{"as_of":"2026-08-23T15:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d0c2017e3d76f3cd2a8b8d20966095956985ab5f83d27706647f4ede91b7160d","coverage":[{"denominator":21,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":21,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T01:01:52.792606Z","state":"measured"},{"denominator":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+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/2505.02266/citation-record","integrity":"/paper/2505.02266/integrity","json":"/paper/2505.02266/citation-record.json","paper":"/paper/2505.02266"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T01:01:53.086590Z","title":"Quantifying unique information","venue":null,"work_id":"56d4f717-8f41-40d0-9e28-39bf0fe5a7e7","year":2013},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.712574Z"},"links":{"citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:d6e20ef9e15fe929448f9b1d2281c59b53044ac54d4d1a5bf7f139376affa933","observation_id":"7268a1ca-4253-40fc-bf29-18caabc840d6","resolution":{"observed_at":"2026-08-16T01:01:53.090177Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-16T01:01:53.074778Z","title":"A large an- notated corpus for learning natural language inference","venue":null,"work_id":"8cedb65a-da30-4fbc-b36c-343fb9f3d127","year":2015},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.717018Z"},"links":{"citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:87e670e84f585ca0b9ada8891d760392832286187c1c0caaf0dd9e38c8dc762b","observation_id":"6b468769-f974-4d6e-ab62-d843fd89866e","resolution":{"observed_at":"2026-08-16T01:01:53.078977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.12816","last_updated":"2024-09-02T01:48:34Z","snapshot_observed_at":"2026-08-16T15:46:20.117062Z","submitted_at":"2023-03-22T07:34:33Z","title":"From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding","version":4},"cited_work":{"arxiv_id":"2303.12816","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.12816","snapshot_observed_at":"2026-08-16T01:01:52.929394Z","title":"From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding","venue":"cs.LG","work_id":"a1a4556a-d0e5-4883-b853-7d3a1ad90b32","year":2023},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.721592Z"},"links":{"cited_paper":"/paper/2303.12816","citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:cbcb97443393cfd264b69b18e33c505a8ff3569228d9d1a75b0b0767e95bea99","observation_id":"f927bbec-2066-416a-891e-e5843119dcd4","resolution":{"observed_at":"2026-08-16T01:01:52.934507Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-16T01:01:53.061545Z","title":"Bert: Pre-training of deep bidirectional transformers for language understan ding","venue":null,"work_id":"9a853ddb-1d64-465b-b367-8fa23ead30c2","year":2019},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.726094Z"},"links":{"citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:dba84fd61997b1567b0841faaea3229d32970f708602feda38418870df29b45b","observation_id":"eff6d408-f644-4f66-914c-f1cf866fe0cb","resolution":{"observed_at":"2026-08-16T01:01:53.066256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.11942","last_updated":"2020-02-09T03:00:18Z","snapshot_observed_at":"2026-08-18T01:27:11.590348Z","submitted_at":"2019-09-26T07:06:13Z","title":"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.11942","snapshot_observed_at":"2026-08-16T01:01:52.730325Z","title":"Albert: A lite bert for self-supervised learning o f language representations","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.730325Z"},"links":{"cited_paper":"/paper/1909.11942","citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:c40cac53e788c481b88dcc33ddd1ebbae2869d19548c36452dace933706340b2","observation_id":"3029fb09-463b-457a-bfa9-fb02e1c07f6c","resolution":{"observed_at":"2026-08-16T01:01:52.730325Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.07467","last_updated":"2019-10-16T16:44:22Z","snapshot_observed_at":"2026-08-08T08:22:25.110228Z","submitted_at":"2019-10-16T16:44:22Z","title":"Root Mean Square Layer Normalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.07467","snapshot_observed_at":"2026-08-16T01:01:52.734574Z","title":"Root mean square layer normalization","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.734574Z"},"links":{"cited_paper":"/paper/1910.07467","citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:92cf30f58457ba4759d9e50a79302101f52bf242eeb059f5325d75eae6e662a2","observation_id":"5bce0399-1d85-444c-8fd7-3d5e998e7d3b","resolution":{"observed_at":"2026-08-16T01:01:52.734574Z","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":"2026-08-16T01:01:53.050044Z","title":"Decoupled weight deca y regularization","venue":null,"work_id":"16dbb697-6a54-4b35-a07f-b1e93c27b994","year":2019},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.739362Z"},"links":{"citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:37b98233a998ee6ca1631aaa31f315f0394f1970dfb171e011f028a27ba0f6bf","observation_id":"a4bdd1ff-6e5c-4b2c-808a-710f90715f7a","resolution":{"observed_at":"2026-08-16T01:01:53.054043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.00020","last_updated":"2021-02-26T19:04:58Z","snapshot_observed_at":"2026-07-06T10:45:03.059688Z","submitted_at":"2021-02-26T19:04:58Z","title":"Learning Transferable Visual Models From Natural Language Supervision","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.00020","snapshot_observed_at":"2026-08-16T01:01:52.743107Z","title":"Learning transferable visual models from natural la nguage supervision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.743107Z"},"links":{"cited_paper":"/paper/2103.00020","citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:03b8e25b0593e2a9ba5e753da202e96b1945f69ad3b1fbfd96f7731acc7f0e63","observation_id":"8f503cdf-3b12-47c3-97a8-bf4648ce8a23","resolution":{"observed_at":"2026-08-16T01:01:52.743107Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.02054","last_updated":"2020-05-13T06:45:15Z","snapshot_observed_at":"2026-07-06T08:27:00.558613Z","submitted_at":"2019-10-04T17:29:39Z","title":"ZeRO: Memory Optimizations Toward Training Trillion Parameter Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.02054","snapshot_observed_at":"2026-08-16T01:01:52.747306Z","title":"Zero: Memory opti- mization towards training trillion parameter models","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.747306Z"},"links":{"cited_paper":"/paper/1910.02054","citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:4b272b208cd5086f5c36b9d3d086b2909f1959063e146362ba4247acb3f2f179","observation_id":"8181d766-724f-44e4-8c91-916fe72b84bb","resolution":{"observed_at":"2026-08-16T01:01:52.747306Z","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":"2026-08-16T01:01:53.037874Z","title":"Neur al machine translation of rare words with subword units","venue":null,"work_id":"a394e03a-fd21-4975-9776-64a0649b11ea","year":2016},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.751113Z"},"links":{"citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:dc710a565ca5017a5540025202bdeaaa296135d1ff34496326b79fb1a0c797d1","observation_id":"9519b7a1-914e-41b2-aec8-e7f355a963b0","resolution":{"observed_at":"2026-08-16T01:01:53.041960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-16T01:01:53.024937Z","title":"Glu variants improve transformer","venue":null,"work_id":"3544baed-c99d-4238-9e25-2b76704cd327","year":2020},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.754626Z"},"links":{"citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:c2253d99168674b52ca2ecc5f6c8158e283a55c2ec716a586deb0ac49750108c","observation_id":"a3f71735-eac3-4f28-b396-fda14e2db219","resolution":{"observed_at":"2026-08-16T01:01:53.028979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-16T01:01:53.012709Z","title":"Q-bert: Hessian-based ul tra low precision quantization of bert","venue":null,"work_id":"67e7b953-8faf-4f43-8e6d-667cde5c18d9","year":2020},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.758537Z"},"links":{"citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:96fc18cd709e5cdc653bdec2dde540c5cb046c464c7ebcf4ab961618c7caaa85","observation_id":"9c7d9da3-2e58-4797-a5e0-4305e2ac90ed","resolution":{"observed_at":"2026-08-16T01:01:53.017014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.01079","last_updated":"2017-08-03T09:49:42Z","snapshot_observed_at":"2026-08-14T20:43:27.872892Z","submitted_at":"2017-08-03T09:49:42Z","title":"The Gram-Schmidt Walk: A Cure for the Banaszczyk Blues","version":1},"cited_work":{"arxiv_id":"1708.01079","doi":null,"metadata_source":"pith","pith_arxiv_id":"1708.01079","snapshot_observed_at":"2026-08-16T01:01:52.858892Z","title":"The Gram-Schmidt Walk: A Cure for the Banaszczyk Blues","venue":"cs.DS","work_id":"15185e91-5fb1-41f3-8648-0ffd85bc246c","year":2017},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.762021Z"},"links":{"cited_paper":"/paper/1708.01079","citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:01f7df06481b2f3552072ace3743d12e56392ddb06abb0d7328344e15b222623","observation_id":"bf7b2bdb-be15-4a81-bfd0-f4ff4b7c9e40","resolution":{"observed_at":"2026-08-16T01:01:52.865197Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.09864","last_updated":"2023-11-08T13:36:32Z","snapshot_observed_at":"2026-08-17T06:56:20.644738Z","submitted_at":"2021-04-20T09:54:06Z","title":"RoFormer: Enhanced Transformer with Rotary Position Embedding","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.09864","snapshot_observed_at":"2026-08-16T01:01:52.765804Z","title":"Roformer: Enhanced trans- former with rotary position embedding","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.765804Z"},"links":{"cited_paper":"/paper/2104.09864","citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:04fcb4f3d9fdd79d51a80241a922f46e1ce5d2f9bdb7117cd7cc7200f1902e75","observation_id":"85ed60b4-1055-4f70-8cb8-5f08c554a975","resolution":{"observed_at":"2026-08-16T01:01:52.765804Z","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":"2026-08-16T01:01:52.998798Z","title":"Hash embeddings for efficient word representations","venue":null,"work_id":"c406ee32-d61b-4c87-8366-63fa1b013951","year":2017},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.769155Z"},"links":{"citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:beff75c90723d8be8a084ec932b5f459a231d02acb7ba5f6026b127abd47af31","observation_id":"3402f499-17f1-4bac-9b74-3e7a488178ad","resolution":{"observed_at":"2026-08-16T01:01:53.003744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-16T01:01:52.984512Z","title":"Represe ntation learning with contrastive predictive coding","venue":null,"work_id":"2525cc03-c071-417b-97e1-2d7e861e9a9a","year":2018},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.772557Z"},"links":{"citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:64105d907876820dd9094101ed0a5a695cac2a069698456da95687f3baa7500c","observation_id":"53b2c9fa-9ebf-434b-a002-6125e3b27baf","resolution":{"observed_at":"2026-08-16T01:01:52.989063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-16T01:01:52.971374Z","title":"Structured embedding compression, 20 20","venue":null,"work_id":"46aa0b62-aa79-46cb-b5df-e903be02baf3","year":null},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.776509Z"},"links":{"citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:24da9d1f0e2aee7acdbc7d79ffa0230001bc52d8c7bc599e7fcc7126914a3eab","observation_id":"d6902805-443d-4e1d-b822-6fd4001366de","resolution":{"observed_at":"2026-08-16T01:01:52.975963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-16T01:01:52.955544Z","title":"A br oad-coverage challenge cor- pus for sentence understanding through inference","venue":null,"work_id":"2b6c560d-3847-465a-b20d-cd1a8f606f12","year":2017},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.780103Z"},"links":{"citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:8b777c468fb5a497d4f4a51d36391b4aada16ab04ca73140177d7e3b629de2b5","observation_id":"7032d44b-8eef-4996-88b7-f7d9691f1c74","resolution":{"observed_at":"2026-08-16T01:01:52.960466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1004.2515","last_updated":"2010-04-14T22:12:52Z","snapshot_observed_at":"2026-08-16T20:52:40.400093Z","submitted_at":"2010-04-14T22:12:52Z","title":"Nonnegative Decomposition of Multivariate Information","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1004.2515","snapshot_observed_at":"2026-08-16T01:01:52.783725Z","title":"Nonnegative decompo sition of multivariate information","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.783725Z"},"links":{"cited_paper":"/paper/1004.2515","citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:c20ecfec4c87e1e87ac5230c9ed973a86534a22047643d1d2425e4353e0cac81","observation_id":"3254060e-3a39-454a-98a8-4d452d197d1e","resolution":{"observed_at":"2026-08-16T01:01:52.783725Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.00526","last_updated":"2024-10-03T23:28:27Z","snapshot_observed_at":"2026-08-16T15:19:24.090296Z","submitted_at":"2023-07-02T09:33:09Z","title":"TensorGPT: Efficient Compression of Large Language Models based on Tensor-Train Decomposition","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.00526","snapshot_observed_at":"2026-08-16T01:01:52.788232Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.788232Z"},"links":{"cited_paper":"/paper/2307.00526","citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:5389154f254d5eff08b7563a18ee7d5c00297dd382d444c22f58a830c5c9b016","observation_id":"3dcfb862-b619-4c9d-8ce5-4afebc9c34d2","resolution":{"observed_at":"2026-08-16T01:01:52.788232Z","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":"2026-08-16T01:01:52.943029Z","title":"Adaptively-masked twins-based layer f or efficient embeddings, 2021","venue":null,"work_id":"b3f763bb-bcda-4145-80ea-a1365bb41eb3","year":2021},"citing_paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T01:01:52.792606Z"},"links":{"citing_paper":"/paper/2505.02266"},"observation_digest":"sha256:7b9b03329dd70ce8d9bc157c4ffb0722994e955d134fb7522186f241b295d2c9","observation_id":"e625b904-9e1a-4b55-9b8c-b97057112d23","resolution":{"observed_at":"2026-08-16T01:01:52.947546Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.02266","last_updated":"2025-05-04T21:47:18Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T00:54:12.864154Z","submitted_at":"2025-05-04T21:47:18Z","title":"Parameter-Efficient Transformer Embeddings"},"reference_resolution":{"displayed":21,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":7,"verified_exact":1,"verified_fuzzy":12},"total_outbound_references":21},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2505.02266."}