{"as_of":"2026-08-08T07:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:54b2a18797ba6e95493fa4ca07d0f1ff399fdf10e34f81e00dcf2587b99b870f","coverage":[{"denominator":17,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:01:45.923680Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2507.04099/citation-record","integrity":"/paper/2507.04099/integrity","json":"/paper/2507.04099/citation-record.json","paper":"/paper/2507.04099"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.03741","last_updated":"2024-10-01T06:34:31Z","snapshot_observed_at":"2026-08-04T03:26:17.430656Z","submitted_at":"2024-10-01T06:34:31Z","title":"Towards Democratization of Subspeciality Medical Expertise","version":1},"cited_work":{"arxiv_id":"2410.03741","doi":"10.48550/arxiv.2410.03741","metadata_source":"pith","pith_arxiv_id":"2410.03741","snapshot_observed_at":"2026-08-07T06:16:28.064256Z","title":"Towards Democratization of Subspeciality Medical Expertise","venue":"cs.HC","work_id":"af88c056-e72c-4238-a7be-3fe1d0a11eb7","year":2024},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:43.960781Z"},"links":{"cited_paper":"/paper/2410.03741","citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:06ed8c7c72bd9b7efc4b4311d8bc86eafedb6b20bfb4f27519a1df3a5aeeed60","observation_id":"e67e7342-26c5-4827-bb62-2a5ace367e72","resolution":{"observed_at":"2026-08-06T20:01:46.567450Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12741","last_updated":"2024-12-13T15:37:01Z","snapshot_observed_at":"2026-07-06T19:18:08.790424Z","submitted_at":"2024-09-19T13:03:24Z","title":"Fine Tuning Large Language Models for Medicine: The Role and Importance of Direct Preference Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12741","snapshot_observed_at":"2026-08-06T20:01:44.023672Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:44.023672Z"},"links":{"cited_paper":"/paper/2409.12741","citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:e975167c982ab4d6e546800f3180a5fd115486a36ef44d04f0de436273121077","observation_id":"755229fb-6832-44f3-b0d3-e2e37aee6581","resolution":{"observed_at":"2026-08-06T20:01:44.023672Z","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":"2026-08-06T20:01:47.649917Z","title":null,"venue":null,"work_id":"51d7b0ae-f6ac-49cd-a42c-869025d0b7a3","year":2024},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:44.093173Z"},"links":{"citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:6d539cd4b7bb414075d3e482ecea4da72aeeb2fcd4258c600b2dd1287e99127a","observation_id":"dc603ba7-f9ce-4f01-a310-20f742650006","resolution":{"observed_at":"2026-08-06T20:01:47.717253Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17887","last_updated":"2024-06-28T13:23:31Z","snapshot_observed_at":"2026-07-06T17:36:27.931373Z","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":"2026-08-06T20:01:44.210006Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:44.210006Z"},"links":{"cited_paper":"/paper/2402.17887","citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:3c55f249bfa80e68a363ee4ad9c8ac4602ad9c5196d97af04c6de1f185fe10f3","observation_id":"001eb42d-6342-44f7-b695-ca61cdeaabe2","resolution":{"observed_at":"2026-08-06T20:01:44.210006Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.18290","last_updated":"2024-07-29T22:26:36Z","snapshot_observed_at":"2026-08-01T16:34:38.795326Z","submitted_at":"2023-05-29T17:57:46Z","title":"Direct Preference Optimization: Your Language Model is Secretly a Reward Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.18290","snapshot_observed_at":"2026-08-06T20:01:44.337724Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:44.337724Z"},"links":{"cited_paper":"/paper/2305.18290","citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:53d1018b57bde27a5a028e56d97bb8bb5542f96819c4403787f21bf3cb92c0ab","observation_id":"08eda640-9124-4153-80d1-664381f8a5f0","resolution":{"observed_at":"2026-08-06T20:01:44.337724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-06T20:01:44.392416Z","title":"& Klimov, O","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:44.392416Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:34738a6c899cf2da466546bf9c650611108773c9b05e0c7bdc4a5182f4a6595f","observation_id":"44f05754-39d1-4135-b491-3e1a231fddd4","resolution":{"observed_at":"2026-08-06T20:01:44.392416Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-08-06T14:58:42.911363Z","submitted_at":"2024-02-05T18:55:32Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-08-06T20:01:44.443750Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:44.443750Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:6c5a33cfb74b39a264dce0b707a9d6c08198764eaeaceaa6dfc7078a4a89dbf7","observation_id":"2f22cf46-d81d-41b4-970a-9f0f0a2984a6","resolution":{"observed_at":"2026-08-06T20:01:44.443750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.04717","last_updated":"2026-04-20T02:55:07Z","snapshot_observed_at":"2026-07-06T21:05:11.303685Z","submitted_at":"2025-04-07T04:00:08Z","title":"Beyond Single-Turn: A Survey on Multi-Turn Interactions with Large Language Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.04717","snapshot_observed_at":"2026-08-06T20:01:44.587262Z","title":"https://arxiv.org/html/2504.04717v1?utm_source=chatgpt.com","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:44.587262Z"},"links":{"cited_paper":"/paper/2504.04717","citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:d6306ac36854f57d2be074caf1920d5f0a7108985fa5a674f648d84f32ab9c69","observation_id":"4175c59e-8224-4ff4-8289-4b7226c494a5","resolution":{"observed_at":"2026-08-06T20:01:44.587262Z","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":"2026-08-06T20:01:47.466253Z","title":"US Elsevier Health https://www.us.elsevierhealth.com/the-medical-interview-9780323052214.html","venue":null,"work_id":"4deeed60-72da-4394-aa76-3e52cfa5e8bb","year":null},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:44.728062Z"},"links":{"citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:0787610920f5cdf69cb873467d7755567e7c8e6490b43d57871f77fd127ca273","observation_id":"8cddbe88-51a7-4687-93c1-08f27c024484","resolution":{"observed_at":"2026-08-06T20:01:47.521488Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.11107","last_updated":"2022-10-13T13:37:42Z","snapshot_observed_at":"2026-08-03T19:24:13.227094Z","submitted_at":"2022-05-23T07:57:32Z","title":"Learning to branch with Tree MDPs","version":3},"cited_work":{"arxiv_id":"2205.11107","doi":"10.48550/arxiv.2205.11107","metadata_source":"pith","pith_arxiv_id":"2205.11107","snapshot_observed_at":"2026-08-07T06:16:28.064256Z","title":"Learning to branch with Tree MDPs","venue":"cs.LG","work_id":"32ef76c6-ed41-4fdb-b136-c62a596f2a3e","year":2022},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:44.927157Z"},"links":{"cited_paper":"/paper/2205.11107","citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:7739d61f8403558b6565e86981053ad32e28d2db726123b7ad991fc9b55c9549","observation_id":"30747c74-d84d-467a-9eb6-4bfd4d8e3087","resolution":{"observed_at":"2026-08-06T20:01:46.229114Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T20:01:47.319094Z","title":"& Chen, W","venue":null,"work_id":"5e7e3043-5bcc-46f2-b835-08427e72cc03","year":null},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:45.110846Z"},"links":{"citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:442fd49d690985098ae4e277972d76c1061830cf0c8de35ab15f626b392bff8c","observation_id":"026835ad-b87a-48a7-bc24-21fdf1d75606","resolution":{"observed_at":"2026-08-06T20:01:47.376195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.15478","last_updated":"2025-03-19T17:55:08Z","snapshot_observed_at":"2026-08-07T16:51:01.687483Z","submitted_at":"2025-03-19T17:55:08Z","title":"SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.15478","snapshot_observed_at":"2026-08-06T20:01:45.270627Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:45.270627Z"},"links":{"cited_paper":"/paper/2503.15478","citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:9bb338f8e8cac1ecc0fb7ee0ebcd9d1c4faad655e2641b6183bae811c0f58979","observation_id":"1f0dc4f4-f5e2-4c7e-91bb-808ff5e823e3","resolution":{"observed_at":"2026-08-06T20:01:45.270627Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.13081","last_updated":"2020-09-28T05:07:51Z","snapshot_observed_at":"2026-08-06T03:17:52.286711Z","submitted_at":"2020-09-28T05:07:51Z","title":"What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.13081","snapshot_observed_at":"2026-08-06T20:01:45.385242Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:45.385242Z"},"links":{"cited_paper":"/paper/2009.13081","citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:fb4c50fd49e763b0a80b8b08bd2fc8962203826cce79d6158402e540aadc32c6","observation_id":"6f65453d-6c5f-4325-88c5-f393af758795","resolution":{"observed_at":"2026-08-06T20:01:45.385242Z","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":"2026-08-06T20:01:47.139230Z","title":"https://huggingface.co/meta-llama/Llama- 3.1-8B-Instruct (2024)","venue":null,"work_id":"d3d8e25d-48c8-4dab-9635-277ade1b7aad","year":2024},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:45.509959Z"},"links":{"citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:31b93150090b62b85543edef27b888baa8491e37b297e670b20de363a6c79e40","observation_id":"ebbb0cb5-17b1-49c8-a137-50e113bd89c1","resolution":{"observed_at":"2026-08-06T20:01:47.227192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T20:01:46.898419Z","title":"https://huggingface.co/mistralai/Ministral- 8B-Instruct-2410","venue":null,"work_id":"55685c69-4b6d-484b-829e-0e83ed31ae05","year":null},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:45.651039Z"},"links":{"citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:acdb054c848c3b948321122a6c1d2554adc01cb1ad3d507c38937837dafd76d3","observation_id":"b78a7175-7309-4e5a-8c6a-c9a4a95c9504","resolution":{"observed_at":"2026-08-06T20:01:46.997475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T20:01:46.723792Z","title":"https://platform.openai.com","venue":null,"work_id":"6601914d-22dc-42a8-b7ed-010cca9511bd","year":null},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:45.773828Z"},"links":{"citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:46c33757c0c6823f732bd120b383b975d6491be3921f691b9805108ac26845a1","observation_id":"7612c144-f160-4fce-a610-0097b64df3a8","resolution":{"observed_at":"2026-08-06T20:01:46.812795Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1510.03055","last_updated":"2016-06-10T22:03:28Z","snapshot_observed_at":"2026-07-06T04:32:42.614338Z","submitted_at":"2015-10-11T14:04:57Z","title":"A Diversity-Promoting Objective Function for Neural Conversation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1510.03055","snapshot_observed_at":"2026-08-06T20:01:45.923680Z","title":"& Dolan, B","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:45.923680Z"},"links":{"cited_paper":"/paper/1510.03055","citing_paper":"/paper/2507.04099"},"observation_digest":"sha256:2556ce8a215d82cdc0eaac0e68b4e63bcbba4948f740a49ebf30a9d57dc6f71d","observation_id":"0a3e0925-f9e2-44ff-8887-5c85b88b41fb","resolution":{"observed_at":"2026-08-06T20:01:45.923680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.04099","last_updated":"2025-07-15T16:49:25Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-06T19:53:19.486487Z","submitted_at":"2025-07-05T16:49:34Z","title":"Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching"},"reference_resolution":{"displayed":17,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":2,"verified_fuzzy":5},"total_outbound_references":17},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2507.04099."}