{"as_of":"2026-08-09T06:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f46945b080f08d6c931c32cab3cec0fb1c8ed9cc26bd328ca91ceb745fbb0dff","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:14:06.430966Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-18T18:24:14.848351Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-18T18:26:43.754522Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"cited_work":{"arxiv_id":"2507.08992","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.08992","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Semantic source code segmentation using small and large language models","venue":null,"work_id":"09f7adf5-50a9-4012-9358-142d665f2c0f","year":2025},"citing_paper":{"arxiv_id":"2509.09192","last_updated":"2026-04-02T18:43:42Z","snapshot_observed_at":"2026-07-06T22:28:38.311664Z","submitted_at":"2025-09-11T07:07:11Z","title":"ReDef: Do Code Language Models Truly Understand Code Changes for Just-in-Time Software Defect Prediction?","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-18T18:24:14.848351Z"},"links":{"cited_paper":"/paper/2507.08992","citing_paper":"/paper/2509.09192"},"observation_digest":"sha256:411b3b4b22beb3ef0be048c0bd3702cb67da56e97f972756d3995627705e82d6","observation_id":"a11549c5-5733-4f7e-9e45-71aebebbecb7","resolution":{"observed_at":"2026-05-18T18:26:43.757215Z","resolver_source":"arxiv_id","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"}}],"links":{"evidence":"/evidence","html":"/paper/2507.08992/citation-record","integrity":"/paper/2507.08992/integrity","json":"/paper/2507.08992/citation-record.json","paper":"/paper/2507.08992"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-06T18:13:05.182319Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:05.182319Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:c4b79be6816c7703f31762535274fb02abea2ddad951e204ccdec3e5b865aa06","observation_id":"09e7a8ff-09eb-4a86-b91b-d71589d4abad","resolution":{"observed_at":"2026-08-06T18:13:05.182319Z","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":"2026-08-06T18:14:07.231892Z","title":"Monitor-guided decoding of code lms with static analysis of repository context","venue":null,"work_id":"9428cdee-3e31-4062-b356-6f87ddff6786","year":2024},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:05.280709Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:7633e104b8b59b929648f00e23b87b2a70cdf212c489d10933a8c853ed011980","observation_id":"5719370e-5cb1-4101-8a1d-b4b47eb5c381","resolution":{"observed_at":"2026-08-06T18:14:07.237557Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:13:05.351496Z","title":"The claude 3 model family: Opus, sonnet, haiku","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:05.351496Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:65516c20b723643eefa2dafa2f6c1653faac7063ae27817eb002bbf48825ced1","observation_id":"47fd636d-a19f-4f9b-9245-3daf2fa244a3","resolution":{"observed_at":"2026-08-06T18:13:05.351496Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.13282","last_updated":"2025-01-23T00:17:48Z","snapshot_observed_at":"2026-08-04T14:29:09.653355Z","submitted_at":"2025-01-23T00:17:48Z","title":"Experience with GitHub Copilot for Developer Productivity at Zoominfo","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.13282","snapshot_observed_at":"2026-08-06T18:13:05.433827Z","title":"Experience with github copilot for developer productivity at zoominfo","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:05.433827Z"},"links":{"cited_paper":"/paper/2501.13282","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:0dbe071d0e9791b0a1fa11adeb5cc1cae2d1c37dfe84c04f67833cf4c07ca13c","observation_id":"b77ce6ce-952f-488b-8fff-afac19e35d8e","resolution":{"observed_at":"2026-08-06T18:13:05.433827Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.11055","last_updated":"2020-05-22T08:29:06Z","snapshot_observed_at":"2026-08-07T22:56:53.839124Z","submitted_at":"2020-05-22T08:29:06Z","title":"Improving Segmentation for Technical Support Problems","version":1},"cited_work":{"arxiv_id":"2005.11055","doi":null,"metadata_source":"pith","pith_arxiv_id":"2005.11055","snapshot_observed_at":"2026-08-06T18:14:06.889780Z","title":"Improving Segmentation for Technical Support Problems","venue":"cs.CL","work_id":"caf3673a-0f6f-4445-a692-0d476e27d195","year":2020},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:05.534077Z"},"links":{"cited_paper":"/paper/2005.11055","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:4f4ad1e6dfc7716abdf776352808fdcbda82a1b3dc9db6068826f04735a63c83","observation_id":"f681f682-2876-4242-98f0-cce72ee09cda","resolution":{"observed_at":"2026-08-06T18:14:06.896220Z","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":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-08-08T11:58:24.516369Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-06T18:13:05.637926Z","title":"Evaluating large language models trained on code","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:05.637926Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:0a3c0ed862544179185a10fd3285c6fbd22d26dafbabc3250a447fcc66443814","observation_id":"7d344381-90d3-47cb-a53b-1997c2d5306f","resolution":{"observed_at":"2026-08-06T18:13:05.637926Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"cs/0003083","last_updated":"2000-03-30T16:56:02Z","snapshot_observed_at":"2026-07-07T03:03:36.688330Z","submitted_at":"2000-03-30T16:56:02Z","title":"Advances in domain independent linear text segmentation","version":1},"cited_work":{"arxiv_id":"cs/0003083","doi":null,"metadata_source":"pith","pith_arxiv_id":"cs/0003083","snapshot_observed_at":"2026-08-06T18:14:06.847491Z","title":"Advances in domain independent linear text segmentation","venue":"cs.CL","work_id":"c6f43040-878b-44b4-9085-dc8c914dd58d","year":2000},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:05.706019Z"},"links":{"cited_paper":"/paper/cs/0003083","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:efa0546c2fc7024f106bce67490a1ae014df5305d1502f55979a9ac919f6061d","observation_id":"9f62be24-d7e4-44c3-8127-3ea61553da72","resolution":{"observed_at":"2026-08-06T18:14:06.853162Z","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":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-06T18:13:05.781656Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:05.781656Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:e8bff5e8ee1b994ae4ce1d36fac08f24c47c53baa3f211f4094d37d1dcf5d031","observation_id":"6a0a3d9d-4c04-4352-87be-812b6f3b9494","resolution":{"observed_at":"2026-08-06T18:13:05.781656Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.09707","last_updated":"2023-11-16T09:35:00Z","snapshot_observed_at":"2026-07-06T16:48:29.239105Z","submitted_at":"2023-11-16T09:35:00Z","title":"GenCodeSearchNet: A Benchmark Test Suite for Evaluating Generalization in Programming Language Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.09707","snapshot_observed_at":"2026-08-06T18:13:05.849772Z","title":"Gencodesearchnet: A benchmark test suite for evaluating generalization in programming language understanding","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:05.849772Z"},"links":{"cited_paper":"/paper/2311.09707","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:93766c659e3afe8eb7c0b3de8984d371afaa5315aca78b2927e55f23c06c6d76","observation_id":"93da85aa-9701-4565-be52-baa7871d0497","resolution":{"observed_at":"2026-08-06T18:13:05.849772Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.08615","last_updated":"2019-07-18T18:23:26Z","snapshot_observed_at":"2026-07-06T08:08:56.355510Z","submitted_at":"2019-07-18T18:23:26Z","title":"Logical Segmentation of Source Code","version":1},"cited_work":{"arxiv_id":"1907.08615","doi":null,"metadata_source":"pith","pith_arxiv_id":"1907.08615","snapshot_observed_at":"2026-08-06T18:14:06.786871Z","title":"Logical Segmentation of Source Code","venue":"cs.SE","work_id":"115746db-6924-4d80-9673-d591fe0daa1d","year":2019},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:05.919590Z"},"links":{"cited_paper":"/paper/1907.08615","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:d34d7c5ca358eba057841eb1a71532617d875970a1a1410529b4b6e344ee4702","observation_id":"9982aa18-6f5e-4434-9f6e-3631ae910107","resolution":{"observed_at":"2026-08-06T18:14:06.792889Z","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":"2406.17526","last_updated":"2024-06-25T13:08:35Z","snapshot_observed_at":"2026-07-06T18:36:42.830927Z","submitted_at":"2024-06-25T13:08:35Z","title":"LumberChunker: Long-Form Narrative Document Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.17526","snapshot_observed_at":"2026-08-06T18:13:05.999683Z","title":"Lumberchunker: Long-form narrative document segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:05.999683Z"},"links":{"cited_paper":"/paper/2406.17526","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:11d935d2105c9014c20534456cb57103a3f1fad7121e96e2cf8924510694b465","observation_id":"60530762-2a9c-476b-8542-5c47402c88cb","resolution":{"observed_at":"2026-08-06T18:13:05.999683Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08155","last_updated":"2020-09-18T15:38:12Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-02-19T13:09:07Z","title":"CodeBERT: A Pre-Trained Model for Programming and Natural Languages","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.08155","snapshot_observed_at":"2026-08-06T18:13:06.126279Z","title":"Codebert: A pre-trained model for programming and natural languages","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:06.126279Z"},"links":{"cited_paper":"/paper/2002.08155","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:5ae49a33a72da31d537f96bad1ac48644dff0672471c261e099d15507e3e7237","observation_id":"38e6c7cd-620f-44ae-86b4-592c41901aaf","resolution":{"observed_at":"2026-08-06T18:13:06.126279Z","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":"2026-08-06T18:14:07.191693Z","title":"The limits of the identifiable: Challenges in python version identification with deep learning","venue":null,"work_id":"dd155c57-a810-42d8-83fc-b6bcaa5b8126","year":2024},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:06.220863Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:58fc77ce015e8106b49f50a705c62f0a5559640353ba657a704106e6461914b3","observation_id":"8f6b7f47-6a3f-4ab7-aeda-5037c4ffe9e7","resolution":{"observed_at":"2026-08-06T18:14:07.197133Z","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":"2026-08-06T18:14:07.175394Z","title":"Topic segmentation of semi-structured and unstructured conversational datasets using language models","venue":null,"work_id":"3c214d6d-05af-443c-ad54-67e942da4f81","year":2023},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:06.288372Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:1dc09e9e24a9e54e04f9812764b3d28ad6e5cdcbc12ff102272d82e056794b06","observation_id":"8a6c2fbe-9d23-4453-b8f0-1fe3d9bafab6","resolution":{"observed_at":"2026-08-06T18:14:07.180963Z","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":"2401.14196","last_updated":"2024-01-26T09:23:11Z","snapshot_observed_at":"2026-08-06T22:40:28.707813Z","submitted_at":"2024-01-25T14:17:53Z","title":"DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14196","snapshot_observed_at":"2026-08-06T18:13:06.408039Z","title":"Deepseek-coder: When the large language model meets programming--the rise of code intelligence","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:06.408039Z"},"links":{"cited_paper":"/paper/2401.14196","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:fd9f32b601eeaab214dfb068c649ade103b00f40a6e0bd10c5255ebf21d60a9a","observation_id":"338c9c17-c485-4cd9-a1a2-3da721b00ed3","resolution":{"observed_at":"2026-08-06T18:13:06.408039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12186","last_updated":"2024-11-12T13:24:25Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-18T17:57:57Z","title":"Qwen2.5-Coder Technical Report","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12186","snapshot_observed_at":"2026-08-06T18:13:06.480669Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T18:13:06.480669Z"},"links":{"cited_paper":"/paper/2409.12186","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:0edfa715f3744a523f87b79de503fbd47f9d35614ab1aab6a35e6eea44b4efae","observation_id":"41fd656c-0d31-4d14-84cf-b553a7db0da9","resolution":{"observed_at":"2026-08-06T18:13:06.480669Z","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":"2026-08-06T18:14:07.158971Z","title":"Topic segmentation and labeling in asynchronous conversations","venue":null,"work_id":"4eed8068-bcb8-4cb2-8328-bc16012ddc9f","year":2013},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.127692Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:c417313c158fdfff8b1a0817b25376c75a38d292d34dfb35b617f5b2de88f8cf","observation_id":"816731af-b5ba-4564-b9d7-a77b4c9f4893","resolution":{"observed_at":"2026-08-06T18:14:07.163912Z","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":"1803.09337","last_updated":"2018-03-25T20:53:40Z","snapshot_observed_at":"2026-07-06T06:30:07.223267Z","submitted_at":"2018-03-25T20:53:40Z","title":"Text Segmentation as a Supervised Learning Task","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.09337","snapshot_observed_at":"2026-08-06T18:14:06.166674Z","title":"Text segmentation as a supervised learning task","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.166674Z"},"links":{"cited_paper":"/paper/1803.09337","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:c21c46221446a53da565a409fa2aea5096a4cafb4a80346d770177b5aa2c58dd","observation_id":"ff1c3f9b-44bf-472c-86a8-7a39ce3bcd9e","resolution":{"observed_at":"2026-08-06T18:14:06.166674Z","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":"2026-08-06T18:14:07.142988Z","title":"The measurement of observer agreement for categorical data","venue":null,"work_id":"351295ff-c810-47a0-a9ff-67ea6c99b902","year":1977},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.242488Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:dd6122fecb8322a53d496c4431fe03ce0b57a13048ab38511b4a181bbc0a2343","observation_id":"750001ce-a999-4303-abf3-a3de6e30c3e5","resolution":{"observed_at":"2026-08-06T18:14:07.147986Z","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":"2402.19173","last_updated":"2024-02-29T13:53:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-29T13:53:35Z","title":"StarCoder 2 and The Stack v2: The Next Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.19173","snapshot_observed_at":"2026-08-06T18:14:06.257855Z","title":"Starcoder 2 and the stack v2: The next generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.257855Z"},"links":{"cited_paper":"/paper/2402.19173","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:53d09a3b27570b0eb6088598c2465875b0152df38353aec08a4e659955fd44d1","observation_id":"b03177cc-6cc8-4886-8257-fd83b128e5ed","resolution":{"observed_at":"2026-08-06T18:14:06.257855Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.14535","last_updated":"2020-12-07T16:00:42Z","snapshot_observed_at":"2026-08-08T22:01:42.715664Z","submitted_at":"2020-04-30T01:36:52Z","title":"Text Segmentation by Cross Segment Attention","version":2},"cited_work":{"arxiv_id":"2004.14535","doi":null,"metadata_source":"pith","pith_arxiv_id":"2004.14535","snapshot_observed_at":"2026-08-06T18:14:06.648576Z","title":"Text Segmentation by Cross Segment Attention","venue":"cs.CL","work_id":"d987a889-f58a-4b61-9345-8da86b7e0be8","year":2020},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.264775Z"},"links":{"cited_paper":"/paper/2004.14535","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:5ef8b509eca2a63b159039f31ce20fcbfdbc632baa236979612d02daed36436f","observation_id":"5b114f65-c8b6-44fd-8faf-bf13eb0c2acc","resolution":{"observed_at":"2026-08-06T18:14:06.656030Z","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":"2305.12138","last_updated":"2026-05-20T13:07:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-20T08:43:49Z","title":"Exploring Code Analysis: Zero-Shot Insights on Syntax and Semantics with LLMs","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.12138","snapshot_observed_at":"2026-08-06T18:14:06.271650Z","title":"Lms: Understanding code syntax and semantics for code analysis","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.271650Z"},"links":{"cited_paper":"/paper/2305.12138","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:7b3cc5f35cfc810fa21c7d8ccf265be5d4b48fea89d138a0aca69d4e3bf802de","observation_id":"64cda335-2d9e-4266-b79c-8d2d921c8338","resolution":{"observed_at":"2026-08-06T18:14:06.271650Z","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":"2026-08-06T18:14:07.123635Z","title":"Evaluating ai-based code segmentation for abap programs in an industrial use case","venue":null,"work_id":"e848af27-f83e-4d5d-9447-1674b5f1e615","year":2024},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.277737Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:f37dd97261004e944dda50fb014b2ac6606a6a010ed2794841999aeb5b1db0d4","observation_id":"00a13d50-1815-45b6-82c6-0335e3099149","resolution":{"observed_at":"2026-08-06T18:14:07.128464Z","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":"2026-08-06T18:14:07.107391Z","title":"Beamseg: A joint model for multi-document segmentation and topic identification","venue":null,"work_id":"1c8c73fb-e207-4854-ba86-460db66cc296","year":2019},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.284479Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:e02b3f8e37997a8513b43a20eaa933a5225ae6847d5ed71c104273802372bd36","observation_id":"19c379a7-d2f1-47df-8d60-8a847da3f1cb","resolution":{"observed_at":"2026-08-06T18:14:07.112755Z","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":"2305.02309","last_updated":"2023-07-11T21:11:23Z","snapshot_observed_at":"2026-07-06T15:22:55.122322Z","submitted_at":"2023-05-03T17:55:25Z","title":"CodeGen2: Lessons for Training LLMs on Programming and Natural Languages","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.02309","snapshot_observed_at":"2026-08-06T18:14:06.296411Z","title":"Codegen2: Lessons for training llms on programming and natural languages","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.296411Z"},"links":{"cited_paper":"/paper/2305.02309","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:9ec19c5c1cc17b317509e7f8ee189bc8791aca52963c4abd6ad7eac9681eac21","observation_id":"db4aa14f-a804-4390-b5cb-a432b82360ed","resolution":{"observed_at":"2026-08-06T18:14:06.296411Z","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":"2026-08-06T18:14:07.088357Z","title":"Automated support for legacy code understanding","venue":null,"work_id":"df6bba17-d942-4edc-bd3b-1d5586f165f9","year":1994},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.323479Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:698cc1d6c76b2e508bf9329eecc6caa7ab62eb6f10119f4582fd5cabd68fd04e","observation_id":"ec221f42-1d6e-4195-932f-3858574aecde","resolution":{"observed_at":"2026-08-06T18:14:07.093788Z","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":"2308.10464","last_updated":"2023-08-21T04:42:24Z","snapshot_observed_at":"2026-07-06T16:08:21.060375Z","submitted_at":"2023-08-21T04:42:24Z","title":"Unsupervised Dialogue Topic Segmentation in Hyperdimensional Space","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.10464","snapshot_observed_at":"2026-08-06T18:14:06.329912Z","title":"Unsupervised dialogue topic segmentation in hyperdimensional space","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.329912Z"},"links":{"cited_paper":"/paper/2308.10464","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:ce4a8af2437f6242fcf49c0d3eae5ac7ce5a55a80e044d93207afe742fbdee90","observation_id":"44efb3c7-5187-4871-8314-6c8a6e217047","resolution":{"observed_at":"2026-08-06T18:14:06.329912Z","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":"2026-08-06T18:14:07.068429Z","title":"Topictiling: a text segmentation algorithm based on lda","venue":null,"work_id":"fd732a88-bb80-486d-ace1-8b0985ed4c45","year":2012},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.337992Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:072f1c2fbcfe60dc2e3a07891b86555ca3027b07e2d5ac6bf6b23705e393f7ef","observation_id":"281a68cb-8419-486f-a3ea-cd5d1008d3da","resolution":{"observed_at":"2026-08-06T18:14:07.074454Z","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":"2308.12950","last_updated":"2024-01-31T19:47:26Z","snapshot_observed_at":"2026-07-06T16:10:07.931347Z","submitted_at":"2023-08-24T17:39:13Z","title":"Code Llama: Open Foundation Models for Code","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.12950","snapshot_observed_at":"2026-08-06T18:14:06.343323Z","title":"Code llama: Open foundation models for code","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.343323Z"},"links":{"cited_paper":"/paper/2308.12950","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:e677cfb792d46ac92575eddb02088830c2daff10fc7bf30bb2fc6ee8bc3bebf1","observation_id":"4460500b-3e5a-4c88-9cee-8eca6994d519","resolution":{"observed_at":"2026-08-06T18:14:06.343323Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.12978","last_updated":"2021-06-24T12:54:43Z","snapshot_observed_at":"2026-08-02T22:54:03.351342Z","submitted_at":"2021-06-24T12:54:43Z","title":"Unsupervised Topic Segmentation of Meetings with BERT Embeddings","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.12978","snapshot_observed_at":"2026-08-06T18:14:06.349554Z","title":"Unsupervised topic segmentation of meetings with bert embeddings","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.349554Z"},"links":{"cited_paper":"/paper/2106.12978","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:01a46ae549a8a022e7fa9fdebea986886096c7f527c8b2d942c133be18dbebac","observation_id":"3de14e9b-3e7a-4019-b5d9-9aa5fbcb355b","resolution":{"observed_at":"2026-08-06T18:14:06.349554Z","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":"2026-08-06T18:14:07.052471Z","title":"Chaos to clarity with semantic inferencing for python source code snippets","venue":null,"work_id":"c74278f9-324a-4ac7-981b-71b0c427617f","year":2023},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.355212Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:295a6b583833b0ca6431ff9ed27ca92cec4cb289ce7fea7856a37d671ea0a1f0","observation_id":"2188a8eb-b8b7-4f7f-b0dc-3b1d103f4f19","resolution":{"observed_at":"2026-08-06T18:14:07.057429Z","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":"2026-08-06T18:14:07.027886Z","title":"Linguistic approach to segmenting source code","venue":null,"work_id":"cca90b1e-0bf8-4839-b75a-0718e33b36e7","year":2022},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.359982Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:1b790885c161e09702a5217a439dec87e44854ee7759aa1d3a78eb5c64d4fef0","observation_id":"cbf3f1ee-b036-42ce-b29c-62457aaec833","resolution":{"observed_at":"2026-08-06T18:14:07.034546Z","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":"2305.08377","last_updated":"2023-10-09T15:52:30Z","snapshot_observed_at":"2026-08-04T12:11:05.713517Z","submitted_at":"2023-05-15T06:24:45Z","title":"Text Classification via Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.08377","snapshot_observed_at":"2026-08-06T18:14:06.368007Z","title":"Text classification via large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.368007Z"},"links":{"cited_paper":"/paper/2305.08377","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:ee73ce6ed7fe290adb7dcb375affe76f2d0b4391ec70a368a732ceed8713a518","observation_id":"45b2d580-965c-44f0-834c-c603bc2f61b0","resolution":{"observed_at":"2026-08-06T18:14:06.368007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11805","last_updated":"2025-05-09T21:04:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-19T02:39:27Z","title":"Gemini: A Family of Highly Capable Multimodal Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.11805","snapshot_observed_at":"2026-08-06T18:14:06.374612Z","title":"Gemini: a family of highly capable multimodal models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.374612Z"},"links":{"cited_paper":"/paper/2312.11805","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:9b9c27f83ff7ec338c739015c4924bcb4f0363b94a51dbb9c2430f30fcf46fd1","observation_id":"34ecd070-83dc-4f30-aa50-6c063fae7f68","resolution":{"observed_at":"2026-08-06T18:14:06.374612Z","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":"2026-08-06T18:14:07.010568Z","title":"Automatic segmentation of method code into meaningful blocks to improve readability","venue":null,"work_id":"5b8df685-82af-4396-a789-b19231f05c6b","year":2011},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.379437Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:004121a33dac04f033ae49068caf1aef27fd8c63a474ea8ed966b14a96b8011a","observation_id":"19806ad2-d762-4ed0-9838-1e3f8ff1f20b","resolution":{"observed_at":"2026-08-06T18:14:07.015842Z","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":"2305.07922","last_updated":"2023-05-20T07:27:15Z","snapshot_observed_at":"2026-08-08T01:28:05.271964Z","submitted_at":"2023-05-13T14:23:07Z","title":"CodeT5+: Open Code Large Language Models for Code Understanding and Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.07922","snapshot_observed_at":"2026-08-06T18:14:06.388934Z","title":"Codet5+: Open code large language models for code understanding and generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.388934Z"},"links":{"cited_paper":"/paper/2305.07922","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:1c8df86e80625e0bc1d37d39821f1009ad1b84cfbd6275db54f3200d9e30ea32","observation_id":"1a7c71e1-4a5b-41e7-9338-149820dd4c53","resolution":{"observed_at":"2026-08-06T18:14:06.388934Z","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":"2026-08-06T18:14:06.993074Z","title":"Coral: Code representation learning with weakly-supervised transformers for analyzing data analysis","venue":null,"work_id":"e23827d2-b913-4834-a0b7-f42f127b95d8","year":2022},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.398901Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:a435f7738cdb86cccb09232d60ff49b0eac4192f3179874985dd1fa027df4eaf","observation_id":"a1ca5300-8a9c-4980-bd76-bf664ce9b666","resolution":{"observed_at":"2026-08-06T18:14:06.998957Z","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":"2410.12788","last_updated":"2025-05-21T15:45:06Z","snapshot_observed_at":"2026-08-04T22:31:07.005062Z","submitted_at":"2024-10-16T17:59:32Z","title":"Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical Perception","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.12788","snapshot_observed_at":"2026-08-06T18:14:06.404839Z","title":"Meta-chunking: Learning efficient text segmentation via logical perception","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.404839Z"},"links":{"cited_paper":"/paper/2410.12788","citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:0efd57344b31f37d169949c3f234426cd13e6e27ad02054975ef31e89ccb6774","observation_id":"2cdf6b6c-1bb7-43a1-9b65-cc71cf0ce349","resolution":{"observed_at":"2026-08-06T18:14:06.404839Z","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":"2026-08-06T18:14:06.410607Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.410607Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:80001dc7086d0ec1e43f9e05bbc36eff946961c1d2466c70ae262be58d9d2af3","observation_id":"324b123f-c5c9-4750-b15c-607fd731f963","resolution":{"observed_at":"2026-08-06T18:14:06.410607Z","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":"2026-08-06T18:14:06.416782Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.416782Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:d123dc7b27c4f253e93673eeb2a91ca989b3eaf0cb742b7470b89a0bfe268239","observation_id":"0189ae8c-8df2-4df3-9598-e7f34c913c89","resolution":{"observed_at":"2026-08-06T18:14:06.416782Z","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":"2026-08-06T18:14:06.424572Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.424572Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:d59f8bcb85e1772b7eb66df6017f47afbf25bbfbdc498c04c187be014a448adf","observation_id":"1023cd00-cd09-4871-8dee-c3645815b728","resolution":{"observed_at":"2026-08-06T18:14:06.424572Z","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":"2026-08-06T18:14:06.430966Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-06T18:14:06.430966Z"},"links":{"citing_paper":"/paper/2507.08992"},"observation_digest":"sha256:124f9b60c3c396a542c6dfa230b77e32f591f122b0f60bf9389ef61cbfb8c9d1","observation_id":"69d90398-316e-447f-ade9-e6f252e2c762","resolution":{"observed_at":"2026-08-06T18:14:06.430966Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.08992","last_updated":"2025-07-11T19:49:59Z","latest_version":1,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-09T04:53:41.985165Z","submitted_at":"2025-07-11T19:49:59Z","title":"Semantic Source Code Segmentation using Small and Large Language Models"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":25,"verified_exact":4,"verified_fuzzy":13},"total_outbound_references":42},"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 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2507.08992."}