,"work_id":"22ad4cec-5465-4cdb-a2aa-ace82b84b5e9","year":2022},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/2203.03466","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:c3b9bececb64dd2074bd32e7482cff7fdbc3239815647e7efe122c2a80e70500","observation_id":"8c8492ca-96bf-428a-b8bd-ec0762b8846e","resolution":{"observed_at":"2026-06-29T23:44:03.344613Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.03888","last_updated":"2017-09-13T23:25:07Z","snapshot_observed_at":"2026-07-06T05:55:05.946111Z","submitted_at":"2017-08-13T11:01:57Z","title":"Large Batch Training of Convolutional Networks","version":3},"cited_work":{"arxiv_id":"1708.03888","doi":"10.48550/arxiv.1708.03888","metadata_source":"pith","pith_arxiv_id":"1708.03888","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Large Batch Training of Convolutional Networks","venue":"cs.CV","work_id":"92799584-c1e5-4828-bbfe-0771b7fe8706","year":2017},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/1708.03888","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:ebfd6980855af495933d9fa9519acae5982e855648a01c3b86a505848ccb1b45","observation_id":"cc81bd36-f1a5-4a6c-b1d1-240da1e3096b","resolution":{"observed_at":"2026-06-29T23:44:03.347192Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.04560","last_updated":"2022-06-20T09:13:51Z","snapshot_observed_at":"2026-08-04T23:08:49.433171Z","submitted_at":"2021-06-08T17:47:39Z","title":"Scaling Vision Transformers","version":2},"cited_work":{"arxiv_id":"2106.04560","doi":"10.48550/arxiv.2106.04560","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.04560","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Scaling vision transform- ers, 6 2021","venue":null,"work_id":"bed1e12d-8db3-4995-a609-386387c6cb86","year":2022},"citing_paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-29T22:56:43.393302Z"},"links":{"cited_paper":"/paper/2106.04560","citing_paper":"/paper/2605.26248"},"observation_digest":"sha256:ec41d598233d6593dc3cbb08ac2a34822070c302e007f1f859299e3972a9ccd5","observation_id":"96e3459a-788e-4879-b74a-317410fee9eb","resolution":{"observed_at":"2026-06-29T23:44:03.336396Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.26248","last_updated":"2026-05-25T18:15:27Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T23:36:06.135765Z","submitted_at":"2026-05-25T18:15:27Z","title":"Unified Neural Scaling Laws"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":14,"parse_uncertain":0,"unresolved":4,"verified_exact":12,"verified_fuzzy":0},"total_outbound_references":30},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2605.26248."}