{"as_of":"2026-08-17T18:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:55f26a3659b95b7486724fcec0609c987993fc58a647b56e1739d094537badc6","coverage":[{"denominator":13,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T22:37:38.459837Z","state":"measured"},{"denominator":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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":"2026-05-20T05:28:43.607900Z","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":"2026-05-20T05:33:04.235308Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.18638","last_updated":"2025-08-27T21:28:06Z","snapshot_observed_at":"2026-08-17T04:12:55.895673Z","submitted_at":"2025-05-10T18:27:03Z","title":"Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity","version":2},"cited_work":{"arxiv_id":"2507.18638","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.18638","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Prompt engineering and the effectiveness of large language models in enhancing human productivity","venue":null,"work_id":"8c256c53-091d-457b-9136-eb0781c015e7","year":2025},"citing_paper":{"arxiv_id":"2605.20149","last_updated":"2026-05-19T17:40:14Z","snapshot_observed_at":"2026-08-11T18:16:59.055106Z","submitted_at":"2026-05-19T17:40:14Z","title":"Less Back-and-Forth: A Comparative Study of Structured Prompting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-20T05:28:43.607900Z"},"links":{"cited_paper":"/paper/2507.18638","citing_paper":"/paper/2605.20149"},"observation_digest":"sha256:4a3db32dc9de87189a4bfd18fe758aeda33dd82e5a9f9e271a048ebf66e11217","observation_id":"f5927dc0-94d9-4b2e-b423-ff2f6d7ae65c","resolution":{"observed_at":"2026-05-20T05:33:04.238199Z","resolver_source":"arxiv_id","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"}}],"links":{"evidence":"/evidence","html":"/paper/2507.18638/citation-record","integrity":"/paper/2507.18638/integrity","json":"/paper/2507.18638/citation-record.json","paper":"/paper/2507.18638"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2309.13205","last_updated":"2023-09-22T23:00:34Z","snapshot_observed_at":"2026-08-17T05:46:58.242368Z","submitted_at":"2023-09-22T23:00:34Z","title":"A Practical Survey on Zero-shot Prompt Design for In-context Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.13205","snapshot_observed_at":"2026-08-15T22:37:38.408250Z","title":"A Practical Survey on Zero-shot Prompt Design for In-context Learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.18638","last_updated":"2025-08-27T21:28:06Z","snapshot_observed_at":"2026-08-17T04:12:55.895673Z","submitted_at":"2025-05-10T18:27:03Z","title":"Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T22:37:38.408250Z"},"links":{"cited_paper":"/paper/2309.13205","citing_paper":"/paper/2507.18638"},"observation_digest":"sha256:ebb201332d5622cbf1ce183b30a4c69425b3908fd06c3a8bf8f302f6db7a2987","observation_id":"ce6824e6-2e90-4778-bb9b-24498edbdfb2","resolution":{"observed_at":"2026-08-15T22:37:38.408250Z","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":"2026-08-15T22:37:38.634904Z","title":"Fairness-guided Few-shot Prompting for Large Language Models,","venue":null,"work_id":"5441e225-2c67-4461-8b3b-5c3751b7a0f3","year":2023},"citing_paper":{"arxiv_id":"2507.18638","last_updated":"2025-08-27T21:28:06Z","snapshot_observed_at":"2026-08-17T04:12:55.895673Z","submitted_at":"2025-05-10T18:27:03Z","title":"Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T22:37:38.413548Z"},"links":{"citing_paper":"/paper/2507.18638"},"observation_digest":"sha256:31bf9d69a6500fa8f690e5b1f865e4919ac4662019d89a2aad4d6488f65b1ece","observation_id":"fc575519-9761-48a0-b0c1-98d1710de879","resolution":{"observed_at":"2026-08-15T22:37:38.638589Z","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":"2201.11903","last_updated":"2023-01-10T23:07:57Z","snapshot_observed_at":"2026-08-13T07:04:41.220509Z","submitted_at":"2022-01-28T02:33:07Z","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.11903","snapshot_observed_at":"2026-08-15T22:37:38.417827Z","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.18638","last_updated":"2025-08-27T21:28:06Z","snapshot_observed_at":"2026-08-17T04:12:55.895673Z","submitted_at":"2025-05-10T18:27:03Z","title":"Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T22:37:38.417827Z"},"links":{"cited_paper":"/paper/2201.11903","citing_paper":"/paper/2507.18638"},"observation_digest":"sha256:57d52d04fd774641055541fc1548ecb0a4eb4f15fbb52cfe35359a192a82618e","observation_id":"084eb0fa-6c82-4ca6-8513-85564fa177d8","resolution":{"observed_at":"2026-08-15T22:37:38.417827Z","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":"2026-08-15T22:37:38.623424Z","title":"Guidelines for Prompting Large Language Models,","venue":null,"work_id":"07c55cd2-b89b-4c07-a9ba-a503c4815887","year":2023},"citing_paper":{"arxiv_id":"2507.18638","last_updated":"2025-08-27T21:28:06Z","snapshot_observed_at":"2026-08-17T04:12:55.895673Z","submitted_at":"2025-05-10T18:27:03Z","title":"Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T22:37:38.422054Z"},"links":{"citing_paper":"/paper/2507.18638"},"observation_digest":"sha256:e2cc0628c09b6d8ccd5b97add3bc94bd951ff11c7ab47e7c009ce22df867b884","observation_id":"b3d86b0d-47fc-4c3d-a619-d0a125fb0127","resolution":{"observed_at":"2026-08-15T22:37:38.627392Z","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":"2026-08-15T22:37:38.612060Z","title":"Assigning Roles to Chatbots,","venue":null,"work_id":"f14c06cf-3c25-4374-ada2-ce256c3276d1","year":null},"citing_paper":{"arxiv_id":"2507.18638","last_updated":"2025-08-27T21:28:06Z","snapshot_observed_at":"2026-08-17T04:12:55.895673Z","submitted_at":"2025-05-10T18:27:03Z","title":"Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T22:37:38.426234Z"},"links":{"citing_paper":"/paper/2507.18638"},"observation_digest":"sha256:7f45faaa1e9d983f47b3ae247c608a1ca3150eaceac032b2f7be193871c87dc3","observation_id":"e099cd82-4177-4413-b3bd-7dba859edb2d","resolution":{"observed_at":"2026-08-15T22:37:38.615900Z","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":"2026-08-15T22:37:38.600211Z","title":"Automatic Prompt Engineer (APE),","venue":null,"work_id":"22539c5b-6d30-4326-b65a-6f1b3986f75c","year":null},"citing_paper":{"arxiv_id":"2507.18638","last_updated":"2025-08-27T21:28:06Z","snapshot_observed_at":"2026-08-17T04:12:55.895673Z","submitted_at":"2025-05-10T18:27:03Z","title":"Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T22:37:38.430048Z"},"links":{"citing_paper":"/paper/2507.18638"},"observation_digest":"sha256:f46feb5c39c8cf26b05c096e9a26c867ef2e4b3305ae1d50cf6b81e69917f504","observation_id":"8baa7051-c0af-4216-8825-a5057b9562bb","resolution":{"observed_at":"2026-08-15T22:37:38.604170Z","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":"2026-08-15T22:37:38.587422Z","title":"Prompt Tuning, Hard Prompts and Soft Prompts,","venue":null,"work_id":"3e391f24-3367-40b1-b01b-d303101b1cd3","year":2023},"citing_paper":{"arxiv_id":"2507.18638","last_updated":"2025-08-27T21:28:06Z","snapshot_observed_at":"2026-08-17T04:12:55.895673Z","submitted_at":"2025-05-10T18:27:03Z","title":"Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T22:37:38.433910Z"},"links":{"citing_paper":"/paper/2507.18638"},"observation_digest":"sha256:55923643bc291df3a31143e3731750f3cfe130d2c6f075955048e1f6f7d0cc88","observation_id":"1808ae3a-a411-4c86-9005-0f9436fc2433","resolution":{"observed_at":"2026-08-15T22:37:38.591648Z","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":"2026-08-15T22:37:38.574460Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning,","venue":null,"work_id":"4ddb221d-20ae-43c4-9cb6-5cd1f9e5be90","year":2023},"citing_paper":{"arxiv_id":"2507.18638","last_updated":"2025-08-27T21:28:06Z","snapshot_observed_at":"2026-08-17T04:12:55.895673Z","submitted_at":"2025-05-10T18:27:03Z","title":"Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T22:37:38.439121Z"},"links":{"citing_paper":"/paper/2507.18638"},"observation_digest":"sha256:16f03e2c4a99fc48ec30e8426adf9708f04183ea4c59bde2236aaa3b91ce8354","observation_id":"a1776c99-a409-44c8-b2ba-b21d457fa3f9","resolution":{"observed_at":"2026-08-15T22:37:38.578612Z","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":"2305.03495","last_updated":"2023-10-19T04:37:25Z","snapshot_observed_at":"2026-08-16T15:35:36.835145Z","submitted_at":"2023-05-04T15:15:22Z","title":"Automatic Prompt Optimization with \"Gradient Descent\" and Beam Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.03495","snapshot_observed_at":"2026-08-15T22:37:38.443498Z","title":"Automatic Prompt Optimization with ‘Gradient Descent’ and Beam Search,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.18638","last_updated":"2025-08-27T21:28:06Z","snapshot_observed_at":"2026-08-17T04:12:55.895673Z","submitted_at":"2025-05-10T18:27:03Z","title":"Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T22:37:38.443498Z"},"links":{"cited_paper":"/paper/2305.03495","citing_paper":"/paper/2507.18638"},"observation_digest":"sha256:564388f98b9584d3c8ad5511422c8f8bfe1705fcea5cebba4f67631bea71bd39","observation_id":"3cddf09b-d4f8-4c9d-85b3-669bbd6404fa","resolution":{"observed_at":"2026-08-15T22:37:38.443498Z","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":"2026-08-15T22:37:38.561848Z","title":"Enhancing English Comprehension through Generative AI and Prompt Engineering: A Study on Undergraduate Learning Outcomes,","venue":null,"work_id":"b05d7993-7d33-42e9-a9b9-541f0384dd6a","year":2024},"citing_paper":{"arxiv_id":"2507.18638","last_updated":"2025-08-27T21:28:06Z","snapshot_observed_at":"2026-08-17T04:12:55.895673Z","submitted_at":"2025-05-10T18:27:03Z","title":"Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T22:37:38.447811Z"},"links":{"citing_paper":"/paper/2507.18638"},"observation_digest":"sha256:ad63a974e2782363fa1a5f80d4c8cc911f193d49887c6a5f033fe796a58d1564","observation_id":"81904bcf-8c78-451b-915d-296a2d6892af","resolution":{"observed_at":"2026-08-15T22:37:38.566130Z","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":"2026-08-15T22:37:38.549580Z","title":"Mastering generative AI: Why effective prompting is the key to success at work,","venue":null,"work_id":"aeeaa78c-909c-40fe-ae8e-526b12c2379b","year":2024},"citing_paper":{"arxiv_id":"2507.18638","last_updated":"2025-08-27T21:28:06Z","snapshot_observed_at":"2026-08-17T04:12:55.895673Z","submitted_at":"2025-05-10T18:27:03Z","title":"Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T22:37:38.451538Z"},"links":{"citing_paper":"/paper/2507.18638"},"observation_digest":"sha256:92e9ac49c8ab4d2e0c0b24ac725a2c74176c9462cc585f047cdad1a8c3b49da0","observation_id":"8a20c93b-cd59-4548-b027-3bb829fb2d7a","resolution":{"observed_at":"2026-08-15T22:37:38.553793Z","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":"2026-08-15T22:37:38.537654Z","title":"The Critical Role of Prompt Engineering in the Workplace,","venue":null,"work_id":"24f6b445-1c49-4a8f-93c8-74d4d6c63385","year":2024},"citing_paper":{"arxiv_id":"2507.18638","last_updated":"2025-08-27T21:28:06Z","snapshot_observed_at":"2026-08-17T04:12:55.895673Z","submitted_at":"2025-05-10T18:27:03Z","title":"Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T22:37:38.455463Z"},"links":{"citing_paper":"/paper/2507.18638"},"observation_digest":"sha256:92adb6f7cb522adffa2396da3748e5af2b88449b4795851cd5ae63c01b1dc826","observation_id":"b3863b71-0c2e-4aff-94ee-8bae0be4ab72","resolution":{"observed_at":"2026-08-15T22:37:38.541468Z","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":"2026-08-15T22:37:38.523636Z","title":"The Cognitive Effects of AI-Human Collaboration: A Behavioral Study on Prompt Design and Decision-Making,","venue":null,"work_id":"9f74584f-c9ef-4551-8822-b2bc526d6381","year":2024},"citing_paper":{"arxiv_id":"2507.18638","last_updated":"2025-08-27T21:28:06Z","snapshot_observed_at":"2026-08-17T04:12:55.895673Z","submitted_at":"2025-05-10T18:27:03Z","title":"Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T22:37:38.459837Z"},"links":{"citing_paper":"/paper/2507.18638"},"observation_digest":"sha256:9f55399a676dcac0145f0854a3332f681f19379a310f7e2f5901379356f077f9","observation_id":"c8ac6f4d-23c8-4cba-9496-0c56036beaf9","resolution":{"observed_at":"2026-08-15T22:37:38.529467Z","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":"2507.18638","last_updated":"2025-08-27T21:28:06Z","latest_version":2,"primary_category":"cs.HC","snapshot_observed_at":"2026-08-17T04:12:55.895673Z","submitted_at":"2025-05-10T18:27:03Z","title":"Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity"},"reference_resolution":{"displayed":13,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":10},"total_outbound_references":13},"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 13 of 13 outbound references and 1 inbound Pith citation observation for arXiv:2507.18638."}