{"as_of":"2026-08-17T14:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5d8d2d3b444e478edcbaf47042e57c11eac9b2c44a2f5121dde27c6e22750bf6","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T05:37:39.703208Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2412.00239/citation-record","integrity":"/paper/2412.00239/integrity","json":"/paper/2412.00239/citation-record.json","paper":"/paper/2412.00239"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:37:40.447070Z","title":"On the opportunities and risks of foundation models,","venue":null,"work_id":"df7a70f0-eca2-44df-ada5-e63139965f7e","year":null},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.528178Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:82a31afdf408777b133cd7ff2e806feecb97f94cd9426a6cf790a3c1ed94145a","observation_id":"67450f68-1f96-4a49-925c-b9073b461709","resolution":{"observed_at":"2026-08-12T05:37:40.450924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10688","last_updated":"2024-04-15T18:43:12Z","snapshot_observed_at":"2026-08-16T14:17:58.997853Z","submitted_at":"2024-02-16T13:46:06Z","title":"Towards Uncovering How Large Language Model Works: An Explainability Perspective","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10688","snapshot_observed_at":"2026-08-12T05:37:39.536209Z","title":"Towards uncovering how large language model works: An explainability perspective,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.536209Z"},"links":{"cited_paper":"/paper/2402.10688","citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:47cdb1abc5404b004dc16cbac08ee4220579a7e820ddec68ec478990e1d7746c","observation_id":"4dd78202-aec5-4174-bc0f-372d67e52f14","resolution":{"observed_at":"2026-08-12T05:37:39.536209Z","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":"2026-08-12T05:37:40.425110Z","title":"How does machine learning change software development practices?","venue":null,"work_id":"e3903804-ae65-4fe3-8b4d-598046577c62","year":2021},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.540749Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:7f1582be3b741c9cdb86efd8657a1ac5c054b3f47058cb158db944585bec5cfa","observation_id":"a8253f64-8d57-49e1-92ee-2188bdec2c6d","resolution":{"observed_at":"2026-08-12T05:37:40.428781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:40.414124Z","title":"Design patterns for ai-based systems: A multivocal literature review and pattern repository,","venue":null,"work_id":"abc678f0-cc3d-4316-a97b-b13686c974f1","year":2023},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.544287Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:621161bc69f30bae94cfda2025e6c5bfb5647e7cca61d61b1efef242a2e455b9","observation_id":"9a992c20-3376-4f07-87ea-b2ba6a79bb66","resolution":{"observed_at":"2026-08-12T05:37:40.418130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:40.402783Z","title":"Architectural design decisions for the machine learning workflow,","venue":null,"work_id":"57fa4ede-8088-4694-bd0a-96cadab6dd92","year":2022},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.547811Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:759e0f2aa4ea154ef7e52a7f9a681d5f2956d1a0476595334ae656b6a883a390","observation_id":"622fffec-8ae7-4067-9b6b-5e805d3883ab","resolution":{"observed_at":"2026-08-12T05:37:40.406672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:40.392465Z","title":"A taxonomy of software engineering challenges for machine learning systems: An empirical investigation,","venue":null,"work_id":"324a8a71-049f-4aa1-b7fa-3ff73d35643c","year":2019},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.551298Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:2ead8cd051f5c7a57e44c71baf4bba376c8b9a0fff5f57d674eaafb14e1227b0","observation_id":"9b76e7f2-b9d1-44a2-a297-b35595e043e4","resolution":{"observed_at":"2026-08-12T05:37:40.396284Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:39.555043Z","title":"Software engineering for machine learning: A case study,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.555043Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:d32c776e9c0e79a45620650c426e7bc81f3371fe44b4e5290c77f6ee2c1b8110","observation_id":"69b0a164-7ada-458c-b306-dcc90a4301f0","resolution":{"observed_at":"2026-08-12T05:37:39.555043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00515","last_updated":"2024-11-10T22:02:27Z","snapshot_observed_at":"2026-08-14T16:40:35.729198Z","submitted_at":"2024-06-01T17:48:15Z","title":"A Survey on Large Language Models for Code Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.00515","snapshot_observed_at":"2026-08-12T05:37:39.558824Z","title":"A survey on large language models for code generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.558824Z"},"links":{"cited_paper":"/paper/2406.00515","citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:cf679ac7ee465687d25aab5067ad051144fef66daa9d82c4ef73f004ef7ae81f","observation_id":"2e70079c-126d-47db-b1c1-8f85ca2b04db","resolution":{"observed_at":"2026-08-12T05:37:39.558824Z","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":"2026-08-12T05:37:40.375359Z","title":"Studying software engineering patterns for designing machine learning systems,","venue":null,"work_id":"edbacca4-5cb1-4401-93d9-ca9de5f853a2","year":2019},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.562579Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:26c4635d3175ecb908d9654cbdcd5f07e194264deacf727f129a44983bad07ab","observation_id":"f42da09b-23e5-47e6-8abe-0b22d89e867e","resolution":{"observed_at":"2026-08-12T05:37:40.379102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:40.365019Z","title":"Software engineering for ai-based systems: A survey,","venue":null,"work_id":"b4e726b9-24d3-4d56-9c1a-8ea5013885db","year":2022},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.565881Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:d57510188b3ce50911f25de82044e5309d6a6fc9292e0966b2b39b3e2d30fcbf","observation_id":"62305d82-21a2-4228-8885-dc03ca438c6a","resolution":{"observed_at":"2026-08-12T05:37:40.368679Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:40.354008Z","title":"Archi- tectural decisions in ai-based systems: An ontological view,","venue":null,"work_id":"7aaa446e-a782-4f7c-b39f-7332628f6f7f","year":2022},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.569593Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:25ee31d3868862ee8cf77f877c43bc41b24589e69a0a3c5afed5877b517df763","observation_id":"6c69ad4a-a8a2-4cae-9a3e-07f06cb81d41","resolution":{"observed_at":"2026-08-12T05:37:40.358212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:40.343088Z","title":"Adapting Software Architectures to Machine Learning Challenges ,","venue":null,"work_id":"60855b44-09e4-47ca-8dff-2dbe6a1478e7","year":2022},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.572703Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:4c03f01e83d3d1cfa48f978b6fdcb3cbffd87fc21c1f3338c0d2c8e324651d86","observation_id":"1daee827-ca6d-4867-99fd-6401eeb6a47e","resolution":{"observed_at":"2026-08-12T05:37:40.347125Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:40.332922Z","title":"Architecture decisions in ai-based systems development: An empirical study,","venue":null,"work_id":"cb0f0c00-1525-40a8-9393-e01a45844bf7","year":2023},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.579951Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:22d4297507961b211b818047dd2025cdcb5003e3c0bd6e6290e22a8a4d255bde","observation_id":"3301bc9e-e117-432d-b8c4-f6c13430b570","resolution":{"observed_at":"2026-08-12T05:37:40.336562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:39.583185Z","title":"Rethinking software engineering in the era of foundation models: A curated catalogue of challenges in the development of trustworthy fmware,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.583185Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:5b2f9b9483b7a32830487dcd3f006e2e320a5db96bc61b2d4e363e58eb0f4577","observation_id":"a6dc7f55-030c-4e37-bfe9-728d26edf73c","resolution":{"observed_at":"2026-08-12T05:37:39.583185Z","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":"2026-08-12T05:37:40.321875Z","title":"Requirements and reference architecture for mlops:insights from industry,","venue":null,"work_id":"5bddb2cf-1f45-4166-b5f2-1c2f3bff57aa","year":2022},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.589835Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:ef91c09c58e6866e73a9b43fe0656e71bd6536e63f21f8219d9a4f5d7d015c17","observation_id":"69876a0f-3918-426a-a459-c4cc6281b78c","resolution":{"observed_at":"2026-08-12T05:37:40.325891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:40.310861Z","title":"Iso/iec 25010:2023 systems and software engineering — systems and software quality requirements and evaluation (square) — product quality model,","venue":null,"work_id":"8b1d4e94-1021-453a-aefa-401ce9c8360b","year":2023},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.593091Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:54fb328749c6e3d1eccac98c858ff06410a287d531911d0258d87eaa8e35bbb9","observation_id":"3dcb7c27-9025-4dc9-b59a-d108a233b18f","resolution":{"observed_at":"2026-08-12T05:37:40.314610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.09886","last_updated":"2024-09-23T16:45:04Z","snapshot_observed_at":"2026-08-16T13:34:31.436421Z","submitted_at":"2024-07-13T13:26:43Z","title":"Speech-Copilot: Leveraging Large Language Models for Speech Processing via Task Decomposition, Modularization, and Program Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.09886","snapshot_observed_at":"2026-08-12T05:37:39.596526Z","title":"Speech- copilot: Leveraging large language models for speech processing via task decomposition, modularization, and program generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.596526Z"},"links":{"cited_paper":"/paper/2407.09886","citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:69262ae08fa50602ce25c6992694dbd1ed4c53f25b16f808e1d73795ca25ef78","observation_id":"b421d270-05b4-46cc-a08e-5c86be50ae59","resolution":{"observed_at":"2026-08-12T05:37:39.596526Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.08774","last_updated":"2024-12-30T13:43:54Z","snapshot_observed_at":"2026-08-17T07:19:04.802509Z","submitted_at":"2021-11-16T20:50:52Z","title":"Finding the Right Moment: Human-Assisted Trailer Creation via Task Composition","version":2},"cited_work":{"arxiv_id":"2111.08774","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.08774","snapshot_observed_at":"2026-08-12T05:37:39.925745Z","title":"Finding the Right Moment: Human-Assisted Trailer Creation via Task Composition","venue":"cs.CV","work_id":"b9ed4a96-9861-466f-a160-c5077c4abf36","year":2021},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.600414Z"},"links":{"cited_paper":"/paper/2111.08774","citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:5415905c9c4fef50ba580b3e422b6d4e57a9ee076617155939c4ae7da7a6ebd5","observation_id":"e7926b85-3994-465a-8855-5a695156ca96","resolution":{"observed_at":"2026-08-12T05:37:39.930295Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.61822/amcs-2024-0022","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:37:39.744599Z","title":"Learning abstract visual reasoning via task decomposition: A case study in raven progressive matrices,","venue":null,"work_id":"79fcbf29-0549-497d-83ec-9b79b126c924","year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.604770Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:2260584200a3a20be84691d3bf7ae7860371047cd36950c9faef24a1616ff8dc","observation_id":"fd8eef1e-2aac-414d-bd71-2afc41a2ec4b","resolution":{"observed_at":"2026-08-12T05:37:39.751530Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.10997","last_updated":"2024-03-27T09:16:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-18T07:47:33Z","title":"Retrieval-Augmented Generation for Large Language Models: A Survey","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.10997","snapshot_observed_at":"2026-08-12T05:37:39.608467Z","title":"Retrieval-augmented generation for large language models: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.608467Z"},"links":{"cited_paper":"/paper/2312.10997","citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:38a00d6d17849849635769a9bf1ec1c9ca11ed0489da2dc52f65fa1c5d2f83e3","observation_id":"3d5e21e3-218b-4802-b6ec-926d950295c7","resolution":{"observed_at":"2026-08-12T05:37:39.608467Z","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":"2026-08-12T05:37:40.300132Z","title":"Reducing hallucination in structured outputs via retrieval-augmented generation,","venue":null,"work_id":"88945f4c-106f-4ab4-9243-4ab815182507","year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.612486Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:72fbb86c3036aa6ea779fd6155a226378d888219e2a6c121bd55699b346d1ac7","observation_id":"5c0aacaf-fb4e-4058-918b-ba9bf8d1b950","resolution":{"observed_at":"2026-08-12T05:37:40.303971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.11761","last_updated":"2023-08-17T13:07:00Z","snapshot_observed_at":"2026-08-16T15:07:50.636588Z","submitted_at":"2023-08-17T13:07:00Z","title":"KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.11761","snapshot_observed_at":"2026-08-12T05:37:39.615909Z","title":"Knowledgpt: Enhancing large language models with retrieval and storage access on knowledge bases,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.615909Z"},"links":{"cited_paper":"/paper/2308.11761","citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:7bc3845a39d868b90aa9a4488f63771a734f04ad222ca449a852c4677b49fa07","observation_id":"9dd7a354-4ecb-40ad-9fec-d592cb3f390b","resolution":{"observed_at":"2026-08-12T05:37:39.615909Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.14732","last_updated":"2024-02-24T16:54:29Z","snapshot_observed_at":"2026-08-16T15:37:01.995131Z","submitted_at":"2023-04-28T10:15:25Z","title":"Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.14732","snapshot_observed_at":"2026-08-12T05:37:39.619720Z","title":"Search- in-the-chain: Interactively enhancing large language models with search for knowledge-intensive tasks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.619720Z"},"links":{"cited_paper":"/paper/2304.14732","citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:3ef420092fbdeff8911ec8470dcb8428aace8a44a3035e91de665cfba38201b3","observation_id":"7fbb0e90-0585-4858-adb9-8f9d4f70e1b8","resolution":{"observed_at":"2026-08-12T05:37:39.619720Z","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":"2026-08-12T05:37:39.623364Z","title":"Guidelines for conducting and reporting case study research in software engineering,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.623364Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:3cf5f7d9fb9555385e753687102268f511cbc98fe514fe96a9e6efe9db19fab8","observation_id":"97de0060-b8b1-4c9c-81a9-507d43dfeac8","resolution":{"observed_at":"2026-08-12T05:37:39.623364Z","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":"2026-08-12T05:37:40.288964Z","title":"A taxon- omy of foundation model based systems through the lens of software architecture,","venue":null,"work_id":"b614c22e-f4e2-48b2-8f30-1a576a5a76f9","year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.627016Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:aab6a81d67a142341d2b9c11dff28981150d7ce270444a771b711e59c8147172","observation_id":"e5005a44-1c14-409e-8c42-5bd43b2c0029","resolution":{"observed_at":"2026-08-12T05:37:40.292869Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:39.630405Z","title":"Toward responsible ai in the era of generative ai: A reference architecture for designing foundation model-based systems,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.630405Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:d2d7df8aef518eb45deeb3ff42091204d2df08c0c4b6c0d90bc71c3a476c7680","observation_id":"4cd8d8d1-eaae-43df-a9a7-46cedfbab349","resolution":{"observed_at":"2026-08-12T05:37:39.630405Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-12T05:37:40.278111Z","title":"RAFT: Adapting language model to domain specific RAG,","venue":null,"work_id":"aeb7b667-13da-4420-a0de-fae5950cd94b","year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.634284Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:11aa59c1f9abe86a5485c9ecd03b4a8ee69354c386e18767cf896b6d5b34e368","observation_id":"d335f85a-008e-4e0d-b1e0-80b6bd605a96","resolution":{"observed_at":"2026-08-12T05:37:40.281960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:40.267291Z","title":"Low-code LLM: Graphical user interface over large language models,","venue":null,"work_id":"5b8bced3-3969-476c-a19b-4a2ce0e25b1b","year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.637775Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:99750c111c86fee22626c4858984bf9cc26c4e01a99c5ab1bcfeb0f67ff78140","observation_id":"e6d1dfa5-a3aa-44a0-9e44-aae64a4fb940","resolution":{"observed_at":"2026-08-12T05:37:40.271363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:39.641096Z","title":"Gamma, R","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.641096Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:3e7a15eec0bbf9156f1e4273888fa23de36437e24f34cea410e68afd3ab5ff56","observation_id":"877fa5f8-f68e-4fc4-9f04-c1e7aab3633b","resolution":{"observed_at":"2026-08-12T05:37:39.641096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15734","last_updated":"2024-07-22T15:37:41Z","snapshot_observed_at":"2026-08-17T10:59:39.751793Z","submitted_at":"2024-07-22T15:37:41Z","title":"TaskGen: A Task-Based, Memory-Infused Agentic Framework using StrictJSON","version":1},"cited_work":{"arxiv_id":"2407.15734","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.15734","snapshot_observed_at":"2026-08-12T05:37:39.805353Z","title":"TaskGen: A Task-Based, Memory-Infused Agentic Framework using StrictJSON","venue":"cs.AI","work_id":"95b2e415-e961-4f93-8857-e0be4893c783","year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.645331Z"},"links":{"cited_paper":"/paper/2407.15734","citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:913d778504d67e67783463f325cf6ab177f2b843de8fbeb44b47719e819caba3","observation_id":"1dcf28fa-5d24-40de-bdba-e645b668d6f6","resolution":{"observed_at":"2026-08-12T05:37:39.810190Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:40.249977Z","title":"A jailbroken genai model can cause substantial harm: Genai-powered applications are vulnerable to promptwares,","venue":null,"work_id":"877a88d0-14ea-4749-9a89-757753959636","year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.648987Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:b4e9a7933b5b620acdaad315b7e3a69f5fd52a6a6c0430527c82e6650f373543","observation_id":"b8d9ee88-9dac-4f31-b71f-935418110fa2","resolution":{"observed_at":"2026-08-12T05:37:40.253859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.17915","last_updated":"2024-12-24T09:35:05Z","snapshot_observed_at":"2026-08-16T13:31:09.363033Z","submitted_at":"2024-07-25T10:09:21Z","title":"The Dark Side of Function Calling: Pathways to Jailbreaking Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.17915","snapshot_observed_at":"2026-08-12T05:37:39.652530Z","title":"The dark side of function calling: Pathways to jailbreaking large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.652530Z"},"links":{"cited_paper":"/paper/2407.17915","citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:dfaabd9e3c4e2e00fe86b604d03a96d19d2e3f4ca3f3e40d2897b7bae92bb2ff","observation_id":"9c6192d0-fec8-40e5-a268-7a50011c2efe","resolution":{"observed_at":"2026-08-12T05:37:39.652530Z","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":"2026-08-12T05:37:40.237836Z","title":"Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval,","venue":null,"work_id":"b830bf6c-b15d-435e-a3e8-c94b1f8ffef0","year":1994},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.656585Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:cbcdfbe65e2f959869b78ace77cf3b3f20ede08e3633972597c9660b96ef8cf3","observation_id":"8baf7184-a44d-4d92-bd89-aff9a1157397","resolution":{"observed_at":"2026-08-12T05:37:40.242319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:39.660207Z","title":"Seven failure points when engineering a retrieval augmented generation system,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.660207Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:46b3078af5ad1e3c261a435e62b5f06e81faa92c09b856533fe02e8427d22ada","observation_id":"a6817d2a-c4db-4742-8732-cbc7684575c7","resolution":{"observed_at":"2026-08-12T05:37:39.660207Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17887","last_updated":"2024-06-28T13:23:31Z","snapshot_observed_at":"2026-08-16T14:14:45.612232Z","submitted_at":"2024-02-27T21:01:41Z","title":"JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17887","snapshot_observed_at":"2026-08-12T05:37:39.664617Z","title":"Jmlr: Joint medical llm and retrieval training for enhancing reasoning and professional question answering capability,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.664617Z"},"links":{"cited_paper":"/paper/2402.17887","citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:f155e1c209e5aca31b38693844aeb984d8335b2eef4f0eef0cbc10d575ac8349","observation_id":"891d92ce-2cfa-4d86-8243-2a7ea967cf9f","resolution":{"observed_at":"2026-08-12T05:37:39.664617Z","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":"2026-08-12T05:37:40.226116Z","title":"Toolformer: language models can teach themselves to use tools,","venue":null,"work_id":"c1ddf178-4650-4c8c-935e-634b93d62b12","year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.668567Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:1835e849437d66cd244aca18db8ed24ca9adf7085bc083fde833454d8d1a9c1e","observation_id":"6894d1b0-e7af-48a1-8ec7-bba43c9202cb","resolution":{"observed_at":"2026-08-12T05:37:40.230271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:40.215016Z","title":"Large dual encoders are generalizable retrievers,","venue":null,"work_id":"92fe524e-40ac-40ac-a330-e98d89abfcab","year":2022},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.672150Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:094a0b796bcd63e9dcc997a9acbf8b6bd01cd5c873c1939aeb49ae27f5cc81e5","observation_id":"21aa4954-ae28-4d18-9c56-e8a727660c7f","resolution":{"observed_at":"2026-08-12T05:37:40.219096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:39.675576Z","title":"Dimensionality reduction by learning an invariant mapping,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.675576Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:0de4d2dcb412f678d6c2372e073220ef79a23a883cb03646d71ee993da5d9853","observation_id":"8ebac195-1674-401c-8b10-a0fec1a77d79","resolution":{"observed_at":"2026-08-12T05:37:39.675576Z","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":"2026-08-12T05:37:40.197155Z","title":"SimCSE: Simple contrastive learning of sentence embeddings,","venue":null,"work_id":"a4703083-be76-4e0f-87e5-d226c2d2adf1","year":2021},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.679826Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:c5601a5d6689bb0807cb1e527411f4fe7ce33e99f45b54e2046cce03590b0fa1","observation_id":"72e1dfdb-5a09-444b-a987-d41d3209c96e","resolution":{"observed_at":"2026-08-12T05:37:40.200793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:39.684437Z","title":"A learning algorithm for continually running fully recurrent neural networks,","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.684437Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:bf50a21b499553bbac54359fbb3682553213bac54c9d39f23dda201539c9e48c","observation_id":"8cde2b0e-368d-461a-bd56-999ca09800de","resolution":{"observed_at":"2026-08-12T05:37:39.684437Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.03339","last_updated":"2024-06-13T15:13:40Z","snapshot_observed_at":"2026-08-16T13:45:51.547717Z","submitted_at":"2024-06-05T14:55:10Z","title":"The Challenges of Evaluating LLM Applications: An Analysis of Automated, Human, and LLM-Based Approaches","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.03339","snapshot_observed_at":"2026-08-12T05:37:39.688496Z","title":"The challenges of evaluating llm applications: An analysis of automated, human, and llm-based approaches,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.688496Z"},"links":{"cited_paper":"/paper/2406.03339","citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:33308ba1d8bdeac624b370f4347d20a473841a9c89f4110583eea504d3ba6e34","observation_id":"e3dacc7a-4c46-40c4-ba19-14c6edfdc2c5","resolution":{"observed_at":"2026-08-12T05:37:39.688496Z","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":"2026-08-12T05:37:39.692058Z","title":"G-eval: NLG evaluation using gpt-4 with better human alignment,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.692058Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:1eca1f2a8de5747f658edd034a7e0d09a6332865db5e82e9139d876c5d383fdb","observation_id":"55a9698d-b9e2-4054-a28f-b8a184b26f31","resolution":{"observed_at":"2026-08-12T05:37:39.692058Z","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":"2026-08-12T05:37:39.695361Z","title":"Simple fast algorithms for the editing distance between trees and related problems,","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.695361Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:b7303906e2a6c72e9b7e778eb7342a7f5a037ca4b1d643316e32c4e5669c7940","observation_id":"31a33b3a-2159-4c2e-bf72-ad4e01efaab9","resolution":{"observed_at":"2026-08-12T05:37:39.695361Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.03146","last_updated":"2024-05-08T02:10:36Z","snapshot_observed_at":"2026-08-16T13:55:11.745491Z","submitted_at":"2024-05-06T03:42:34Z","title":"Quantifying the Capabilities of LLMs across Scale and Precision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.03146","snapshot_observed_at":"2026-08-12T05:37:39.699662Z","title":"Quantifying the capabilities of llms across scale and precision,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.699662Z"},"links":{"cited_paper":"/paper/2405.03146","citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:0804b6226dffdf2595bf8a26005a98d51fe6411b15f06db24d86fa80f0388da0","observation_id":"046e2736-a07d-46d5-9d1b-c229b49006a0","resolution":{"observed_at":"2026-08-12T05:37:39.699662Z","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":"2026-08-12T05:37:40.172575Z","title":"Data labeling: An empirical investigation into industrial challenges and mit- igation strategies,","venue":null,"work_id":"bc747032-06da-45a0-8cc7-436389de2faf","year":2020},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.703208Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:47cd03277c3e33731dfaac13adf324d3394730e4c08512a346c7fc59b9aa9c20","observation_id":"b6e47542-6457-4c8e-88ea-6af4aa69bcfe","resolution":{"observed_at":"2026-08-12T05:37:40.176530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-12T05:37:39.575910Z","title":"Available: https://doi.ieeecomputersociety.org/10.1109/ SANER53432.2022.00029","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":163,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.575910Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:f25c3efd9d52831a4144c54c209f6935da4141e898c6d8397447cb64a83a4c93","observation_id":"dc285a63-8a38-4ba9-bc40-eb5bd1dde7c4","resolution":{"observed_at":"2026-08-12T05:37:39.575910Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-12T05:37:40.435837Z","title":"Available: https://crfm.stanford.edu/assets/report.pdf","venue":null,"work_id":"a027c50e-1bfe-4c96-8d06-6f07bb64c377","year":null},"citing_paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-12T05:37:39.532560Z"},"links":{"citing_paper":"/paper/2412.00239"},"observation_digest":"sha256:54b2f089ce6cf3b2dfe5f64c6fdf942168a32dd219008f8d383dc9d93415dce2","observation_id":"54d8ab2a-71ad-4f7b-81ad-814157a0ab3c","resolution":{"observed_at":"2026-08-12T05:37:40.439678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.00239","last_updated":"2024-11-29T20:13:56Z","latest_version":1,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-17T05:40:58.901758Z","submitted_at":"2024-11-29T20:13:56Z","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":3,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":3,"verified_fuzzy":22},"total_outbound_references":47},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2412.00239."}