{"as_of":"2026-08-06T12:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:223feba904a60c3e1161229d56fd798bdef2b24cdb223db85650e291338ea398","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-19T22:30:21.756864Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+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":"2026-06-27T06:49:12.070481Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-03T14:58:33.184885Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"cited_work":{"arxiv_id":"2605.17558","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.17558","snapshot_observed_at":"2026-07-03T14:58:33.184885Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","venue":"cs.SE","work_id":"e5816093-09d9-4c5d-8514-ade6208daac2","year":2026},"citing_paper":{"arxiv_id":"2606.12908","last_updated":"2026-06-11T05:06:50Z","snapshot_observed_at":"2026-08-05T04:59:46.668803Z","submitted_at":"2026-06-11T05:06:50Z","title":"SENTINEL: Failure-Driven Reinforcement Learning for Training Tool-Using Language Model Agents","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-06-27T06:49:12.070481Z"},"links":{"cited_paper":"/paper/2605.17558","citing_paper":"/paper/2606.12908"},"observation_digest":"sha256:396c15d4e79c99d4d72f4a845b3930833156d81b36085f8cf25178418689d714","observation_id":"33cbcf67-c751-4e06-82df-d41b5a216c8b","resolution":{"observed_at":"2026-07-03T14:58:33.186358Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2605.17558/citation-record","integrity":"/paper/2605.17558/integrity","json":"/paper/2605.17558/citation-record.json","paper":"/paper/2605.17558"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers, May 2026","venue":null,"work_id":"f6630c64-4616-44e2-98ca-63891932848f","year":2026},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:eb8109289892047879404f4b38a23c5dce54d74d52266aca406883c9f2998964","observation_id":"fd5316e3-0469-44e0-8b81-3f2526c5bb0b","resolution":{"observed_at":"2026-05-19T22:32:50.221646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07982","last_updated":"2025-06-09T17:52:18Z","snapshot_observed_at":"2026-07-06T21:39:13.304260Z","submitted_at":"2025-06-09T17:52:18Z","title":"$\\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment","version":1},"cited_work":{"arxiv_id":"2506.07982","doi":"10.48550/arxiv.2506.07982","metadata_source":"pith","pith_arxiv_id":"2506.07982","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"$\\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment","venue":"cs.AI","work_id":"3a498b1a-455f-4667-b572-c5216c99a89c","year":2025},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"cited_paper":"/paper/2506.07982","citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:1e30fc81679a2154eb196fe4d6d89d01f375f1ac8bd2fa181c74827578863b2d","observation_id":"d2caee5d-7adf-4b92-bb85-de4a46b9853f","resolution":{"observed_at":"2026-05-19T22:32:49.683772Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-14T18:20:22.129969+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T18:20:22.129969+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":"2107.03374","doi":"10.48550/arxiv.2107.03374","metadata_source":"pith","pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Evaluating Large Language Models Trained on Code","venue":"cs.LG","work_id":"042493e9-b26f-4b4e-bbde-382072ca9b08","year":2021},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:2042cb67504d92b89ec161ed1b25fada1a3d5d99b42cc9e743ce7029065ebe83","observation_id":"5303d856-de6d-4c06-9137-d7ba02a4eac8","resolution":{"observed_at":"2026-05-19T22:32:49.689730Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-01T08:08:23.404839+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T08:08:23.404839+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Scaling Agent Learning via Experience Synthesis, November 2025","venue":null,"work_id":"c4ed3596-4df7-4a87-bc6b-660810a70685","year":2025},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:8a299b9737ac2eb5a811aaf695a696f9b85b90dc745f4ec1a00846c39f087998","observation_id":"d477e8ae-15d8-45d7-84b7-39b20963fb97","resolution":{"observed_at":"2026-05-19T22:32:50.226565Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08244","last_updated":"2023-10-25T06:54:12Z","snapshot_observed_at":"2026-08-02T00:07:12.855748Z","submitted_at":"2023-04-14T14:05:32Z","title":"API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs","version":2},"cited_work":{"arxiv_id":"2304.08244","doi":"10.48550/arxiv.2304.08244","metadata_source":"pith","pith_arxiv_id":"2304.08244","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs","venue":"cs.CL","work_id":"a20d9332-ab34-485c-a060-1ba47cc98930","year":2023},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"cited_paper":"/paper/2304.08244","citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:980cf7bd4c12e4adf0e145aaaf11485813ed2936ed252c2f4f57685c2ab1f4c6","observation_id":"90dc69b7-7f6e-4b53-aff8-4fe5b1b3a533","resolution":{"observed_at":"2026-05-19T22:32:49.692323Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-14T18:20:23.914543+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T18:20:23.914543+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04062","last_updated":"2025-04-04T09:34:37Z","snapshot_observed_at":"2026-07-06T14:49:41.442616Z","submitted_at":"2023-02-08T13:59:31Z","title":"Machine Learning for Synthetic Data Generation: A Review","version":10},"cited_work":{"arxiv_id":"2302.04062","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.04062","snapshot_observed_at":"2026-07-04T21:10:09.196610Z","title":"InProceedings of the 18th Confer- ence of the European Chapter of the Association for Computational Linguistics, pages 139–151","venue":null,"work_id":"e2d7fa22-15d9-41e9-8486-23b870c0ac81","year":2023},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"cited_paper":"/paper/2302.04062","citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:20c2fc35ffc0fb4bee1ab340e13a1603f8dd0313bc3fbde7fdf4eacc7521eedf","observation_id":"8d130a63-fc49-477e-bb43-63e1a562f1c0","resolution":{"observed_at":"2026-05-19T22:32:49.697954Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks, November 2023","venue":null,"work_id":"2fd8ef7a-8185-47cb-81a5-0e8add8279c2","year":2023},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:a7854a5c8ff03a2474afd4cd81b73abe82fd04b576d69ae9c4ecd6bbf64ab3ee","observation_id":"d8a6a477-8f45-4995-bf17-91d3cdb2b84b","resolution":{"observed_at":"2026-05-19T22:32:50.224150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.09583","last_updated":"2025-06-04T08:58:56Z","snapshot_observed_at":"2026-08-02T06:48:43.121988Z","submitted_at":"2023-08-18T14:23:21Z","title":"WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct","version":3},"cited_work":{"arxiv_id":"2308.09583","doi":"10.48550/arxiv.2308.09583","metadata_source":"pith","pith_arxiv_id":"2308.09583","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct","venue":"cs.CL","work_id":"4fc3ba2f-b615-4eef-86e5-131dc8b98389","year":2023},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"cited_paper":"/paper/2308.09583","citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:6505099298385190b495e4d703304e14255fb09ff701f40f61def9925488cc15","observation_id":"585199ab-1ecd-4bc8-b2b0-05da0791f4af","resolution":{"observed_at":"2026-05-19T22:32:49.694984Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.03601","last_updated":"2025-07-19T17:39:17Z","snapshot_observed_at":"2026-08-05T07:23:13.534267Z","submitted_at":"2025-04-04T17:13:57Z","title":"APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay","version":4},"cited_work":{"arxiv_id":"2504.03601","doi":"10.48550/arxiv.2504.03601","metadata_source":"arxiv_reference","pith_arxiv_id":"2504.03601","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Apigen-mt: Agentic pipeline for multi-turn data generation via simulated agent-human interplay","venue":"ArXiv.org","work_id":"57da2252-8f38-4ac6-8459-00c0bfce1f9a","year":2025},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"cited_paper":"/paper/2504.03601","citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:a7f3f313174ce424738d6e8a8bfe121eefb4de6bebfb2817812be879cb8311bc","observation_id":"f7b139a4-3be4-442c-8425-2c49e10b1e9d","resolution":{"observed_at":"2026-05-19T22:32:49.687103Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2308.12950","doi":"10.48550/arxiv.2308.12950","metadata_source":"pith","pith_arxiv_id":"2308.12950","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Code Llama: Open Foundation Models for Code","venue":"cs.CL","work_id":"e73bffa4-7620-47ac-9327-259a60db52ca","year":2023},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"cited_paper":"/paper/2308.12950","citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:3a6290d07d162951ec197d8a1bd70117def2a5de5fc6c2dcb7cad4ecb81fbe05","observation_id":"bd4a0a5f-19ee-461b-a735-2a32d3f3f625","resolution":{"observed_at":"2026-05-19T22:32:49.706909Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-09T08:48:36.742994+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-09T08:48:36.742994+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Toolformer: Language models can teach themselves to use tools.Advances in Neural Information Processing Systems, 36:68539– 68551","venue":null,"work_id":"b6ced5f6-61f7-413d-ae8a-76680700a981","year":2023},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:c040a7b9717fda55451b85b50c630aedb6f7ba4ea2619f0cde4276562a356352","observation_id":"3dc4b6e8-731e-4545-8b25-36c3493fa538","resolution":{"observed_at":"2026-05-19T22:32:50.219506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4bc53fbf-bc04-410f-8cf3-f9cf956ab449","year":2024},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:70d564bb38bd3acb7eafdc8bb8163708ced84df1f1121d04d454a6d238f9c1fa","observation_id":"a52249a1-05c8-463e-acd7-074b1516c696","resolution":{"observed_at":"2026-05-19T22:32:50.215140Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.05301","last_updated":"2023-09-07T12:20:45Z","snapshot_observed_at":"2026-07-06T15:40:20.267344Z","submitted_at":"2023-06-08T15:46:32Z","title":"ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases","version":2},"cited_work":{"arxiv_id":"2306.05301","doi":"10.48550/arxiv.2306.05301","metadata_source":"pith","pith_arxiv_id":"2306.05301","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases","venue":"cs.CL","work_id":"e900f660-9178-4fda-ad54-a788e23aa0d8","year":2023},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"cited_paper":"/paper/2306.05301","citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:0648f4014b2917913bb29676fdf0058af88f8f4bf86228d2aa19b1b937b5497f","observation_id":"15ca8cce-74c6-423a-8c1b-54d4a0ec8d72","resolution":{"observed_at":"2026-05-19T22:32:49.700791Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Alpaca: A strong, replicable instruction-following model.Stanford University Center for Research on Foundation Models (CRFM) Technical Report","venue":null,"work_id":"95ff0a61-c86a-474c-9bc4-7f82cb399983","year":2023},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:5fccc69a9fbaa83a9239deb977579d8816ab1c4cca70f988ab5605d07d34e08a","observation_id":"789420d6-3639-47c5-bc59-0c50e4dae8c3","resolution":{"observed_at":"2026-05-19T22:32:50.217409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2510.24284","last_updated":"2026-04-16T11:42:35Z","snapshot_observed_at":"2026-07-06T22:34:13.674208Z","submitted_at":"2025-10-28T10:42:17Z","title":"MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools","version":3},"cited_work":{"arxiv_id":"2510.24284","doi":null,"metadata_source":"pith","pith_arxiv_id":"2510.24284","snapshot_observed_at":"2026-07-03T14:58:33.144483Z","title":"MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools","venue":"cs.AI","work_id":"09cf53e9-8f13-48a7-a6cc-527aa8e9bb81","year":2025},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"cited_paper":"/paper/2510.24284","citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:10a917f304b684d25be8132f58f2854e8dd2faf58c592d70582201c39c55cebd","observation_id":"8b33de74-6f9d-4440-9857-5d605896a5d4","resolution":{"observed_at":"2026-05-19T22:32:49.709724Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.11171","last_updated":"2023-03-07T17:57:37Z","snapshot_observed_at":"2026-07-06T12:50:22.773056Z","submitted_at":"2022-03-21T17:48:52Z","title":"Self-Consistency Improves Chain of Thought Reasoning in Language Models","version":4},"cited_work":{"arxiv_id":"2203.11171","doi":"10.1101/2025.04.03.646459","metadata_source":"pith","pith_arxiv_id":"2203.11171","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Self-Consistency Improves Chain of Thought Reasoning in Language Models","venue":"cs.CL","work_id":"8c6d5a6b-b5cc-4105-9c84-9c34bb9375bb","year":2022},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"cited_paper":"/paper/2203.11171","citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:fe6f302c62a125334beb3d5f63f7ac3f5f5f62514430c815a18d5631b8d6d9e1","observation_id":"a6ad14e5-816a-40ec-8dc9-689ab7b3be55","resolution":{"observed_at":"2026-05-19T22:32:49.718275Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-05-21T18:52:42.88633+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-21T18:52:42.88633+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents, January 2026","venue":null,"work_id":"6b8e4df1-578a-4fc5-9e6a-e09707713e4d","year":2026},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:121993a6bce766fa9a4647d2343332588e65642e189bbf6afd7f41c630770a3a","observation_id":"9b199f33-4c2a-4d1b-93b1-c918467fba27","resolution":{"observed_at":"2026-05-19T22:32:50.211203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T02:06:42.595164Z","title":"Chain-of-thought prompting elicits reasoning in large language models","venue":null,"work_id":"a3ac3b4d-8c58-4772-ad55-18e8fb162bd1","year":2022},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:0724d436643712a192a7949e3370347ec7ded844dca715a94200d9471fd8fb6a","observation_id":"16a96b81-390f-4b0e-aaea-f4c38a8a391b","resolution":{"observed_at":"2026-05-19T22:32:50.209090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"MCPMark: A Benchmark for Stress-Testing Realistic and Compre- hensive MCP Use, September 2025","venue":null,"work_id":"28cb4a29-92df-4364-8088-ae28d2ff3159","year":2025},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:daea2ba78c8d63ad056fe8d2c603a3e735377bfdfb91a4688df0375c60e8e360","observation_id":"f6314767-d63b-4938-b266-a4b2611f146b","resolution":{"observed_at":"2026-05-19T22:32:50.213339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Wizardlm: Empowering large pre-trained language models to follow complex instructions","venue":null,"work_id":"e855256d-fb97-4bde-8365-79ec55a819da","year":2024},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:87f18774772e75a175d7ad6dac8d3d9ab7f0b9987904443fe391459e47fe2aaf","observation_id":"01c66ab0-6ac4-484f-8e53-15f68e1d7823","resolution":{"observed_at":"2026-05-19T22:32:50.205970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13116","last_updated":"2024-10-21T16:22:33Z","snapshot_observed_at":"2026-08-02T17:17:56.296462Z","submitted_at":"2024-02-20T16:17:37Z","title":"A Survey on Knowledge Distillation of Large Language Models","version":4},"cited_work":{"arxiv_id":"2402.13116","doi":"10.48550/arxiv.2402.13116","metadata_source":"pith","pith_arxiv_id":"2402.13116","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A Survey on Knowledge Distillation of Large Language Models","venue":"cs.CL","work_id":"b1dcdadf-875e-4695-8fcf-0907c69302f3","year":2024},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"cited_paper":"/paper/2402.13116","citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:5707baf25aaf484c9bc834667fbdb813ebf787c91b1d60ffc479f79998a60a02","observation_id":"d1ad10bc-4d82-4dbe-b6a2-7207ab14cb2d","resolution":{"observed_at":"2026-05-19T22:32:49.703926Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T12:49:12.049248+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T12:49:12.049248+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.01179","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-03T17:38:43.979672Z","title":"Toucan: Synthesizing 1.5 m tool-agentic data from real- world mcp environments","venue":null,"work_id":"e05d2070-29e9-4737-94d7-551c5c1c6b71","year":2025},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:a0f0afd3e741d49b729b79b7367a35c97d9a08da20228b12da07c5b4fdc8e1e2","observation_id":"1a7e0e56-3350-4102-9853-3d00002afb7b","resolution":{"observed_at":"2026-05-19T22:32:49.715526Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Qwen3 Technical Report, May 2025","venue":null,"work_id":"9da79881-cfbf-4c3d-8ebb-727be1377ecc","year":2025},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:ec5226d61c3ca4a9d5d593fa36d19306ab541930553a60abf57c910798aacef0","observation_id":"3f91fde3-f9b4-4c56-b27f-62215bc2e17b","resolution":{"observed_at":"2026-05-19T22:32:50.203981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.12045","last_updated":"2024-06-17T19:33:08Z","snapshot_observed_at":"2026-08-02T22:19:29.043854Z","submitted_at":"2024-06-17T19:33:08Z","title":"$\\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains","version":1},"cited_work":{"arxiv_id":"2406.12045","doi":"10.48550/arxiv.2406.12045","metadata_source":"pith","pith_arxiv_id":"2406.12045","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"$\\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains","venue":"cs.AI","work_id":"6a8d8dc4-0cc0-4052-8109-abbcdcd4a962","year":2024},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"cited_paper":"/paper/2406.12045","citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:6cf539d41b1659022517a3157dd4069859f1ffa2c89f5cbbd3816469ee35ea70","observation_id":"fe6cecc6-e81e-4437-9a92-a91468a1c762","resolution":{"observed_at":"2026-05-19T22:32:49.712511Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-14T18:20:21.86453+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T18:20:21.86453+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Limitations","venue":null,"work_id":"57a8a555-c807-4af6-9de7-3256d4cfc2cc","year":2025},"citing_paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-19T22:30:21.756864Z"},"links":{"citing_paper":"/paper/2605.17558"},"observation_digest":"sha256:9f8fb75f8960fa1b4637c3d4bca6f3286978d23e161b118e9a64fe40ffe689f9","observation_id":"eb8f0a76-4976-490a-9435-f3b16721f7ec","resolution":{"observed_at":"2026-05-19T22:32:50.201796Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.17558","last_updated":"2026-05-17T17:38:17Z","latest_version":1,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-03T01:04:03.264088Z","submitted_at":"2026-05-17T17:38:17Z","title":"Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":1,"verified_exact":12,"verified_fuzzy":10},"total_outbound_references":25},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2605.17558."}