{"as_of":"2026-08-09T19:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:be8e3eabff09ca1762c16e00a332ff1448a83078be5fca9b415f8ff9a4559f1e","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-09T02:52:28.852922Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2607.07678/citation-record","integrity":"/paper/2607.07678/integrity","json":"/paper/2607.07678/citation-record.json","paper":"/paper/2607.07678"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T02:55:53.891599Z","title":"Alabdulmohsin, Vinh Quoc Tran, and Mostafa Dehghani","venue":null,"work_id":"bccc7c32-3f7d-4022-a7cc-1c283d512c2b","year":2024},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:499de233b7076cfbd1bb63dbbbfe77692d0914fd2ede7230a30476ee54f96a94","observation_id":"1694792b-e549-4aba-b9d3-5d02733aaaad","resolution":{"observed_at":"2026-07-09T02:55:53.892857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.831412Z","title":"Neural machine translation by jointly learning to align and translate","venue":null,"work_id":"895155b9-ee81-4c82-bedc-cda2414cbaa7","year":2015},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:d0069da81abbf8e18887d35889570f5797d26c1fc4481319ab6c98e86b6ceb74","observation_id":"0ce85a18-55d6-4b94-b8ff-326507bd9a14","resolution":{"observed_at":"2026-07-09T02:55:53.832797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.855187Z","title":"Round and round we go! what makes rotary positional encodings useful? InICLR, 2025","venue":null,"work_id":"a6e38648-03c2-46ce-96b6-e59a96264c8a","year":2025},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:bf54d40c4e05ccb002deb3d1c08a0aed008ec23464ccff65817cb4dcff0354ef","observation_id":"7063f56a-3c4d-4bd3-9053-34bb55f5ba5e","resolution":{"observed_at":"2026-07-09T02:55:53.856586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.01261","last_updated":"2018-10-17T17:51:36Z","snapshot_observed_at":"2026-07-06T06:42:54.610341Z","submitted_at":"2018-06-04T17:58:18Z","title":"Relational inductive biases, deep learning, and graph networks","version":3},"cited_work":{"arxiv_id":"1806.01261","doi":"10.48550/arxiv.1806.01261","metadata_source":"pith","pith_arxiv_id":"1806.01261","snapshot_observed_at":"2026-07-10T16:47:24.357908Z","title":"Relational inductive biases, deep learning, and graph networks","venue":"cs.LG","work_id":"858410c0-7a66-4b27-b4e5-49aee9725be0","year":2018},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"cited_paper":"/paper/1806.01261","citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:67841b03476d2405abdd964d1ee7948494e2ba4499f0bd60453d62d48eec8039","observation_id":"da561eac-8b08-41aa-8b20-b3d4756f7c61","resolution":{"observed_at":"2026-07-09T02:55:53.505422Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-03T17:38:15.281147+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T17:38:15.281147+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.845664Z","title":"by Parts","venue":null,"work_id":"bb51f895-c471-4125-8214-5b9bde2dc8bc","year":2023},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:8a864654a66ae154ff601817fd29c30db25360f38b4020e12e22887b36e2cb6a","observation_id":"04ff8185-920b-4cf7-9be6-17d86a28ebbe","resolution":{"observed_at":"2026-07-09T02:55:53.846895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.843607Z","title":"NTK-Aware Scaled RoPE Allows LLaMA Models to Have Extended (8k+) Context Size Without Any Fine-Tuning and Minimal Perplexity Degradation","venue":null,"work_id":"b24b01ec-4cb3-4219-9441-1400181becdd","year":2023},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:e3a62395a1f8acd4a2f1b53f36dbcb424115bfa33bf297fd789405fcce0ad65b","observation_id":"ece55931-732c-4909-96b3-f24412798e09","resolution":{"observed_at":"2026-07-09T02:55:53.845011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.15595","last_updated":"2023-06-28T04:26:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-27T16:26:26Z","title":"Extending Context Window of Large Language Models via Positional Interpolation","version":2},"cited_work":{"arxiv_id":"2306.15595","doi":"10.48550/arxiv.2306.15595","metadata_source":"pith","pith_arxiv_id":"2306.15595","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Extending Context Window of Large Language Models via Positional Interpolation","venue":"cs.CL","work_id":"c8b6df85-e7da-4bd8-90a4-d309cc2a0f60","year":2023},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"cited_paper":"/paper/2306.15595","citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:2bd07101314a4d60d4c8abfa79f9c67bb8e1d730f687a7f0cec9df8bd1b6f273","observation_id":"29d6936a-fe67-4fc3-a44e-5fab0b4cf375","resolution":{"observed_at":"2026-07-09T02:55:53.504805Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-01T10:38:12.283235+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T10:38:12.283235+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-07T01:45:38.840969Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":"2110.14168","doi":"10.1002/j.1545-","metadata_source":"pith","pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Training Verifiers to Solve Math Word Problems","venue":"cs.LG","work_id":"acab1aa8-b4d6-40e0-a3ee-25341701dca2","year":2021},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:e8ca278f2bbe355b9b64a0e3200bb6e8ff7f21313206abf5f6433f020b474ad4","observation_id":"841f58c6-d6fb-49c7-96e8-417e11025cc0","resolution":{"observed_at":"2026-07-09T02:55:53.520293Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.13161","last_updated":"2025-11-30T07:03:40Z","snapshot_observed_at":"2026-08-08T11:46:20.080583Z","submitted_at":"2025-04-17T17:58:13Z","title":"Nemotron-CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training","version":2},"cited_work":{"arxiv_id":"2504.13161","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.13161","snapshot_observed_at":"2026-07-09T02:55:53.506375Z","title":"Nemotron-climb: Clustering-based iterative data mixture bootstrap- ping for language model pre-training","venue":"cs.CL","work_id":"412269c2-ec20-4706-8440-3bb6773c3fc0","year":2025},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"cited_paper":"/paper/2504.13161","citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:e797fc6cdeb1da3bdd7c3ef6d5417a270c1a9527eb8f219eb5fb7cddd447a505","observation_id":"90fbd041-b348-4dd2-8ec7-42a983133588","resolution":{"observed_at":"2026-07-09T02:55:53.507969Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.849247Z","title":"Dynamically Scaled RoPE Further Increases Performance of Long Context LLaMA with Zero Fine-Tuning","venue":null,"work_id":"746ea4fa-fd24-44ee-b983-ba9205eb2be7","year":2023},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:358d32214d4436e529f0b5df5e28763a486e2c4209ed25996ebc8248643c8cc8","observation_id":"ba26b5e8-bba0-4e5a-8247-860db6e21584","resolution":{"observed_at":"2026-07-09T02:55:53.850561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.23771","last_updated":"2025-07-27T14:45:02Z","snapshot_observed_at":"2026-08-03T10:49:26.615197Z","submitted_at":"2024-10-31T09:39:28Z","title":"What is Wrong with Perplexity for Long-context Language Modeling?","version":5},"cited_work":{"arxiv_id":"2410.23771","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.23771","snapshot_observed_at":"2026-07-09T02:55:53.524341Z","title":"What is wrong with perplexity for long-context language modeling?","venue":"cs.CL","work_id":"2c2295b4-54be-4cd3-9e05-25fce19e2b57","year":2024},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"cited_paper":"/paper/2410.23771","citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:ff9add79a99a1b4b92f777730b479dd1d677d52bba38b7b3a4efe4548f2805c0","observation_id":"8dd77c53-abfc-460f-8cfb-1a8a671423b9","resolution":{"observed_at":"2026-07-09T02:55:53.525674Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.886302Z","title":"Rethinking invariance in in-context learning","venue":null,"work_id":"205ddfa3-3d50-415d-9d90-876677f81e65","year":2025},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:576fcefd494058ce4bf9293c9049c21e960ea78c2d753f130881628fb17e5980","observation_id":"7ac5d788-9e4a-46dc-9fc9-228b099904e7","resolution":{"observed_at":"2026-07-09T02:55:53.887524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.884358Z","title":"When attention sink emerges in language models: An empirical view","venue":null,"work_id":"1e3895ca-8b68-4e73-a16d-19623fe5d56d","year":2025},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:eb89fe91ac04895c5b5b1559feffd2398fca8568cf62242577b6bc3fed739eb2","observation_id":"32be4c5b-cf09-41e6-941a-34772186d023","resolution":{"observed_at":"2026-07-09T02:55:53.885655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.839694Z","title":"Serial position effects of large language models","venue":null,"work_id":"ba745f25-434c-45e4-b9ec-9382a898609c","year":2025},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:6b84e69847d851fb0c4a53da5b7cb8bcddd3be92ee51273cefd999ae608f24b2","observation_id":"5ec052e8-682c-46ed-a97e-02913556d7f2","resolution":{"observed_at":"2026-07-09T02:55:53.840929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.833425Z","title":"Large language models are zero-shot rankers for recommender systems","venue":null,"work_id":"99ef51c3-8762-4356-871e-b2cb9cf463fa","year":2024},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:3ebde73ca2855576b2368a1bff5ec80bcd5162625a0d82ff6bc645e7102c1185","observation_id":"728d0568-2d93-421b-a4de-01ed6c854ef5","resolution":{"observed_at":"2026-07-09T02:55:53.834878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.837570Z","title":"Fourier position embedding: Enhancing attention’s periodic extension for length generalization","venue":null,"work_id":"11a6bd53-3a6e-4474-ba79-662bfb6d450e","year":2025},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:cba4b3dfa0b135e75aaae1e0cc6b8746d27822cafbab00741ddf50945ca4b8e4","observation_id":"25934c14-3821-4bbf-bade-07ff0e251fd3","resolution":{"observed_at":"2026-07-09T02:55:53.839036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.851137Z","title":"Massive values in self-attention modules are the key to contextual knowledge understanding","venue":null,"work_id":"34d18265-bc14-4893-bbe0-a77e261a1e51","year":2025},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:cf5cd6d0439d0290e7f2b65697d80d64476bae66d9dff11fd9296a56706501cf","observation_id":"ace7169e-fb47-49d3-a124-20a099e71fc9","resolution":{"observed_at":"2026-07-09T02:55:53.852489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.853287Z","title":"nanochat: The best chatgpt that $100 can buy, 2025","venue":null,"work_id":"6e480fb4-ced3-4d79-8573-7886321175d8","year":2025},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:13e1d2daa91f4a823b74bf386febc2304ea33858a637445fe6076852d6098e47","observation_id":"638eb450-fd9a-4808-a077-c2924b0cd87b","resolution":{"observed_at":"2026-07-09T02:55:53.854554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-10T20:07:33.954106Z","title":"The impact of positional encoding on length generalization in transformers.Advances in Neural Information Processing Systems, 36:24892–24928","venue":null,"work_id":"e18f7741-ff47-4eae-92d6-9748c9cc37f9","year":2023},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:b968fdd12a4323f4f0554ed6ee148d4074f5a245d017e74847d8f6e70478d5c5","observation_id":"77ad794b-56ea-4d7d-9f77-dcfce9301e61","resolution":{"observed_at":"2026-07-09T02:55:53.883743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.834701Z","title":null,"venue":null,"work_id":"350ca4a2-47a2-4501-aabf-3f751b1dc81e","year":2017},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:b909c52491a9ad02fdcf63c67604eb2bd53bcbf5152a5838fffcdc3fa83a13e8","observation_id":"e227fe76-e5bf-46a5-acf9-8619a88bdcce","resolution":{"observed_at":"2026-07-09T02:55:53.835870Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.880266Z","title":"Mutual information functions of natural language texts","venue":null,"work_id":"0b7a326c-84f3-4a29-8437-e1264ba219e2","year":1989},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:9aad461d3d7855c1e4ed000943adf67e4d3e03ea378167759520ca4cd08eafd1","observation_id":"23e0c57e-3b2a-41a8-b1fb-ace95c02c96b","resolution":{"observed_at":"2026-07-09T02:55:53.881592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.841495Z","title":"Lost in the middle: How language models use long contexts.Transactions of the Association for Computational Linguistics, 2024","venue":null,"work_id":"da73d620-0a14-4d14-8a09-e3ba074852f4","year":2024},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:630063e8c2dee2b8b84efb3d88df3824657aa1ac392350110257dfa2dc6991c1","observation_id":"857c26ca-5e7d-4140-8c44-9d094a04b7d2","resolution":{"observed_at":"2026-07-09T02:55:53.842923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.859051Z","title":"Decoupled weight decay regularization","venue":null,"work_id":"d59937f2-7a06-4aea-8ba5-ec0385690fbf","year":2019},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:72cf833306319519d76f9ee2e0f1a4612beca69c394dffca844d38f1371c6664","observation_id":"39746906-d9dd-4804-b07d-49803647a185","resolution":{"observed_at":"2026-07-09T02:55:53.860391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.898750Z","title":"Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity","venue":null,"work_id":"3b3844fc-3ebc-497e-a2eb-2de8a8319b0f","year":2022},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:b723d7a5d573039777d27bf6615cd98152cc1b237785eced3c8597283e298db9","observation_id":"db667a91-1eed-4dd4-8553-6631b722052a","resolution":{"observed_at":"2026-07-09T02:55:53.900093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.878402Z","title":"Base of rope bounds context length, 2024","venue":null,"work_id":"84e2d42e-09d5-47d5-bea9-e89527cd5f71","year":2024},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:cb06f4a9adceb596719d1c11cbfa7f6c1d9fc793788247cff37005100412d243","observation_id":"77336fae-c8fc-4f5f-b32e-6f2089c1964c","resolution":{"observed_at":"2026-07-09T02:55:53.879676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.868892Z","title":"Note on the bias of information estimates.Information theory in psychology: Problems and methods, 1955","venue":null,"work_id":"6c708a78-f900-4f0d-ada3-a3b6b8ab670c","year":1955},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:fbf9e56ba61a09b0136bc6f273a2dbbaccc50598913e573cf8b7ea5e97e74497","observation_id":"3bfab21b-c9c0-4a61-a6f0-62e1913da9ab","resolution":{"observed_at":"2026-07-09T02:55:53.870307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.862881Z","title":"Rethinking the role of demonstrations: What makes in-context learning work? InEMNLP, 2022","venue":null,"work_id":"33f2565b-ec5c-4cd0-9181-b36a124264c3","year":2022},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:077bbdb5cf89a38968d2d7510ef33c3670ae524ae2cadc49887c961c7a19522b","observation_id":"17271e25-aa14-4e9f-8ed3-e09180f598ee","resolution":{"observed_at":"2026-07-09T02:55:53.864263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.888089Z","title":"Mitchell","venue":null,"work_id":"56d28766-e767-40c4-b588-85af163f31c2","year":1980},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:4c27f0481df13d7f66a0ab6f7ed3553d432d03e644dbf810133f298b18c87888","observation_id":"c401083d-0136-4980-9599-db737b126952","resolution":{"observed_at":"2026-07-09T02:55:53.889207Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.864855Z","title":"Frequency bands in roPE: Base frequency and context length shape the interpolation–extrapolation trade-off","venue":null,"work_id":"cbd30ce3-a8cb-4a0a-a22b-0d2f2c686855","year":2026},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:fdbce2b90e0a0fbdbc77ca669a12d4b9e0be625d8f11d18d8eb61348d385e3fb","observation_id":"8e626207-c5b8-4f20-ab68-ecde5be4822a","resolution":{"observed_at":"2026-07-09T02:55:53.866397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.11895","last_updated":"2022-09-24T00:43:19Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-09-24T00:43:19Z","title":"In-context Learning and Induction Heads","version":1},"cited_work":{"arxiv_id":"2209.11895","doi":"10.1145/3411763.3451760","metadata_source":"pith","pith_arxiv_id":"2209.11895","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"In-context Learning and Induction Heads","venue":"cs.LG","work_id":"db2b0911-2758-4a2a-99dc-15b14b91bd5e","year":2022},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"cited_paper":"/paper/2209.11895","citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:2765ee12c4ad0b5639639adde17b774efabf639ca743ad453f64bc1ea48b57dc","observation_id":"780a16b0-ecb2-4ff2-aa74-996b8603f274","resolution":{"observed_at":"2026-07-09T02:55:53.510704Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T02:23:10.795052+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T02:23:10.795052+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.866973Z","title":"Yarn: Efficient context window extension of large language models","venue":null,"work_id":"71fb3e79-d36e-4d55-9fe5-d577349c96c4","year":2024},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:14a7bf54aaa8812b51ca924709f888490fa74dbe06cac2d7918bbc228615de04","observation_id":"6290b305-bd55-4ba2-9b21-8af38392101e","resolution":{"observed_at":"2026-07-09T02:55:53.868302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.829327Z","title":"The mechanistic basis of data dependence and abrupt learning in an in-context classification task","venue":null,"work_id":"19ac3191-1c70-4af2-8ba1-43dea7178a03","year":2024},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:31759c4a8cc1f371711a8b941567af2277685252b05293a951a42fb336d0d86b","observation_id":"e0125542-9c0d-40c7-bde6-5ddda5db775d","resolution":{"observed_at":"2026-07-09T02:55:53.830788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.876408Z","title":"Roformer: Enhanced transformer with rotary position embedding","venue":null,"work_id":"23ccdf05-e869-47c1-9433-5fd71f208186","year":2023},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:e97de5db196213f27520772940ae285766f1cac2f43b23a00843ca40086ab3b5","observation_id":"91a0ecef-7c38-4d91-972a-6051c2c9b561","resolution":{"observed_at":"2026-07-09T02:55:53.877800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.872882Z","title":"Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T","venue":null,"work_id":"f3cf5a91-b797-4150-abb5-ddaecc22b458","year":2020},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:0e992073de2c0b8cf1edb30bd186d80aefd7aa8a19887f1b89f4b13828548a92","observation_id":"e200f095-8e72-49de-bc06-c7ddd1d4131a","resolution":{"observed_at":"2026-07-09T02:55:53.874127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.827539Z","title":"Hashimoto","venue":null,"work_id":"35104f7e-ffc3-488e-b66a-a4d3d230e657","year":2023},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:87f055706379391c1057c5baf319b4b890b52e0f7555b05fa00345d6bb012b19","observation_id":"0736f869-6dc4-4a67-91a8-b90208155d68","resolution":{"observed_at":"2026-07-09T02:55:53.828734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.870959Z","title":"Qwen2.5: A party of foundation models, September 2024","venue":null,"work_id":"69d50b7a-8945-41ea-b24b-3dec8e57f9fe","year":2024},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:73f4c0e21e2a978a0cf2228fbb2b82fcb55bc59dffb0bb6ad40fb33a1666f3f4","observation_id":"9c32bdf4-720b-4310-8de1-84a059ce7b72","resolution":{"observed_at":"2026-07-09T02:55:53.872324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":"2302.13971","doi":"10.48550/arxiv.2302.13971","metadata_source":"pith","pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"LLaMA: Open and Efficient Foundation Language Models","venue":"cs.CL","work_id":"c018fc23-6f3f-4035-9d02-28a2173b2b9d","year":2023},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:2803a70d00dd9f1ea0ec89f58f36832668e0a92bdfad0cd0932fb9527a5f3559","observation_id":"21bfe6b3-c7f1-45a9-a4c9-7b154b3b257d","resolution":{"observed_at":"2026-07-09T02:55:53.517990Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-08T16:08:17.350515+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-08T16:08:17.350515+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":"2307.09288","doi":"10.24963/ijcai.2025/706","metadata_source":"pith","pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","venue":"cs.CL","work_id":"68a5177f-d644-44c1-bd4f-4e5278c22f5d","year":2023},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:a322cc52058ecc48d94e47bd9f7839d7b23f543d8e5d551256a610a5225acc28","observation_id":"bfadd9d9-c3e1-4886-97dc-fa7d42b95913","resolution":{"observed_at":"2026-07-09T02:55:53.523024Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.874664Z","title":"Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N","venue":null,"work_id":"efd25bea-5601-461f-aabd-38b9300a8519","year":2017},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:347cac8ddc914c648edf298143fdcadc1d41970cd5fc96f467645160867e1fa1","observation_id":"2574888a-590e-444c-b904-b02ce971fbeb","resolution":{"observed_at":"2026-07-09T02:55:53.875850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.889846Z","title":"Kakade, Hao Peng, and Heng Ji","venue":null,"work_id":"adaf7999-7f4d-4fbb-8e61-de7df0472271","year":2025},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:0006d20aa8afe51f291ee2d467dbd4a936bd12d7064601afea16661a8326b35e","observation_id":"b5514cb7-9d29-4b4a-b2de-397ce211dfba","resolution":{"observed_at":"2026-07-09T02:55:53.891012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.857210Z","title":null,"venue":null,"work_id":"5fa79248-cf17-4299-928e-005939a29ba4","year":2020},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:c873f0c551d177693324bf5baa66a19d9b07b435003e7b7c39377fc5cb8dcba8","observation_id":"a300dc53-d92f-4f2e-a137-b76537a25550","resolution":{"observed_at":"2026-07-09T02:55:53.858476Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.860922Z","title":"On the role of attention masks and layernorm in transformers","venue":null,"work_id":"0e4262dd-bd6c-4102-8199-cba96058fe19","year":2024},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:6d5e0bab3b180208cd36c256bcf0e5536593df694731ff512109833510646a4b","observation_id":"53e2db8a-040b-402a-b8e8-499e3f8ff9b5","resolution":{"observed_at":"2026-07-09T02:55:53.862252Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.893419Z","title":"On the emergence of position bias in transformers","venue":null,"work_id":"4b77607a-d50f-487c-b7f9-6252fb9d59f0","year":2025},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:f279045822e2f880cb5c6b152b8803f22a97554728076301bf73633d43d39095","observation_id":"1a3f7d3d-68b2-4bcd-a516-6d78c2c04165","resolution":{"observed_at":"2026-07-09T02:55:53.894668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.838612Z","title":"Efficient streaming language models with attention sinks","venue":null,"work_id":"da16d36a-7e50-4dad-868c-e15146ec79e0","year":2024},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:56e1ff5f9c83bdb0f22dfdd4ac01e773b263bc1d6660b0ed8b47b4b7f0232661","observation_id":"81277e6e-bdb1-44e9-a6a3-cd7a0993c5e3","resolution":{"observed_at":"2026-07-09T02:55:53.839852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.20311","last_updated":"2024-07-29T17:52:40Z","snapshot_observed_at":"2026-07-06T18:53:35.627045Z","submitted_at":"2024-07-29T17:52:40Z","title":"Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process","version":1},"cited_work":{"arxiv_id":"2407.20311","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.20311","snapshot_observed_at":"2026-07-09T02:55:53.512837Z","title":"Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process","venue":"cs.AI","work_id":"2c6517ce-d4db-4a10-ab3a-c160de6ea4ab","year":2024},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"cited_paper":"/paper/2407.20311","citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:8dd772803ea1c5c803998aa4a9427e7705af85fed073a0f41611056d60aab1b6","observation_id":"e22f475f-a47b-4caa-b7fa-645bad8cc22d","resolution":{"observed_at":"2026-07-09T02:55:53.514975Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.847476Z","title":"Reddi, and Sanjiv Kumar","venue":null,"work_id":"1d82f697-ace7-4021-8d4b-f453ff0466e8","year":2020},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:c845c0b5cc43c51aefe3ef68d282917566a1f7cb3717a0d3546783cc37ac3de7","observation_id":"38e1eb26-8f65-4542-a5da-16adccc536ac","resolution":{"observed_at":"2026-07-09T02:55:53.848655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.777953Z","title":"Found in the middle: How language models use long contexts better via plug-and-play positional encoding, 2024","venue":null,"work_id":"f8fca98a-d978-4652-8e34-8e0a91efce03","year":2024},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:c82d56e8c617dc16b4f437e3d9034a6097ebae0d263b98c8f98e8cea1187975e","observation_id":"3ca05476-8c3f-469d-8cf2-615542bc6cc8","resolution":{"observed_at":"2026-07-09T02:55:53.779142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.895320Z","title":"Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh","venue":null,"work_id":"a5e4963f-e75d-4fd1-be65-7779bcd66a8c","year":2021},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:5b195899e43f28ae499b124b9b3dd371298c6665867f193f54a98e35d06219e0","observation_id":"50d61d94-f5bd-4018-9844-a1beaa0b7eea","resolution":{"observed_at":"2026-07-09T02:55:53.897922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.835514Z","title":"Xing, Haotong Zhang, Joseph E","venue":null,"work_id":"77070b0c-9471-4d06-b494-88a93fea8190","year":2023},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:5f705a3a36f4e4c04e92854daf818e35af9c2624ee2ddeb3703190e2503272fe","observation_id":"bc3321f1-1aeb-477d-885b-acf01a44c0e6","resolution":{"observed_at":"2026-07-09T02:55:53.836812Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-09T02:55:53.783335Z","title":"[43] show the multi-layer effects of masks and positional encodings [43]","venue":null,"work_id":"1d600b23-e6b0-4244-8708-84f6a02898b7","year":2048},"citing_paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-09T02:52:28.852922Z"},"links":{"citing_paper":"/paper/2607.07678"},"observation_digest":"sha256:e683eef709b6a5220eee47cae6ae3b12e2b1c381dfd90f000f30d095bb795b6a","observation_id":"c119a7cc-620f-4da8-a434-21d4cd5b6195","resolution":{"observed_at":"2026-07-09T02:55:53.820066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2607.07678","last_updated":"2026-07-08T17:38:14Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-01T03:59:24.875859Z","submitted_at":"2026-07-08T17:38:14Z","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":2,"verified_exact":7,"verified_fuzzy":39},"total_outbound_references":50},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2607.07678."}