{"as_of":"2026-08-07T20:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:049cb53db2b04e7247ea4a403fade058cd043a999ce47b4b4852af273afc47d0","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T05:20:03.263656Z","state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2608.05104/citation-record","integrity":"/paper/2608.05104/integrity","json":"/paper/2608.05104/citation-record.json","paper":"/paper/2608.05104"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T05:20:08.389288Z","title":"and Manning, C.D., 2015","venue":null,"work_id":"ab951545-b081-452f-b56a-48e6baa6e1f7","year":2015},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T05:19:59.271562Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:4eb09350a78abb0d857d474c1fba2b4deb61f09b3a2294f4f23b388e6c12dae4","observation_id":"c993fd74-1ed4-4b89-a4de-1020a1bb0966","resolution":{"observed_at":"2026-08-06T05:20:08.391736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.15115","last_updated":"2021-06-29T06:31:58Z","snapshot_observed_at":"2026-07-31T04:08:35.530110Z","submitted_at":"2021-06-29T06:31:58Z","title":"Neural Machine Translation for Low-Resource Languages: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.15115","snapshot_observed_at":"2026-08-06T05:19:59.299051Z","title":"and Kaur, R., 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T05:19:59.299051Z"},"links":{"cited_paper":"/paper/2106.15115","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:c704f1395a76409005435d90380beac5abe1288c6e61d41e5d35da77eb1be986","observation_id":"2cf0e8f9-1eae-4bad-9fc0-378b9553ff9e","resolution":{"observed_at":"2026-08-06T05:19:59.299051Z","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":"2026-08-06T05:20:08.381588Z","title":"Language models: past, present, and future","venue":null,"work_id":"2e567099-d46a-4bb2-841a-c28a2c626e68","year":2022},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T05:19:59.365964Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:929729900f28f795547b6905a99e85ef27f5ce4f43cb8a76bfa6bd58cdb49d40","observation_id":"183503df-164a-40a0-8081-6dbecc5cffe7","resolution":{"observed_at":"2026-08-06T05:20:08.384194Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:08.373428Z","title":"An unsupervised parts-of-speech tagger for the bangla language","venue":null,"work_id":"f4a7e356-ab43-4fb2-a10b-6dd1ba63f6d9","year":2010},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T05:19:59.440494Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:4a93a591041e247ac951a3a5d67c9f57f27c6c61ed268de500ab7f1935e39bd8","observation_id":"b9536a17-a26c-490a-84ac-15a38551d9e8","resolution":{"observed_at":"2026-08-06T05:20:08.376382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:08.364731Z","title":"Research on Bangla language processing in Bangladesh: progress and challenges","venue":null,"work_id":"30db481e-cb5a-4c7b-ae9e-09566c3dd85c","year":2009},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T05:19:59.509703Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:9a661b1a328caeb161461e2877532946ce1c11179d4556f2328a3ebe4a80771f","observation_id":"ef6d95ea-fd02-4df6-855a-1fffe28d53cc","resolution":{"observed_at":"2026-08-06T05:20:08.367979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:08.356437Z","title":"and Khan, M., 2007","venue":null,"work_id":"ba406b39-e5d7-44cc-9506-834a8d263e83","year":2007},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T05:19:59.597129Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:b9b5722493edbcfcb08de5399e9e17eab2a56314c9b24cf3737607115ace8876","observation_id":"92e6ec17-f268-489d-8c87-dadaed08c1a7","resolution":{"observed_at":"2026-08-06T05:20:08.359354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:08.348466Z","title":"and Bandyopadhyay, S., 2010","venue":null,"work_id":"c3990f27-91ae-4308-8ea3-4a82a518ca7f","year":2010},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T05:19:59.678826Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:7ef25dc519ee227741df5c85a8af7e29f0550a7996350e83b2d710a23c44badd","observation_id":"172df3bf-a0c8-4d85-891c-9a891c815c46","resolution":{"observed_at":"2026-08-06T05:20:08.351128Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:08.341049Z","title":"and Izhar, M.N., 2013","venue":null,"work_id":"3e2a6a43-b6fd-4ac5-b548-acbd474014ff","year":2013},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T05:19:59.752674Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:d2e7a6e9fc2e272337c08d8bf6b2d2ace9591517fae557ca155fdb6dd325552e","observation_id":"135b71ad-462a-46a0-82ea-db3413165c13","resolution":{"observed_at":"2026-08-06T05:20:08.343918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:08.333494Z","title":"and Bandyopadhyay, S., 2010","venue":null,"work_id":"f1f365fb-4fec-4533-9939-b1ef4c1cb555","year":2010},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T05:19:59.824451Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:90ce6366868692963fd0e51db2db4d18a3f77f0368bb63baac132374af863465","observation_id":"6b399d1a-ffaf-44f2-9d64-79db729144e4","resolution":{"observed_at":"2026-08-06T05:20:08.336007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08613","last_updated":"2021-04-17T18:28:39Z","snapshot_observed_at":"2026-08-02T00:51:19.942224Z","submitted_at":"2021-04-17T18:28:39Z","title":"Emotion Classification in a Resource Constrained Language Using Transformer-based Approach","version":1},"cited_work":{"arxiv_id":"2104.08613","doi":null,"metadata_source":"pith","pith_arxiv_id":"2104.08613","snapshot_observed_at":"2026-08-06T05:20:04.482753Z","title":"Emotion Classification in a Resource Constrained Language Using Transformer-based Approach","venue":"cs.CL","work_id":"c9aedf02-4f3d-4426-a027-391990111290","year":2021},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T05:19:59.883792Z"},"links":{"cited_paper":"/paper/2104.08613","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:543f06a4d61eb329f0edf3c4d873445a28bb56edc7c27521ef06b87b0e9eca01","observation_id":"c772178e-a00e-40c1-a19d-4f9be7d8b910","resolution":{"observed_at":"2026-08-06T05:20:04.576380Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.14875","last_updated":"2022-04-09T19:01:54Z","snapshot_observed_at":"2026-07-06T11:14:17.661344Z","submitted_at":"2021-05-31T10:58:58Z","title":"Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods","version":3},"cited_work":{"arxiv_id":"2105.14875","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.14875","snapshot_observed_at":"2026-08-06T05:20:04.261742Z","title":"Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods","venue":"cs.CL","work_id":"77fd8413-b457-44af-99b2-1c54e8f1d234","year":2021},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T05:19:59.989060Z"},"links":{"cited_paper":"/paper/2105.14875","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:fb81afb566cdd4d7bd5eff4252cc3cc620e2704d387a3a4b6660ae4ec38c0403","observation_id":"673e239a-5d2b-4f61-a8ca-35ecaa4181a0","resolution":{"observed_at":"2026-08-06T05:20:04.364105Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.03844","last_updated":"2021-07-25T05:41:15Z","snapshot_observed_at":"2026-07-06T11:27:18.016778Z","submitted_at":"2021-07-08T13:49:46Z","title":"A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models","version":3},"cited_work":{"arxiv_id":"2107.03844","doi":null,"metadata_source":"pith","pith_arxiv_id":"2107.03844","snapshot_observed_at":"2026-08-06T05:20:04.010552Z","title":"A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models","venue":"cs.CL","work_id":"e9c1c6b4-9adc-41e9-a5f5-907f3ba4fd74","year":2021},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:00.073377Z"},"links":{"cited_paper":"/paper/2107.03844","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:090b20b6ec8e493286518a7f215c458c9bd2125387e50b4fd48c5295d1ee049e","observation_id":"5fafcc2c-f692-48e2-a3c2-28938e4e4630","resolution":{"observed_at":"2026-08-06T05:20:04.117803Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-07-31T22:31:37.910868Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-08-06T05:20:00.218709Z","title":"and Stoyanov, V ., 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:00.218709Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:be0b6bb48940ae3a8c43dacca748a742ff9c46c51f12c010b664e3d5bcac0e4e","observation_id":"486941d6-30c5-47eb-a97d-ff7adf4178b0","resolution":{"observed_at":"2026-08-06T05:20:00.218709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.01108","last_updated":"2020-03-01T02:57:50Z","snapshot_observed_at":"2026-08-07T19:07:36.327251Z","submitted_at":"2019-10-02T17:56:28Z","title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.01108","snapshot_observed_at":"2026-08-06T05:20:00.307802Z","title":"and Wolf, T., 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:00.307802Z"},"links":{"cited_paper":"/paper/1910.01108","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:6deb7b4889d2ec7ba8cd05c0b7961bcde2620952810d6e41716858333e9e6f0b","observation_id":"bfe56593-6abb-4144-a156-1efc2dee9872","resolution":{"observed_at":"2026-08-06T05:20:00.307802Z","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":"2026-08-06T05:20:08.326141Z","title":"and Chen, Y ., 2015, June","venue":null,"work_id":"d4cad0fd-121b-498e-9a48-434132461c22","year":2015},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:00.371297Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:facd24091f3f3880ccb116a8baf186c60b00bdc6baf58c529f7fe5ea275838cb","observation_id":"67c27f4f-7891-41c9-84a6-a9e7c6664b17","resolution":{"observed_at":"2026-08-06T05:20:08.328641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:08.318423Z","title":"Gradient and Magnitude Based Pruning for Sparse Deep Neural Networks","venue":null,"work_id":"8dcee963-4d63-4a2a-8b7d-07584d993e7d","year":2022},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:00.459217Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:dfe264546593a7ec54e5648332ef95610b73bbd0a5281d78d21df05094fcf982","observation_id":"b108e256-0650-42ba-972b-56441029cf5a","resolution":{"observed_at":"2026-08-06T05:20:08.321351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1608.08710","last_updated":"2017-03-10T17:57:56Z","snapshot_observed_at":"2026-07-06T05:08:42.861486Z","submitted_at":"2016-08-31T02:29:59Z","title":"Pruning Filters for Efficient ConvNets","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1608.08710","snapshot_observed_at":"2026-08-06T05:20:00.528787Z","title":"and Graf, H.P., 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:00.528787Z"},"links":{"cited_paper":"/paper/1608.08710","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:79843f7ca7bdc977c5bcad9d1875e420eb23850b93cdd2ff5f562ec01041ea20","observation_id":"81ee1455-f457-4c46-ae90-d510d50f4b3b","resolution":{"observed_at":"2026-08-06T05:20:00.528787Z","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":"2026-08-06T05:20:08.311096Z","title":"and Sun, J., 2017","venue":null,"work_id":"c16a060d-60e2-4292-a353-97fa372a1178","year":2017},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:00.625230Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:54e50c38ff038b72ab1bbeb26ab2e63482410364a4949b3ecd8d9a9fcd38afc9","observation_id":"0f82785a-1ddc-4d97-a914-7ec843e3c24f","resolution":{"observed_at":"2026-08-06T05:20:08.313536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1510.00149","last_updated":"2016-02-15T06:25:40Z","snapshot_observed_at":"2026-08-04T16:59:47.843960Z","submitted_at":"2015-10-01T09:03:44Z","title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1510.00149","snapshot_observed_at":"2026-08-06T05:20:00.688633Z","title":"and Dally, W.J., 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:00.688633Z"},"links":{"cited_paper":"/paper/1510.00149","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:63c38b357d52745a21c594168a6bd6dde22c0a10f5c574ebc5e59d23ad029282","observation_id":"fe8bf894-cfbf-47ce-9d44-2d430f8b1751","resolution":{"observed_at":"2026-08-06T05:20:00.688633Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.03635","last_updated":"2019-03-04T15:51:11Z","snapshot_observed_at":"2026-08-05T23:54:27.386622Z","submitted_at":"2018-03-09T18:51:28Z","title":"The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.03635","snapshot_observed_at":"2026-08-06T05:20:00.754595Z","title":"and Carbin, M., 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:00.754595Z"},"links":{"cited_paper":"/paper/1803.03635","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:621e6d0e470ff8fb4466c074d7f0b96631e14e3413b15bd308f46410dcd987b5","observation_id":"3a6e9fd7-0606-4777-9251-297972ab3fa1","resolution":{"observed_at":"2026-08-06T05:20:00.754595Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.07118","last_updated":"2021-04-12T08:16:59Z","snapshot_observed_at":"2026-08-05T04:33:26.758046Z","submitted_at":"2020-09-15T14:18:53Z","title":"It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.07118","snapshot_observed_at":"2026-08-06T05:20:00.831167Z","title":"and Sch ¨utze, H., 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:00.831167Z"},"links":{"cited_paper":"/paper/2009.07118","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:afb6deb554ae769212a0a1d1b876e08110f3652cf6e6fc325b9e349535fb8ccc","observation_id":"f5542167-1883-455a-b761-b5a289743967","resolution":{"observed_at":"2026-08-06T05:20:00.831167Z","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":"2026-08-06T05:20:08.303895Z","title":"and Jana, A., Sentiment Analysis For Bengali Using Transformer Based Models","venue":null,"work_id":"e08cf88b-8d21-4094-ab41-e625c8c2a96c","year":null},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:00.882249Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:6515826b0aef1b0c90c23188dd2d874fb8c7e8a76949683ca2b6a327911aad50","observation_id":"45ef172a-eea1-4dd2-9425-48fc54fe888e","resolution":{"observed_at":"2026-08-06T05:20:08.306800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:08.296635Z","title":"and Alam, F., 2020, November","venue":null,"work_id":"e5f2b319-875b-4acb-bfc7-8181270d0c45","year":2020},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:00.953049Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:89efdb6e689661890c758b67371892a1b20d35fe7fb8ad17d7f9907600a9b278","observation_id":"c4569869-30e5-4958-9b5e-63d1ea66a064","resolution":{"observed_at":"2026-08-06T05:20:08.299573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.07676","last_updated":"2021-01-25T10:56:45Z","snapshot_observed_at":"2026-08-07T14:12:26.620672Z","submitted_at":"2020-01-21T17:57:33Z","title":"Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.07676","snapshot_observed_at":"2026-08-06T05:20:01.006602Z","title":"and Sch ¨utze, H., 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:01.006602Z"},"links":{"cited_paper":"/paper/2001.07676","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:b56b17f3d383682850a9dbc7a3872f810aaba621dccdc625495e6f16a1ea3b9c","observation_id":"df2f1bc5-beb4-49d4-966d-1167460296d8","resolution":{"observed_at":"2026-08-06T05:20:01.006602Z","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":"2026-08-06T05:20:08.289035Z","title":"and Koshiba, T., 2022","venue":null,"work_id":"edb7c3a8-4c0d-4f30-8ae7-460acb2dd9f5","year":2022},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:01.080487Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:79ab15f88a915e864c9610cca1dc25756e946cfb5af6a609b88e46f41a349eed","observation_id":"a9d30fb3-a825-4a62-b818-d73745a7d959","resolution":{"observed_at":"2026-08-06T05:20:08.291482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:08.281239Z","title":"and Shahriyar, R., 2022","venue":null,"work_id":"67cc6a8a-7ce6-41d8-82b1-ec776f75ef0f","year":2022},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:01.142246Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:ac0689930540d25f2c69aa8c37cfde38fcf1f410cb24f2f2c7b0283e0d561b94","observation_id":"a3a50491-cbf4-4415-92a9-4bfb81677f77","resolution":{"observed_at":"2026-08-06T05:20:08.284355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.01878","last_updated":"2017-11-13T18:40:16Z","snapshot_observed_at":"2026-08-05T00:54:50.482913Z","submitted_at":"2017-10-05T04:26:49Z","title":"To prune, or not to prune: exploring the efficacy of pruning for model compression","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.01878","snapshot_observed_at":"2026-08-06T05:20:01.212860Z","title":"and Gupta, S., 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:01.212860Z"},"links":{"cited_paper":"/paper/1710.01878","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:38e08b36629d61f990e13324ad63e1367cf398f2904fc686cf0aa56f16cdf0b4","observation_id":"001a0020-1b10-4c3a-ae77-811ba9e33441","resolution":{"observed_at":"2026-08-06T05:20:01.212860Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.04732","last_updated":"2021-03-28T19:04:25Z","snapshot_observed_at":"2026-08-06T01:26:31.344805Z","submitted_at":"2019-10-10T17:44:18Z","title":"Structured Pruning of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.04732","snapshot_observed_at":"2026-08-06T05:20:01.280806Z","title":"and Lei, T., 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:01.280806Z"},"links":{"cited_paper":"/paper/1910.04732","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:87036ff379e3b6be2da19e7a544f1c67f5af4ee3700dd5b09d45777ea4b937b4","observation_id":"a70c3e9d-7212-4852-bdf8-ce5ea47ef585","resolution":{"observed_at":"2026-08-06T05:20:01.280806Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-06T05:20:01.325022Z","title":"and Toutanova, K., 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:01.325022Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:d739615807e212ef39956d1bd27d7f2149434ca300e5bf64a0ce086d9e72d9c7","observation_id":"42209ac9-e825-493d-8635-97c7229e491f","resolution":{"observed_at":"2026-08-06T05:20:01.325022Z","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":"2026-08-06T05:20:08.273642Z","title":"and Keutzer, K., 2020, April","venue":null,"work_id":"d4ce3f26-f9d8-485b-931e-654fe5ce0f71","year":2020},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:01.393167Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:f50cca71e5cd3bd45a33ce39483031954ed932cc8a5ba3d1ce46c30584445d7a","observation_id":"7da7a875-518e-4f96-9360-8835eaca06ae","resolution":{"observed_at":"2026-08-06T05:20:08.276498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.09355","last_updated":"2019-08-25T16:13:24Z","snapshot_observed_at":"2026-08-07T14:15:56.428313Z","submitted_at":"2019-08-25T16:13:24Z","title":"Patient Knowledge Distillation for BERT Model Compression","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.09355","snapshot_observed_at":"2026-08-06T05:20:01.478142Z","title":"and Liu, J., 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:01.478142Z"},"links":{"cited_paper":"/paper/1908.09355","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:1dd78dcacdba70dfb47e4c59f16f0533a7aec536312cb5e6a36e6eee1daebb27","observation_id":"7326ea17-1aee-4884-8c93-faf489794710","resolution":{"observed_at":"2026-08-06T05:20:01.478142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.02768","last_updated":"2020-02-25T21:50:07Z","snapshot_observed_at":"2026-08-06T02:18:18.491614Z","submitted_at":"2019-06-06T18:38:38Z","title":"Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP","version":3},"cited_work":{"arxiv_id":"1906.02768","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.02768","snapshot_observed_at":"2026-08-06T05:20:03.708446Z","title":"Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP","venue":"stat.ML","work_id":"94075488-31ca-4d88-841f-0826e9879d75","year":2019},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:01.551212Z"},"links":{"cited_paper":"/paper/1906.02768","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:2c11eb532ca2cb248cf8d942e78abba7f43d8c7fb4eb91ccd8610fb321cde994","observation_id":"a877abb5-31e3-429d-9c54-5e4d372715b9","resolution":{"observed_at":"2026-08-06T05:20:03.811426Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:08.213705Z","title":"and Yosinski, J., 2019","venue":null,"work_id":"02c54438-e2cf-4f4a-bd44-ab78628419e8","year":2019},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:01.649747Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:7d959b616117497bd3602dac9cbd1f1076b3acb5946d7ebc5f0759bf9d6952fd","observation_id":"86f0671f-4e1b-40f4-ab97-ea7a6240a804","resolution":{"observed_at":"2026-08-06T05:20:08.267868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:07.975821Z","title":"and Ganguli, S., 2020","venue":null,"work_id":"b8e7965f-bb3e-4222-9a66-9861d9e8d75a","year":2020},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:01.710719Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:0cd8fd2bf4366fad8374f6f39cdf5792361008f34a398024718a7f8d577b4cde","observation_id":"77c0357b-9087-4569-9885-7602c21b9d3a","resolution":{"observed_at":"2026-08-06T05:20:08.079159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:07.707130Z","title":"and Carbin, M., 2020","venue":null,"work_id":"2c1713fe-4301-4446-91b9-5c2a40df47d1","year":2020},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:01.771705Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:336311950ab2940d3835a4b8f3ce281ae533ce8f694e564afd75f9e89bfeb4a7","observation_id":"e1deaffa-09fb-494a-bbb9-761894996a25","resolution":{"observed_at":"2026-08-06T05:20:07.845511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:07.530829Z","title":null,"venue":null,"work_id":"f8f9ab93-21f7-47c8-9f6f-35e7bdbb3b1b","year":2018},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:01.845662Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:8afc3a00a98693d845ad7f91cd6fee5ed2c71047519c642098ded166dee33ca7","observation_id":"ed824e4e-39d5-47f0-a2f7-47b27e43f6da","resolution":{"observed_at":"2026-08-06T05:20:07.539982Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.04446","last_updated":"2020-11-09T14:12:07Z","snapshot_observed_at":"2026-08-07T12:10:12.068523Z","submitted_at":"2020-11-09T14:12:07Z","title":"Bangla Text Classification using Transformers","version":1},"cited_work":{"arxiv_id":"2011.04446","doi":null,"metadata_source":"pith","pith_arxiv_id":"2011.04446","snapshot_observed_at":"2026-08-06T05:20:03.577199Z","title":"Bangla Text Classification using Transformers","venue":"cs.CL","work_id":"f5a2c0c4-f688-4eb9-adbd-b1fb90343e57","year":2020},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:01.905765Z"},"links":{"cited_paper":"/paper/2011.04446","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:1fc87783ee02f03c3ab125cc552d99a42bf40c641706eac83710d67deaddd4cb","observation_id":"cc96807a-3942-4a81-a18d-1678aca085b0","resolution":{"observed_at":"2026-08-06T05:20:03.636822Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:07.369407Z","title":null,"venue":null,"work_id":"fb61aa7c-01a3-4a3f-a83e-d5a865172647","year":2019},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:01.983315Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:9dc74e29b52d733a199a847d074971fd60c5c6c9f884f43593487cf8f30f36a5","observation_id":"079fc756-04b3-4bab-9e65-9e9fda01a5c5","resolution":{"observed_at":"2026-08-06T05:20:07.454568Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00085","last_updated":"2020-04-30T20:21:02Z","snapshot_observed_at":"2026-08-03T16:29:44.476419Z","submitted_at":"2020-04-30T20:21:02Z","title":"AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00085","snapshot_observed_at":"2026-08-06T05:20:02.052319Z","title":"Khapra, and Pratyush Kumar","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:02.052319Z"},"links":{"cited_paper":"/paper/2005.00085","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:f9d84e448a9be77ec10ff48704f0b7ce1803c5c5a3f38275ebc471b19c9c2f3b","observation_id":"44510221-12dc-4a70-800b-bbafff14f425","resolution":{"observed_at":"2026-08-06T05:20:02.052319Z","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":"2026-08-06T05:20:07.165458Z","title":"Arid Hasan, Jannatul Tajrin, Shammur Absar Chowdhury, and Firoj Alam","venue":null,"work_id":"2114c17a-e9ca-4699-a82c-b0f36605e368","year":2020},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:02.118343Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:41dfa069ab5a1647fe6b39335f56a5ebb1b82f498fd2f671f0a9b422e3e804e4","observation_id":"e5b39010-8e05-4043-8793-5f46d5eaba81","resolution":{"observed_at":"2026-08-06T05:20:07.274159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:06.932628Z","title":null,"venue":null,"work_id":"624c30bd-0164-4891-a460-a1eef084eacf","year":2020},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:02.200481Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:07adafee9b4831d6a39730ad93986df8a5b5080617635ce4fc314bb2d7f9d6bf","observation_id":"e51bec10-928a-4cb7-ac54-5092e1d74b33","resolution":{"observed_at":"2026-08-06T05:20:07.044396Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:06.639908Z","title":null,"venue":null,"work_id":"95b9c157-cdb9-422e-8399-fda7178755d2","year":null},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:02.243558Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:2220d58710b1f1691ba4a60c892f5bb6b8276cbec292d438d3e2b5cad8c416e6","observation_id":"8f681cf8-fd67-4557-a80b-98ebf0f7e695","resolution":{"observed_at":"2026-08-06T05:20:06.797245Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:06.075933Z","title":null,"venue":null,"work_id":"df85a9b7-ee9a-46ee-aad4-b6a2adfaf397","year":2018},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:02.397580Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:48c2d4b62c07c9308668a6398276675e79f4dcb715bbe73c57dd871d29d4e48f","observation_id":"a52be7ae-9bac-4478-8c91-70490b48356e","resolution":{"observed_at":"2026-08-06T05:20:06.176483Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:05.825048Z","title":null,"venue":null,"work_id":"41bb8cfd-857d-4716-acd6-8dbebaa6e026","year":2008},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:02.535725Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:a06668780628055fcbc83e0f1fd6f504f87be94fe7de5e2c1f39410fe4d72701","observation_id":"c177b094-9cab-47b4-a5a9-3bea23546e1e","resolution":{"observed_at":"2026-08-06T05:20:05.937441Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:05.598560Z","title":null,"venue":null,"work_id":"331f4303-cef6-468b-91d3-da561cb2b343","year":2010},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:02.650088Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:b7f938caa45fa235832e7dec1642be270bba9a3b4d503efc2c7594085a9c6421","observation_id":"58914d96-bcb9-4952-8e33-d852ffa01aca","resolution":{"observed_at":"2026-08-06T05:20:05.692256Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:05.378853Z","title":null,"venue":null,"work_id":"a2f5899a-5a4c-43f8-bb88-4ed9e9b39900","year":2007},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:02.781798Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:c59ac12f75b4570ba3fab2af9f5619f43313965bf2dd3234877b07b4b89603cc","observation_id":"f02e79e3-facc-4ddf-9303-3cf849b7ce04","resolution":{"observed_at":"2026-08-06T05:20:05.502645Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:05.143042Z","title":null,"venue":null,"work_id":"b545abb6-6774-4053-8da7-6375d1cc6030","year":2008},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:02.893585Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:b471352ba05670c485862c4725a2b2b551bcab139f08a636c22ecfe8be2bdaac","observation_id":"c001f821-91df-40ed-ad94-95cfaa97a1bc","resolution":{"observed_at":"2026-08-06T05:20:05.260673Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:04.926374Z","title":null,"venue":null,"work_id":"c8ae4216-3468-41c7-b48f-a45fcb69ef40","year":2020},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:03.027803Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:18173dc368fb2a020ed09db615b5a15a094c33c2693cef5974a9aeb98975e373","observation_id":"63337a13-63ce-4ae1-b359-288f0be7fea3","resolution":{"observed_at":"2026-08-06T05:20:05.051935Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.07613","last_updated":"2019-11-15T08:22:33Z","snapshot_observed_at":"2026-07-06T08:37:48.713942Z","submitted_at":"2019-11-15T08:22:33Z","title":"A Subword Level Language Model for Bangla Language","version":1},"cited_work":{"arxiv_id":"1911.07613","doi":null,"metadata_source":"pith","pith_arxiv_id":"1911.07613","snapshot_observed_at":"2026-08-06T05:20:03.399637Z","title":"A Subword Level Language Model for Bangla Language","venue":"cs.CL","work_id":"f34275a3-a365-408c-8acc-905c4fc55e1e","year":2019},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:03.173552Z"},"links":{"cited_paper":"/paper/1911.07613","citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:4c46033d064f567f169a726dddab41f0b95fbb2e2e4b1b29e00f9cdbdbe59dba","observation_id":"e4ccf02c-6c67-4d48-91ff-6c5d03f5056a","resolution":{"observed_at":"2026-08-06T05:20:03.459848Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:04.698250Z","title":null,"venue":null,"work_id":"39685ab0-6c7d-46ad-9569-822655f80dcd","year":2022},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:03.263656Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:d2505446dde252bbd720ae4bb0aee02a69cead334fa38a214f0d7fb19b1cf857","observation_id":"65e18dba-3736-464b-bca8-b6547f14ae47","resolution":{"observed_at":"2026-08-06T05:20:04.807272Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T05:20:06.330706Z","title":"In Mining Intelligence and Knowledge Explo- ration, Rajendra Prasath, Anil Kumar Vuppala, and T","venue":null,"work_id":"74785d48-e98b-436d-bdf3-ffee9d283add","year":null},"citing_paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-06T05:20:02.280571Z"},"links":{"citing_paper":"/paper/2608.05104"},"observation_digest":"sha256:d18c692b7451efcf18a1359340913bbad03f11aaf9d85cd1dd7882ad93b5cd67","observation_id":"ddcc655d-983a-4f1f-8864-418bfadee45f","resolution":{"observed_at":"2026-08-06T05:20:06.464726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.05104","last_updated":"2026-08-05T17:42:33Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T20:17:14.105625Z","submitted_at":"2026-08-05T17:42:33Z","title":"BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":24,"verified_exact":3,"verified_fuzzy":21},"total_outbound_references":51},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2608.05104."}