{"as_of":"2026-08-10T12:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3d50822b02dbc3b970547a74fa0a4c8e1e23b4d51eee5219d976ffb86ebdbd86","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:19:32.086306Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-24T12:34:28.397146Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1910.05895","last_updated":"2019-12-30T03:53:04Z","snapshot_observed_at":"2026-08-07T19:31:58.707080Z","submitted_at":"2019-10-14T02:23:43Z","title":"Transformers without Tears: Improving the Normalization of Self-Attention","version":2},"cited_work":{"arxiv_id":"1910.05895","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1910.05895","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Nguyen and Julian Salazar","venue":null,"work_id":"1d2ca339-f1d0-44fb-9eb7-6fe898a9891f","year":2019},"citing_paper":{"arxiv_id":"2201.11990","last_updated":"2022-02-04T18:02:23Z","snapshot_observed_at":"2026-08-09T17:07:26.804289Z","submitted_at":"2022-01-28T08:59:57Z","title":"Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-24T12:10:49.690618Z"},"links":{"cited_paper":"/paper/1910.05895","citing_paper":"/paper/2201.11990"},"observation_digest":"sha256:8d1403afc66439f97c5ea435f072d2939aacbe755acb8f53ae0fc07ecbe6c289","observation_id":"b62bf687-faa3-4741-ba03-c1d63af740f4","resolution":{"observed_at":"2026-05-24T12:14:26.535552Z","resolver_source":"arxiv_id","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":"1910.05895","last_updated":"2019-12-30T03:53:04Z","snapshot_observed_at":"2026-08-07T19:31:58.707080Z","submitted_at":"2019-10-14T02:23:43Z","title":"Transformers without Tears: Improving the Normalization of Self-Attention","version":2},"cited_work":{"arxiv_id":"1910.05895","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1910.05895","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Nguyen and Julian Salazar","venue":null,"work_id":"1d2ca339-f1d0-44fb-9eb7-6fe898a9891f","year":2019},"citing_paper":{"arxiv_id":"2204.06745","last_updated":"2022-04-14T04:00:27Z","snapshot_observed_at":"2026-07-06T13:00:12.148951Z","submitted_at":"2022-04-14T04:00:27Z","title":"GPT-NeoX-20B: An Open-Source Autoregressive Language Model","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-05-24T12:33:37.701655Z"},"links":{"cited_paper":"/paper/1910.05895","citing_paper":"/paper/2204.06745"},"observation_digest":"sha256:ffe124c44aff9d8c71a3f9ab91188cd8e7c2bcaa2cd1b448a1c616bd7c736edd","observation_id":"22ff5b8e-f7cb-4e49-9e28-ca5b745135f7","resolution":{"observed_at":"2026-05-24T12:34:28.399984Z","resolver_source":"arxiv_id","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":"1910.05895","last_updated":"2019-12-30T03:53:04Z","snapshot_observed_at":"2026-08-07T19:31:58.707080Z","submitted_at":"2019-10-14T02:23:43Z","title":"Transformers without Tears: Improving the Normalization of Self-Attention","version":2},"cited_work":{"arxiv_id":"1910.05895","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1910.05895","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Nguyen and Julian Salazar","venue":null,"work_id":"1d2ca339-f1d0-44fb-9eb7-6fe898a9891f","year":2019},"citing_paper":{"arxiv_id":"2307.06435","last_updated":"2024-10-17T01:10:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-12T20:01:52Z","title":"A Comprehensive Overview of Large Language Models","version":10},"reference_index":298,"source":"pdf_text","source_observed_at":"2026-05-19T20:28:38.900026Z"},"links":{"cited_paper":"/paper/1910.05895","citing_paper":"/paper/2307.06435"},"observation_digest":"sha256:48290eccb2cc17ed87c037dc1c6fa8545561bfdfbce1996ad5a8b217cba2e955","observation_id":"e0bc94ef-b68a-4d93-8816-7bd065a5c783","resolution":{"observed_at":"2026-05-19T20:28:39.297137Z","resolver_source":"arxiv_id","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":"1910.05895","last_updated":"2019-12-30T03:53:04Z","snapshot_observed_at":"2026-08-07T19:31:58.707080Z","submitted_at":"2019-10-14T02:23:43Z","title":"Transformers without Tears: Improving the Normalization of Self-Attention","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.05895","snapshot_observed_at":"2026-08-07T10:19:32.086306Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.06398","last_updated":"2025-06-05T23:02:18Z","snapshot_observed_at":"2026-08-09T17:30:00.913672Z","submitted_at":"2025-06-05T23:02:18Z","title":"Theoretical Analysis of Positional Encodings in Transformer Models: Impact on Expressiveness and Generalization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:32.086306Z"},"links":{"cited_paper":"/paper/1910.05895","citing_paper":"/paper/2506.06398"},"observation_digest":"sha256:ceb180ca5c671b6dd0f35c5fc1575c774c4284d1cf568864869e23fee72ace3d","observation_id":"c2e63405-5085-4b8d-b3c8-131abe7a4d76","resolution":{"observed_at":"2026-08-07T10:19:32.086306Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05895","last_updated":"2019-12-30T03:53:04Z","snapshot_observed_at":"2026-08-07T19:31:58.707080Z","submitted_at":"2019-10-14T02:23:43Z","title":"Transformers without Tears: Improving the Normalization of Self-Attention","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.05895","snapshot_observed_at":"2026-08-07T00:50:30.609473Z","title":null,"venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2506.12543","last_updated":"2025-06-14T15:37:31Z","snapshot_observed_at":"2026-08-08T23:08:34.335687Z","submitted_at":"2025-06-14T15:37:31Z","title":"Is your batch size the problem? Revisiting the Adam-SGD gap in language modeling","version":1},"reference_index":1986,"source":"pdf_text","source_observed_at":"2026-08-07T00:50:30.609473Z"},"links":{"cited_paper":"/paper/1910.05895","citing_paper":"/paper/2506.12543"},"observation_digest":"sha256:961deaac2e7da1969981429719fd5039d7cc13b0e8e3e0fd8cc475f9ff91e7e6","observation_id":"f176b608-2526-4295-95c2-1538cb7575ee","resolution":{"observed_at":"2026-08-07T00:50:30.609473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05895","last_updated":"2019-12-30T03:53:04Z","snapshot_observed_at":"2026-08-07T19:31:58.707080Z","submitted_at":"2019-10-14T02:23:43Z","title":"Transformers without Tears: Improving the Normalization of Self-Attention","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.05895","snapshot_observed_at":"2026-08-06T20:07:00.041601Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.03789","last_updated":"2025-08-12T15:38:44Z","snapshot_observed_at":"2026-08-06T20:00:18.817898Z","submitted_at":"2025-07-04T19:50:01Z","title":"Efficient and Effective Query Context-Aware Learning-to-Rank Model for Sequential Recommendation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T20:07:00.041601Z"},"links":{"cited_paper":"/paper/1910.05895","citing_paper":"/paper/2507.03789"},"observation_digest":"sha256:c5b2696a0a231cc72259845c7b3039f7990e26a2dba7119ed040cc0a09bb9b16","observation_id":"c1b805f2-67bb-42a1-9bab-a59829c5c5f5","resolution":{"observed_at":"2026-08-06T20:07:00.041601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05895","last_updated":"2019-12-30T03:53:04Z","snapshot_observed_at":"2026-08-07T19:31:58.707080Z","submitted_at":"2019-10-14T02:23:43Z","title":"Transformers without Tears: Improving the Normalization of Self-Attention","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.05895","snapshot_observed_at":"2026-08-05T16:17:42.074129Z","title":"Transformers without tears: Improving the normalization of self-attention","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2508.18756","last_updated":"2025-08-26T07:33:11Z","snapshot_observed_at":"2026-08-09T07:25:17.981291Z","submitted_at":"2025-08-26T07:33:11Z","title":"UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T16:17:42.074129Z"},"links":{"cited_paper":"/paper/1910.05895","citing_paper":"/paper/2508.18756"},"observation_digest":"sha256:4f04e4a448444b3a69e6ec50c191c89260996997e9ac026733e7a98d35246e59","observation_id":"0b698c83-5376-4f15-832e-f29ac974cc19","resolution":{"observed_at":"2026-08-05T16:17:42.074129Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05895","last_updated":"2019-12-30T03:53:04Z","snapshot_observed_at":"2026-08-07T19:31:58.707080Z","submitted_at":"2019-10-14T02:23:43Z","title":"Transformers without Tears: Improving the Normalization of Self-Attention","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.05895","snapshot_observed_at":"2026-08-03T19:20:50.271527Z","title":null,"venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2512.01208","last_updated":"2026-07-16T17:34:29Z","snapshot_observed_at":"2026-08-08T19:54:04.614437Z","submitted_at":"2025-12-01T02:46:15Z","title":"Language as a Wave Phenomenon: Semantic Phase Locking and Interference in Neural Networks","version":5},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-03T19:20:50.271527Z"},"links":{"cited_paper":"/paper/1910.05895","citing_paper":"/paper/2512.01208"},"observation_digest":"sha256:c982e2796aabb34e9048af674c21351e41d31cf38a7ea512279e8f6a3a7244ba","observation_id":"d4414fba-f60a-4526-8073-2b743fece639","resolution":{"observed_at":"2026-08-03T19:20:50.271527Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05895","last_updated":"2019-12-30T03:53:04Z","snapshot_observed_at":"2026-08-07T19:31:58.707080Z","submitted_at":"2019-10-14T02:23:43Z","title":"Transformers without Tears: Improving the Normalization of Self-Attention","version":2},"cited_work":{"arxiv_id":"1910.05895","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1910.05895","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Nguyen and Julian Salazar","venue":null,"work_id":"1d2ca339-f1d0-44fb-9eb7-6fe898a9891f","year":2019},"citing_paper":{"arxiv_id":"2604.08181","last_updated":"2026-04-09T12:36:29Z","snapshot_observed_at":"2026-07-31T22:44:31.803583Z","submitted_at":"2026-04-09T12:36:29Z","title":"Long-Term Embeddings for Balanced Personalization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T18:39:41.045848Z"},"links":{"cited_paper":"/paper/1910.05895","citing_paper":"/paper/2604.08181"},"observation_digest":"sha256:256510ce5b9022c46d0920af885045ad6cd9fc28d9e6ea5ca56200a7782e44fe","observation_id":"793975f9-0e15-4db5-b78f-b8d48533e0e6","resolution":{"observed_at":"2026-05-11T00:10:52.380626Z","resolver_source":"arxiv_id","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"}}],"links":{"evidence":"/evidence","html":"/paper/1910.05895/citation-record","integrity":"/paper/1910.05895/integrity","json":"/paper/1910.05895/citation-record.json","paper":"/paper/1910.05895"},"outbound":[],"paper":{"arxiv_id":"1910.05895","last_updated":"2019-12-30T03:53:04Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T19:31:58.707080Z","submitted_at":"2019-10-14T02:23:43Z","title":"Transformers without Tears: Improving the Normalization of Self-Attention"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+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 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1910.05895."}