{"as_of":"2026-08-15T09:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:55f454829fc8a552d147718e691ad9a2f9d7509d2423a09df584948225ff4426","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T00:24:12.969305Z","state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2506.14146/citation-record","integrity":"/paper/2506.14146/integrity","json":"/paper/2506.14146/citation-record.json","paper":"/paper/2506.14146"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:12.923461Z","title":"A survey of reinforcement learning from human feedback","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.923461Z"},"links":{"citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:18ef69e27d9a8836963e90efd940b9127f79912015ce76f4e9f3d6cd7f2a3876","observation_id":"2b22ac79-1d82-4d4c-b0d9-84a3d5798a47","resolution":{"observed_at":"2026-08-07T00:24:12.923461Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.04600","last_updated":"2022-03-09T09:33:59Z","snapshot_observed_at":"2026-08-13T16:27:01.668293Z","submitted_at":"2022-03-09T09:33:59Z","title":"Domain Generalization using Pretrained Models without Fine-tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.04600","snapshot_observed_at":"2026-08-07T00:24:12.932909Z","title":"Domain generalization using pretrained models without fine-tuning.arXiv preprint arXiv:2203.04600,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.932909Z"},"links":{"cited_paper":"/paper/2203.04600","citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:d749e7172168c0a362d53a5d8232047fa54979bd086582f9c2f6f3d007c45556","observation_id":"814bb228-c1ca-452d-8f66-1a1340005871","resolution":{"observed_at":"2026-08-07T00:24:12.932909Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.09838","last_updated":"2025-02-21T17:39:29Z","snapshot_observed_at":"2026-08-14T04:57:56.034309Z","submitted_at":"2025-02-14T00:42:36Z","title":"HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.09838","snapshot_observed_at":"2026-08-07T00:24:12.937127Z","title":"Healthgpt: A medical large vision-language model for unifying comprehension and generation via heterogeneous knowledge adaptation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.937127Z"},"links":{"cited_paper":"/paper/2502.09838","citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:1b7b92e04214d31d8e69b75a8c42fa3c4c8ad9a5a9aba8b80bb4e481bc40e307","observation_id":"8e166a05-56f3-41e2-9ae0-e9f6c259bd0d","resolution":{"observed_at":"2026-08-07T00:24:12.937127Z","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":"2026-08-07T00:24:12.945720Z","title":"Towards scientific intelligence: A survey of llm-based scientific agents.arXiv preprint arXiv:2503.24047,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.945720Z"},"links":{"citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:2182f7760676bcf47b9d7922c50265816f4a3277a24a60e46ec76f65249ba99f","observation_id":"6320300a-54d0-4c6a-9d65-58137d6f2a52","resolution":{"observed_at":"2026-08-07T00:24:12.945720Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.10708","last_updated":"2025-08-30T11:17:17Z","snapshot_observed_at":"2026-08-15T00:51:29.401277Z","submitted_at":"2025-02-15T07:43:43Z","title":"Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.10708","snapshot_observed_at":"2026-08-07T00:24:12.949425Z","title":"Injecting domain-specific knowledge into large language models: a comprehensive survey.arXiv preprint arXiv:2502.10708,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.949425Z"},"links":{"cited_paper":"/paper/2502.10708","citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:8129da3fa7b0e271fe847f009de473eeb77b7cbe315504f61f1e0e09329aa3e8","observation_id":"9a1ee0da-0ed8-4edb-a4ac-6b193ac44ecb","resolution":{"observed_at":"2026-08-07T00:24:12.949425Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11805","last_updated":"2025-05-09T21:04:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-19T02:39:27Z","title":"Gemini: A Family of Highly Capable Multimodal Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.11805","snapshot_observed_at":"2026-08-07T00:24:12.952942Z","title":"M., Hauth, A., Millican, K., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.952942Z"},"links":{"cited_paper":"/paper/2312.11805","citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:31a4c8f16cc9189519535340a97c8c381f42df4e78b76b954ba7dee3fa5a5d33","observation_id":"a654120f-d7eb-4b88-a460-f3839d348823","resolution":{"observed_at":"2026-08-07T00:24:12.952942Z","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":"2026-08-07T00:24:13.340347Z","title":"Precedent-enhanced legal judgment prediction with llm and domain-model collab- oration","venue":null,"work_id":"f1584770-54f1-4380-b49c-d8bbb56ed426","year":2023},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.957380Z"},"links":{"citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:dac9d13b074883682e04e72466450cdaa2255846b13702794b6601ec96f4fe07","observation_id":"9cb8b770-4ec3-439e-b703-02b98fe6f3c4","resolution":{"observed_at":"2026-08-07T00:24:13.346210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05156","last_updated":"2024-03-14T14:17:57Z","snapshot_observed_at":"2026-08-13T00:59:28.434753Z","submitted_at":"2024-03-08T08:47:48Z","title":"On Protecting the Data Privacy of Large Language Models (LLMs): A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.05156","snapshot_observed_at":"2026-08-07T00:24:12.960792Z","title":"On protecting the data privacy of large language models (llms): A survey.arXiv preprint arXiv:2403.05156,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.960792Z"},"links":{"cited_paper":"/paper/2403.05156","citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:81525c933f2d0de88a75ac178fe2fa63f229b06851219b931c1dc7e46014aa67","observation_id":"21cc35ac-205a-40ea-bac0-fbbacfceb1c1","resolution":{"observed_at":"2026-08-07T00:24:12.960792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17193","last_updated":"2024-02-27T04:18:49Z","snapshot_observed_at":"2026-08-14T10:43:08.339405Z","submitted_at":"2024-02-27T04:18:49Z","title":"When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17193","snapshot_observed_at":"2026-08-07T00:24:12.965494Z","title":"When scal- ing meets llm finetuning: The effect of data, model and finetuning method.arXiv preprint arXiv:2402.17193,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.965494Z"},"links":{"cited_paper":"/paper/2402.17193","citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:053259e9e91ab341d246659b37810b62875ba167bfc2108cd481308f54cf439f","observation_id":"c951fbed-399d-4aea-9353-023fb92de5e1","resolution":{"observed_at":"2026-08-07T00:24:12.965494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.15061","last_updated":"2024-12-17T12:45:20Z","snapshot_observed_at":"2026-08-14T20:52:12.363932Z","submitted_at":"2024-02-23T02:24:15Z","title":"Fine-tuning Large Language Models for Domain-specific Machine Translation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.15061","snapshot_observed_at":"2026-08-07T00:24:12.969305Z","title":"Fine-tuning large language models for domain-specific machine translation.arXiv preprint arXiv:2402.15061,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.969305Z"},"links":{"cited_paper":"/paper/2402.15061","citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:15c15e4fc0ea8b7765b5e91237b664e87a295471ddc44684d8ebc8a5e574f5e1","observation_id":"17023f33-19f5-40b5-b1c8-7013b4189e5b","resolution":{"observed_at":"2026-08-07T00:24:12.969305Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17259","last_updated":"2024-12-23T04:02:46Z","snapshot_observed_at":"2026-08-13T17:31:34.764750Z","submitted_at":"2024-12-23T04:02:46Z","title":"LegalAgentBench: Evaluating LLM Agents in Legal Domain","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17259","snapshot_observed_at":"2026-08-07T00:24:12.928087Z","title":"Legalagentbench: Evaluating llm agents in legal domain.arXiv preprint arXiv:2412.17259,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.928087Z"},"links":{"cited_paper":"/paper/2412.17259","citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:4f125764cfa8b69c49a0965eec057c8acfa63320ef5d45163e59268c0f921849","observation_id":"c90dfaf9-7314-43e4-902b-b16d313fe0be","resolution":{"observed_at":"2026-08-07T00:24:12.928087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.15217","last_updated":"2023-09-11T17:25:24Z","snapshot_observed_at":"2026-08-03T19:11:09.671782Z","submitted_at":"2023-07-27T22:29:25Z","title":"Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.15217","snapshot_observed_at":"2026-08-07T00:24:12.912770Z","title":"K., Scheurer, J., Rando, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.912770Z"},"links":{"cited_paper":"/paper/2307.15217","citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:db2626d43f8e55fdefab0e668230d1dd30d9572b6dd027a1c33ea00d5e7bf76c","observation_id":"cddfd517-9ac4-4910-a2d7-25882bebf5e6","resolution":{"observed_at":"2026-08-07T00:24:12.912770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-07T00:24:12.920047Z","title":"The llama 3 herd of models.arXiv preprint arXiv:2407.21783,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.920047Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:5bed55f40e2b3d816fff1d66d787bf9c07e6c86278c07f03b219447eada88def","observation_id":"752d27f3-efd9-41c7-a89c-b6c81cc1d16a","resolution":{"observed_at":"2026-08-07T00:24:12.920047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.05862","last_updated":"2022-04-12T15:02:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-12T15:02:38Z","title":"Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.05862","snapshot_observed_at":"2026-08-07T00:24:12.908157Z","title":"Training a helpful and harmless assistant with rein- forcement learning from human feedback.arXiv preprint arXiv:2204.05862,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.908157Z"},"links":{"cited_paper":"/paper/2204.05862","citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:6f64c4ff48485222d7a9619d007bc88066759a6e65eda60ece656dcde9e8f24e","observation_id":"efc53251-3915-4c44-a77c-bd067565b1fc","resolution":{"observed_at":"2026-08-07T00:24:12.908157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.10485","last_updated":"2023-11-14T16:34:00Z","snapshot_observed_at":"2026-08-13T10:52:16.351105Z","submitted_at":"2023-07-19T22:43:57Z","title":"FinGPT: Democratizing Internet-scale Data for Financial Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.10485","snapshot_observed_at":"2026-08-07T00:24:12.941508Z","title":"Fingpt: Democ- ratizing internet-scale data for financial large language models.arXiv preprint arXiv:2307.10485,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.941508Z"},"links":{"cited_paper":"/paper/2307.10485","citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:ff6a83f00baacdbb277051caece05b4720031236b7aa3c334a22eac30ad04809","observation_id":"c3de1433-5cbb-496c-9270-d14f13636445","resolution":{"observed_at":"2026-08-07T00:24:12.941508Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-11T10:40:06.466250Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":0,"verified_fuzzy":1},"total_outbound_references":15},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2506.14146."}