{"as_of":"2026-08-12T14:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0a227e35b8e6ebae531ee325d21e4784507e8a227eafc85dc89d86bacfbdce1e","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:09:46.763100Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2505.16225/citation-record","integrity":"/paper/2505.16225/integrity","json":"/paper/2505.16225/citation-record.json","paper":"/paper/2505.16225"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:49.917174Z","title":"M., Bohnet, B., Rosias, L., Chan, S","venue":null,"work_id":"89c5f6f7-ad5e-4ae5-88ee-d72f4c6a4294","year":2024},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:42.150653Z"},"links":{"citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:da041c77eb1917b1865b2ffae1b0d09f0bf4485e6504facca9f41964266c8538","observation_id":"44c774ba-1e4c-43bc-8344-a7129b7ef201","resolution":{"observed_at":"2026-08-07T15:09:49.952799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.00200","last_updated":"2025-03-03T19:53:28Z","snapshot_observed_at":"2026-08-11T15:16:58.169138Z","submitted_at":"2024-04-30T21:06:52Z","title":"In-Context Learning with Long-Context Models: An In-Depth Exploration","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.00200","snapshot_observed_at":"2026-08-07T15:09:42.428377Z","title":"R., and Neubig, G","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:42.428377Z"},"links":{"cited_paper":"/paper/2405.00200","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:a31e8d5171a47f6f7ea4fa2a55b00c7744a81f46ef02510e43ceceb35ef239b4","observation_id":"27a70b40-34e8-4690-9982-4c7a6ca5290b","resolution":{"observed_at":"2026-08-07T15:09:42.428377Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:42.510056Z","title":"D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:42.510056Z"},"links":{"citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:09db776e8b30a711d2bf14a8ecf4c5ff851c6c8755316843a60e4a45a7f5433f","observation_id":"2d11548b-b947-41d7-b8a2-79444d4955e1","resolution":{"observed_at":"2026-08-07T15:09:42.510056Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.03730","last_updated":"2024-06-06T04:05:54Z","snapshot_observed_at":"2026-08-09T04:15:30.415867Z","submitted_at":"2024-06-06T04:05:54Z","title":"FastGAS: Fast Graph-based Annotation Selection for In-Context Learning","version":1},"cited_work":{"arxiv_id":"2406.03730","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.03730","snapshot_observed_at":"2026-08-07T15:09:47.740943Z","title":"FastGAS: Fast Graph-based Annotation Selection for In-Context Learning","venue":"cs.LG","work_id":"82fce2b4-3e20-45a2-8dea-de9c44e298c6","year":2024},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:42.797353Z"},"links":{"cited_paper":"/paper/2406.03730","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:3f0d21ad3b54f5686fcbc61393a129ca11e70b933712c4fe71538e51c8b44638","observation_id":"8c4c4b9c-e657-46c7-ad2c-4258f744ba19","resolution":{"observed_at":"2026-08-07T15:09:47.893781Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.15334","last_updated":"2024-12-20T01:24:51Z","snapshot_observed_at":"2026-08-11T16:52:37.936776Z","submitted_at":"2024-06-21T17:50:02Z","title":"Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.15334","snapshot_observed_at":"2026-08-07T15:09:43.013943Z","title":"Multimodal task vectors enable many- shot multimodal in-context learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:43.013943Z"},"links":{"cited_paper":"/paper/2406.15334","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:de16ca7af575d80d05622bd3a7d40a30a890bac6b1687a813efd72dca08006ad","observation_id":"277abd81-4632-4e7f-956a-54db7f93624a","resolution":{"observed_at":"2026-08-07T15:09:43.013943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.09118","last_updated":"2022-08-29T12:17:32Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-12-16T18:57:37Z","title":"Unsupervised Dense Information Retrieval with Contrastive Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.09118","snapshot_observed_at":"2026-08-07T15:09:43.146271Z","title":"Unsupervised dense infor- mation retrieval with contrastive learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:43.146271Z"},"links":{"cited_paper":"/paper/2112.09118","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:907dabc3eb773962ad6c84fac74b5b1c4c5ca28f59c3eb613e748dfe7422cbe2","observation_id":"7d596180-3436-4d38-9648-9711953afb01","resolution":{"observed_at":"2026-08-07T15:09:43.146271Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.13121","last_updated":"2024-06-19T00:28:58Z","snapshot_observed_at":"2026-07-06T18:33:24.206083Z","submitted_at":"2024-06-19T00:28:58Z","title":"Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.13121","snapshot_observed_at":"2026-08-07T15:09:43.637290Z","title":"S., Boratko, M., Luan, Y ., Arnold, S","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:43.637290Z"},"links":{"cited_paper":"/paper/2406.13121","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:639a5ff8c24fd612577b7f1adf3b471d9e675a9d87606fe394500b5a9e165fff","observation_id":"8a7bc83b-72d7-4f21-8392-a6b8aa9e2418","resolution":{"observed_at":"2026-08-07T15:09:43.637290Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04931","last_updated":"2023-02-09T20:53:12Z","snapshot_observed_at":"2026-07-06T14:50:14.766783Z","submitted_at":"2023-02-09T20:53:12Z","title":"In-Context Learning with Many Demonstration Examples","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.04931","snapshot_observed_at":"2026-08-07T15:09:43.712823Z","title":"In-context learning with many demonstration examples","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:43.712823Z"},"links":{"cited_paper":"/paper/2302.04931","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:e7c701f2e0572ad8ce8f11bac91cc027a35118c8e60e094aff6775e6d4dfb1eb","observation_id":"542d2853-0d9a-4f8b-8b5d-fb4d94966913","resolution":{"observed_at":"2026-08-07T15:09:43.712823Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.02060","last_updated":"2024-06-12T02:46:16Z","snapshot_observed_at":"2026-08-10T19:55:45.249416Z","submitted_at":"2024-04-02T15:59:11Z","title":"Long-context LLMs Struggle with Long In-context Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.02060","snapshot_observed_at":"2026-08-07T15:09:43.774129Z","title":"D., Yue, X., and Chen, W","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:43.774129Z"},"links":{"cited_paper":"/paper/2404.02060","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:0039ccc22168682f31790887fd6c304cbeeb3321909f8709febfe7ca78238f9b","observation_id":"73ad850e-624c-4ae7-9c86-67b414a48740","resolution":{"observed_at":"2026-08-07T15:09:43.774129Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.06804","last_updated":"2021-01-17T23:38:40Z","snapshot_observed_at":"2026-08-02T23:15:54.644289Z","submitted_at":"2021-01-17T23:38:40Z","title":"What Makes Good In-Context Examples for GPT-$3$?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.06804","snapshot_observed_at":"2026-08-07T15:09:43.888545Z","title":"What makes good in-context examples for gpt- 3? arXiv preprint arXiv:2101.06804,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:43.888545Z"},"links":{"cited_paper":"/paper/2101.06804","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:4db969d34576fe8a705dc982fb9e39383c88a6c446a77fd1076616043e23477c","observation_id":"43014f30-f482-4cdf-849d-8da5cd9e8479","resolution":{"observed_at":"2026-08-07T15:09:43.888545Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08786","last_updated":"2022-03-03T12:10:58Z","snapshot_observed_at":"2026-07-06T11:01:05.577957Z","submitted_at":"2021-04-18T09:29:16Z","title":"Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08786","snapshot_observed_at":"2026-08-07T15:09:44.074542Z","title":"Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:44.074542Z"},"links":{"cited_paper":"/paper/2104.08786","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:5c7d870eed44bf1a1286758ad9f719c67bd48d4aa68febe176fe946582881719","observation_id":"94fa8163-c4d3-4191-bb24-5167f9610be8","resolution":{"observed_at":"2026-08-07T15:09:44.074542Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.09881","last_updated":"2024-12-30T04:27:05Z","snapshot_observed_at":"2026-08-10T11:49:13.645760Z","submitted_at":"2023-10-15T16:40:19Z","title":"In-Context Learning with Iterative Demonstration Selection","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.09881","snapshot_observed_at":"2026-08-07T15:09:44.406330Z","title":"In- context learning with iterative demonstration selection","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:44.406330Z"},"links":{"cited_paper":"/paper/2310.09881","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:2c59ce3369373e5e01d0671702e552903002511fefa1dc848b328a972a7b58c1","observation_id":"33ccc29b-9a80-40c4-bc51-ac0598212838","resolution":{"observed_at":"2026-08-07T15:09:44.406330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10084","last_updated":"2019-08-27T08:50:17Z","snapshot_observed_at":"2026-07-06T08:17:05.681370Z","submitted_at":"2019-08-27T08:50:17Z","title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10084","snapshot_observed_at":"2026-08-07T15:09:44.570500Z","title":"and Gurevych, I","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:44.570500Z"},"links":{"cited_paper":"/paper/1908.10084","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:d88f3562da8b1b40fbd4aaf0193887cedd6fae8f7f1e0d2fdc4e9b90001e6658","observation_id":"62a4e4ff-807a-4f30-9420-2d8c2665c841","resolution":{"observed_at":"2026-08-07T15:09:44.570500Z","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":"2026-08-07T15:09:49.376722Z","title":"Learning to retrieve prompts for in-context learning","venue":null,"work_id":"bac8572f-ea94-422f-80a4-1883b3ef25b7","year":2022},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:44.988846Z"},"links":{"citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:a44d14f1a1647db9028589e2c1745052af1e345830b97f627ca8dce49bf851e2","observation_id":"f9773e69-249f-460b-adba-1800c1180bd1","resolution":{"observed_at":"2026-08-07T15:09:49.450913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.01975","last_updated":"2022-09-05T14:01:15Z","snapshot_observed_at":"2026-08-12T00:49:57.766433Z","submitted_at":"2022-09-05T14:01:15Z","title":"Selective Annotation Makes Language Models Better Few-Shot Learners","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.01975","snapshot_observed_at":"2026-08-07T15:09:45.125390Z","title":"H., Shi, W., Wang, T., Xin, J., Zhang, R., Ostendorf, M., Zettlemoyer, L., Smith, N","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:45.125390Z"},"links":{"cited_paper":"/paper/2209.01975","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:862cf26d48e0b2f6034376ef644adcab9c3937dfd08b742f7d5a38bbd92ccba4","observation_id":"3bc57197-ae01-4c83-a78e-3241e8619101","resolution":{"observed_at":"2026-08-07T15:09:45.125390Z","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":"2026-08-07T15:09:49.216407Z","title":"W., Chowdhery, A., Le, Q., Chi, E., Zhou, D., et al","venue":null,"work_id":"81fbddb5-b28d-4de9-9943-d0e2f426928c","year":2023},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:45.271280Z"},"links":{"citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:593c56d492d3ff2f806da3db86bd1df9eefb2bd2dbb87da3a748ce4640595872","observation_id":"07eaf020-7728-4bad-923a-f5a67efe8ead","resolution":{"observed_at":"2026-08-07T15:09:49.273286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05530","last_updated":"2024-12-16T17:39:39Z","snapshot_observed_at":"2026-07-06T17:41:42.995949Z","submitted_at":"2024-03-08T18:54:20Z","title":"Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.05530","snapshot_observed_at":"2026-08-07T15:09:45.429468Z","title":"I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:45.429468Z"},"links":{"cited_paper":"/paper/2403.05530","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:b50812e1efea622baab6d753a496a1ffbed82d618ffa07c57c875fb0853ffc0e","observation_id":"1300cc60-68b8-44fd-bbc0-440e0be44969","resolution":{"observed_at":"2026-08-07T15:09:45.429468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.07164","last_updated":"2024-01-26T07:04:02Z","snapshot_observed_at":"2026-08-10T12:59:10.737504Z","submitted_at":"2023-07-14T05:23:08Z","title":"Learning to Retrieve In-Context Examples for Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.07164","snapshot_observed_at":"2026-08-07T15:09:45.697415Z","title":"Learning to retrieve in-context examples for large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:45.697415Z"},"links":{"cited_paper":"/paper/2307.07164","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:2b1dac1e937da7db82aa7a8d354a78b3a90d1560c00af4c420cf09ee781bdb9c","observation_id":"8783880f-34cd-4d53-8ac8-de10203aee2b","resolution":{"observed_at":"2026-08-07T15:09:45.697415Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.04873","last_updated":"2025-03-06T16:38:12Z","snapshot_observed_at":"2026-08-09T19:09:10.315827Z","submitted_at":"2025-03-06T16:38:12Z","title":"Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?","version":1},"cited_work":{"arxiv_id":"2503.04873","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.04873","snapshot_observed_at":"2026-08-07T15:09:46.960678Z","title":"Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?","venue":"cs.CL","work_id":"011ae81c-1848-41ca-9c18-0c4a546fe98a","year":2025},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:45.845081Z"},"links":{"cited_paper":"/paper/2503.04873","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:7e95054902368545b3aeb6fc8bebf2b4ecbaad2c54d25896fdeb51fe6b4106bf","observation_id":"33f96c4b-a84f-49ca-980b-b38f54ff3144","resolution":{"observed_at":"2026-08-07T15:09:47.080129Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.04070","last_updated":"2025-05-27T09:48:24Z","snapshot_observed_at":"2026-08-10T21:43:43.284791Z","submitted_at":"2025-01-07T14:57:08Z","title":"More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.04070","snapshot_observed_at":"2026-08-07T15:09:45.969946Z","title":"More is not always better? enhanc- ing many-shot in-context learning with differentiated and reweighting objectives","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:45.969946Z"},"links":{"cited_paper":"/paper/2501.04070","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:db26a19742c2baacb4a96584221656c14c9b3e5c0e6ef808bf17e7b344feed8a","observation_id":"4d8ec9e0-19d2-412d-9d3e-c99b73549966","resolution":{"observed_at":"2026-08-07T15:09:45.969946Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.18223","last_updated":"2026-03-18T05:34:39Z","snapshot_observed_at":"2026-08-06T23:27:24.356320Z","submitted_at":"2023-03-31T17:28:46Z","title":"A Survey of Large Language Models","version":19},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.18223","snapshot_observed_at":"2026-08-07T15:09:46.107837Z","title":"X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y ., Min, Y ., Zhang, B., Zhang, J., Dong, Z., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:46.107837Z"},"links":{"cited_paper":"/paper/2303.18223","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:6e0361a15f1c1c230c368f9e0ac08bb4cc28cccc27217f1383cbdfd76d6b9835","observation_id":"241164e3-05e1-4e0c-9d04-f8e3587a8cb1","resolution":{"observed_at":"2026-08-07T15:09:46.107837Z","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":"2026-08-07T15:09:49.042853Z","title":null,"venue":null,"work_id":"c86698ec-d60a-4a56-81df-5defe86886ef","year":2018},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:46.281149Z"},"links":{"citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:bde3ce4124968c24412eb430db81d0bad87914d45d7717bdf51ad6554843a0b3","observation_id":"5ac60ef3-b72e-4509-ae8c-8d65cf337254","resolution":{"observed_at":"2026-08-07T15:09:49.114369Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T15:09:48.889137Z","title":null,"venue":null,"work_id":"4542e17b-54ce-4180-b403-59754562cda7","year":2017},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:46.346449Z"},"links":{"citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:a06b26194c0f14cfe8c077e4923a1b7f119992d2dc1f30bdb40e0a69f3910f47","observation_id":"ffb24d5c-207c-4b44-a095-20856e767d0a","resolution":{"observed_at":"2026-08-07T15:09:48.959036Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T15:09:48.597384Z","title":"(2018), we set σ as the identity function and W as the identity matrix","venue":null,"work_id":"6e07dffa-b49b-44d2-b598-46aab70c14a1","year":2020},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:46.457921Z"},"links":{"citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:41055e6231edd840166b8372853bfbc8beb8dc973750b4abb3e7a1e7a37eba13","observation_id":"c96b6848-02e3-412e-b1de-9eab88c2f71f","resolution":{"observed_at":"2026-08-07T15:09:48.721004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T15:09:48.362387Z","title":"What is the article about?","venue":null,"work_id":"78ad65ca-5a90-4a86-a687-db7bc2bb12c1","year":2018},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:46.581844Z"},"links":{"citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:0ba89f1d386e95a07f341cb7dff1fff3f37a443e5ae3564dce6ecd72f101c214","observation_id":"8b9da085-9041-450c-b574-a4a606854217","resolution":{"observed_at":"2026-08-07T15:09:48.450415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T15:09:48.145267Z","title":"Sentence: Pharmaceuticals group Orion Corp reported a fall in its third-quarter earnings, which were impacted by larger expenditures on R&D and marketing. Label: negative","venue":null,"work_id":"0c040cd7-ff7a-4b76-813b-16330e9d4156","year":1937},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":1937,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:46.763100Z"},"links":{"citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:94a0744ae8976cb101bf37137bac17d2323ba395cd05a55d6abf1e6e0f5dffed","observation_id":"1116cda8-6e83-46db-9d22-5bc39c367f0c","resolution":{"observed_at":"2026-08-07T15:09:48.227189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T15:09:49.542307Z","title":"B., and Lapata, M","venue":null,"work_id":"b37e42df-6950-4216-8397-b7d9e7a4f874","year":2018},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:44.221492Z"},"links":{"citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:eb8348296b2f44011fc8294d38e31ee1f6d1100457d232cb158ed06446b50ead","observation_id":"137e7d6a-f8de-4614-8f9e-44223a53200e","resolution":{"observed_at":"2026-08-07T15:09:49.618993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.03654","last_updated":"2021-10-06T21:02:00Z","snapshot_observed_at":"2026-07-06T09:26:29.068023Z","submitted_at":"2020-06-05T19:54:34Z","title":"DeBERTa: Decoding-enhanced BERT with Disentangled Attention","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.03654","snapshot_observed_at":"2026-08-07T15:09:42.940547Z","title":"Deberta: Decoding- enhanced bert with disentangled attention","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:42.940547Z"},"links":{"cited_paper":"/paper/2006.03654","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:40dc9ef1b238b8d42a6bc3c5f2806fcbaec37c1e62a0109f928a7edfb0dcc912","observation_id":"d09607da-e044-4ab4-9269-beea4dbcb0ec","resolution":{"observed_at":"2026-08-07T15:09:42.940547Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.06755","last_updated":"2020-02-17T03:23:13Z","snapshot_observed_at":"2026-08-07T19:52:25.867754Z","submitted_at":"2020-02-17T03:23:13Z","title":"Unifying Graph Convolutional Neural Networks and Label Propagation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.06755","snapshot_observed_at":"2026-08-07T15:09:45.569126Z","title":"and Leskovec, J","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:45.569126Z"},"links":{"cited_paper":"/paper/2002.06755","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:8004b82f114d0e97a74bb5be674f160e8dc0bc0e18ceb07cc640c227b759e38a","observation_id":"2b228e8d-c087-4e51-a1b4-8c3fba67edf7","resolution":{"observed_at":"2026-08-07T15:09:45.569126Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.12022","last_updated":"2023-11-20T18:57:34Z","snapshot_observed_at":"2026-08-10T12:02:35.919497Z","submitted_at":"2023-11-20T18:57:34Z","title":"GPQA: A Graduate-Level Google-Proof Q&A Benchmark","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.12022","snapshot_observed_at":"2026-08-07T15:09:44.851478Z","title":"L., Stickland, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:44.851478Z"},"links":{"cited_paper":"/paper/2311.12022","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:7643660365de76b9fd18db1cfb5a3e8f6e9f5289178bbfc89dd3fa350db742cc","observation_id":"f367d205-dd93-41b7-be88-df6edaf08031","resolution":{"observed_at":"2026-08-07T15:09:44.851478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.04807","last_updated":"2020-03-10T15:33:54Z","snapshot_observed_at":"2026-08-09T23:05:47.868289Z","submitted_at":"2020-03-10T15:33:54Z","title":"Efficient Intent Detection with Dual Sentence Encoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.04807","snapshot_observed_at":"2026-08-07T15:09:42.604095Z","title":"Efficient intent detection with dual sentence encoders","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:42.604095Z"},"links":{"cited_paper":"/paper/2003.04807","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:290591a62b7fd6086748673b452ad288090c11272d67b06297b1379fe5f2c76f","observation_id":"23a1121b-0a62-4560-9ac8-898239de4ca6","resolution":{"observed_at":"2026-08-07T15:09:42.604095Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.01200","last_updated":"2023-06-01T23:27:49Z","snapshot_observed_at":"2026-08-11T12:46:04.825798Z","submitted_at":"2023-06-01T23:27:49Z","title":"Multi-Dimensional Evaluation of Text Summarization with In-Context Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.01200","snapshot_observed_at":"2026-08-07T15:09:43.340125Z","title":"M., Fernandes, P., Liu, P., Neubig, G., and Zhou, C","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:43.340125Z"},"links":{"cited_paper":"/paper/2306.01200","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:4fff882e469e468f63fdaa2bb99176f097d7c9813b74fa693e07a607fa506d13","observation_id":"c81ba85c-edb7-42e1-b1b1-06c4df76d0d8","resolution":{"observed_at":"2026-08-07T15:09:43.340125Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16926","last_updated":"2025-05-28T07:02:54Z","snapshot_observed_at":"2026-08-12T04:00:28.313712Z","submitted_at":"2024-12-22T08:55:19Z","title":"Revisiting In-Context Learning with Long Context Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16926","snapshot_observed_at":"2026-08-07T15:09:42.345000Z","title":"J., Gupta, P., Dalmia, S., Kolhar, P., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:42.345000Z"},"links":{"cited_paper":"/paper/2412.16926","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:5a71f3550ad6f1d2bc25752ad94aacd62279371c9c19a57ad00cf643a6994c65","observation_id":"7a8413dd-c119-4635-ae4d-7266a9048d9b","resolution":{"observed_at":"2026-08-07T15:09:42.345000Z","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":"2026-08-07T15:09:49.715115Z","title":"A., Wang, J","venue":null,"work_id":"786fc258-a8b3-46ad-a4ed-069950e9554d","year":2024},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:43.489519Z"},"links":{"citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:0f8d122b14b2e870eac42a029dd044df10233a26f585990805a49b8dee0c1a5f","observation_id":"ed058904-0623-4365-b542-bd74f55a7c02","resolution":{"observed_at":"2026-08-07T15:09:49.820095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.02437","last_updated":"2022-12-05T17:25:15Z","snapshot_observed_at":"2026-08-08T19:17:55.815131Z","submitted_at":"2022-12-05T17:25:15Z","title":"In-context Examples Selection for Machine Translation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.02437","snapshot_observed_at":"2026-08-07T15:09:42.234982Z","title":"In-context examples selection for machine translation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:42.234982Z"},"links":{"cited_paper":"/paper/2212.02437","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:8d1288480bff9a63934437f45a78dcbde86e3a944ecbdd92bcc901d7dbf27fbe","observation_id":"cf5339df-b526-43a3-8924-5dd81d0268a2","resolution":{"observed_at":"2026-08-07T15:09:42.234982Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00547","last_updated":"2020-06-03T00:31:11Z","snapshot_observed_at":"2026-07-06T09:16:56.708811Z","submitted_at":"2020-05-01T18:00:02Z","title":"GoEmotions: A Dataset of Fine-Grained Emotions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00547","snapshot_observed_at":"2026-08-07T15:09:42.852146Z","title":"Goemotions: A dataset of fine-grained emotions","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:42.852146Z"},"links":{"cited_paper":"/paper/2005.00547","citing_paper":"/paper/2505.16225"},"observation_digest":"sha256:681e89f4bfafe0c0d2d18523cdfc842b29a2f7eca18fa69532e36bc5f4f30d85","observation_id":"ba0e3324-869b-4cca-8bd2-939fa5b0cf13","resolution":{"observed_at":"2026-08-07T15:09:42.852146Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.16225","last_updated":"2025-05-26T01:51:47Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-09T10:53:21.539468Z","submitted_at":"2025-05-22T04:54:27Z","title":"MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":26,"verified_exact":2,"verified_fuzzy":8},"total_outbound_references":36},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2505.16225."}