{"as_of":"2026-08-11T12:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6fd3feb3b0063f2b1022fa6a6c827c24afdbd2dca1a9aaf7c9b7308bbd013149","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:20:43.456980Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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":"2026-05-10T02:49:21.124253Z","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":"2026-05-10T02:53:29.828376Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"cited_work":{"arxiv_id":"2507.11071","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.11071","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2507.11071 , year=","venue":null,"work_id":"ec6ba378-1595-4ed2-80bc-5f7824cb82dc","year":null},"citing_paper":{"arxiv_id":"2604.19118","last_updated":"2026-04-21T05:56:51Z","snapshot_observed_at":"2026-07-06T23:05:48.885141Z","submitted_at":"2026-04-21T05:56:51Z","title":"DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-05-10T02:49:21.124253Z"},"links":{"cited_paper":"/paper/2507.11071","citing_paper":"/paper/2604.19118"},"observation_digest":"sha256:ce77abf02e7da9055cd368bd2204ce55c0453fad105197b5850341fb7d0ffa0d","observation_id":"57070d0d-378e-4cb9-9a28-095abbc9dc1e","resolution":{"observed_at":"2026-05-10T02:53:29.829602Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.11071/citation-record","integrity":"/paper/2507.11071/integrity","json":"/paper/2507.11071/citation-record.json","paper":"/paper/2507.11071"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:20:45.146631Z","title":"https://moldstud.com/articles/p-the-impact-of-big-data-on-software-development (2024)","venue":null,"work_id":"02fa2e2d-9554-4dc8-9a49-f246b2487f90","year":2024},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:41.960879Z"},"links":{"citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:6a9028490c4b4c065dc7f07c4086b3c7c20f9555656f2bfb2dba1dcd06c491bf","observation_id":"7e4a4774-3186-4d14-be1d-ccb35320424e","resolution":{"observed_at":"2026-08-06T17:20:45.179841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:20:42.001992Z","title":"ICLR1(2), 3 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:42.001992Z"},"links":{"citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:3364c4da8d9f4799b9214b021cadc2fb2f1bec02c02b472af3e3f3f63a3bafe8","observation_id":"f48f2aad-d1a1-40ab-8dca-4a932256b9c3","resolution":{"observed_at":"2026-08-06T17:20:42.001992Z","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":"2026-08-06T17:20:45.002210Z","title":"In: International Conference on Machine Learning, pp","venue":null,"work_id":"7ab26b74-1c9a-49d1-a624-c6fc6e8ffb5b","year":2019},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:42.101364Z"},"links":{"citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:b139787578dab86f7218a85aa5b1cde37e6ff86694cd51d00f1d5fe7ecace8fb","observation_id":"aefcab09-62bd-4a22-b9ff-961d54298ea9","resolution":{"observed_at":"2026-08-06T17:20:45.099322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.02385","last_updated":"2024-06-04T02:05:30Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-01-04T17:54:59Z","title":"TinyLlama: An Open-Source Small Language Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.02385","snapshot_observed_at":"2026-08-06T17:20:42.202188Z","title":"arXiv preprint arXiv:2401.02385 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:42.202188Z"},"links":{"cited_paper":"/paper/2401.02385","citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:7deed4d13f60bf53c070a56835a913d53ca3eb3e0e96b590f44233a92d63c67f","observation_id":"6fdb71f2-c4d4-49c4-baf9-3bfa29695b41","resolution":{"observed_at":"2026-08-06T17:20:42.202188Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.03303","last_updated":"2026-05-09T05:30:15Z","snapshot_observed_at":"2026-08-08T14:14:26.000056Z","submitted_at":"2023-08-07T05:12:27Z","title":"LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.03303","snapshot_observed_at":"2026-08-06T17:20:42.301515Z","title":"arXiv preprint arXiv:2308.03303 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:42.301515Z"},"links":{"cited_paper":"/paper/2308.03303","citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:d541530914dbe998b1cb706e39010aef98bf4606331012bf639f2cda573ca924","observation_id":"0c77f119-3df5-4ca2-88b5-d56227742f61","resolution":{"observed_at":"2026-08-06T17:20:42.301515Z","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":"2026-08-06T17:20:44.820724Z","title":null,"venue":null,"work_id":"ff5b8734-dc9d-48cd-977c-4ee3d07e828e","year":2023},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:42.367563Z"},"links":{"citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:fa2baf87cbba92110038df2c9a102282c45505576bc9666344315cdd8860a636","observation_id":"9e6367af-b238-4c38-b212-4c942f07db9e","resolution":{"observed_at":"2026-08-06T17:20:44.909840Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T17:20:44.663925Z","title":"IEEE international conference on web services (ICWS) (2017)","venue":null,"work_id":"0f09a328-826a-4282-83b3-4181045d0feb","year":2017},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:42.446200Z"},"links":{"citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:2a967b151744207f104468d102c949785678e6bef571ba6e139aa8b3132002e7","observation_id":"5d6599a8-a181-4343-824a-69a864638d46","resolution":{"observed_at":"2026-08-06T17:20:44.725586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T17:20:44.507661Z","title":"Advances in Neural Information Pro- cessing Systems 30 (2017)","venue":null,"work_id":"8eff56c2-779e-4b91-807b-1dd04cca1fa6","year":2017},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:42.527676Z"},"links":{"citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:08ac58dcdd37aeef5d9028ddd94023280b84aa733fa5c2c54d392e1bbf5d0b49","observation_id":"d70b4de3-8c58-40a9-a171-588bafe04f18","resolution":{"observed_at":"2026-08-06T17:20:44.573471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T17:20:44.349688Z","title":"In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp","venue":null,"work_id":"24aaf2db-c978-4fcd-ad36-48f7cef7d481","year":2017},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:42.600656Z"},"links":{"citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:c86ae775bf9a4ea633987beb03e6c106a7010a8489866b471fa0d536ed967897","observation_id":"8efddc4e-e8f5-49a7-a628-b0069e6f699d","resolution":{"observed_at":"2026-08-06T17:20:44.426260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:20:42.700282Z","title":"Advances in Neural Information Processing Systems (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:42.700282Z"},"links":{"citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:5cf0b0bf91fb74de741d85cc2f49086e4074834a61a9bf4f13b27c5773693f84","observation_id":"ed366201-e1c7-4af2-a148-01612fc10b7a","resolution":{"observed_at":"2026-08-06T17:20:42.700282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-06T17:20:42.850968Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:42.850968Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:ba3745be94e66f31f5c662b31435fb9cdb7804a67a265087595a9eb54aea8036","observation_id":"b5499fc1-d7ae-4c45-86c2-37cb209e7c6e","resolution":{"observed_at":"2026-08-06T17:20:42.850968Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.05911","last_updated":"2019-11-23T06:36:13Z","snapshot_observed_at":"2026-08-10T18:23:52.033853Z","submitted_at":"2019-11-23T06:36:13Z","title":"Recurrent Neural Networks (RNNs): A gentle Introduction and Overview","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.05911","snapshot_observed_at":"2026-08-06T17:20:42.937876Z","title":null,"venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:42.937876Z"},"links":{"cited_paper":"/paper/1912.05911","citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:6f27162d0964e0ff370e03c2755b812e5eba0f05bb8a58581848f4e3b87bf40a","observation_id":"0b022f57-4425-4903-ac28-dbb279415dff","resolution":{"observed_at":"2026-08-06T17:20:42.937876Z","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":"2026-08-06T17:20:44.187404Z","title":"In: 2021 International Joint Conference on Neural Networks (IJCNN), pp","venue":null,"work_id":"d5a31930-5cbf-4630-afcd-8c5718e51d23","year":2021},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:43.026122Z"},"links":{"citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:c5023ff3a38f276f29990c0b87d27af914e20f70189d0d824130a10d204d0e94","observation_id":"f37655ed-feee-42ee-96e3-5bb49c24fb5e","resolution":{"observed_at":"2026-08-06T17:20:44.276727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T17:20:43.999337Z","title":null,"venue":null,"work_id":"676896c7-b69d-4f55-8fa3-3b62294074a6","year":2024},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:43.116443Z"},"links":{"citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:8014f2cae4bffdb42216d9931bd4aff54cfa31909f28dca11cce65054dbc3b02","observation_id":"72100165-ec66-4655-9a32-998cc6577324","resolution":{"observed_at":"2026-08-06T17:20:44.090326Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T17:20:43.806445Z","title":"In: 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE), pp","venue":null,"work_id":"f404bd02-7ab7-4a8c-bf51-0ff3e1dc51c2","year":2023},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:43.208221Z"},"links":{"citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:9b6d6aa8320b0b93bd1c8159af132dc6d603c33a4e4178f1241578330ba6999a","observation_id":"b65eaa95-92a8-43bc-bd5f-cebfb4864d91","resolution":{"observed_at":"2026-08-06T17:20:43.902566Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T17:20:43.613274Z","title":"In: 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN’07), pp","venue":null,"work_id":"6ba9aca5-8d14-4357-ba67-ddaac10487c9","year":2007},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:43.301666Z"},"links":{"citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:252ffe963a2abe41cfb94f222d3469f3dd3e7239dc82b277f93d7071b55322ce","observation_id":"52008183-0cea-44f2-9eeb-a08057643376","resolution":{"observed_at":"2026-08-06T17:20:43.675147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05463","last_updated":"2023-09-11T14:01:45Z","snapshot_observed_at":"2026-08-02T22:47:03.212781Z","submitted_at":"2023-09-11T14:01:45Z","title":"Textbooks Are All You Need II: phi-1.5 technical report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.05463","snapshot_observed_at":"2026-08-06T17:20:43.394359Z","title":"arXiv preprint arXiv:2309.05463 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:43.394359Z"},"links":{"cited_paper":"/paper/2309.05463","citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:3926e9be326f9ead1c94f9870eb27ba30a624c4faf8ee082d633c66308b5444f","observation_id":"d35a484d-3fd9-438c-958d-c5102796ed0b","resolution":{"observed_at":"2026-08-06T17:20:43.394359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.01068","last_updated":"2022-06-21T17:04:40Z","snapshot_observed_at":"2026-08-06T03:13:37.403059Z","submitted_at":"2022-05-02T17:49:50Z","title":"OPT: Open Pre-trained Transformer Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.01068","snapshot_observed_at":"2026-08-06T17:20:43.456980Z","title":"arXiv preprint arXiv:2205.01068 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T17:20:43.456980Z"},"links":{"cited_paper":"/paper/2205.01068","citing_paper":"/paper/2507.11071"},"observation_digest":"sha256:b6ce01bb0c5065826c7acc6d49e0ebfd62253069e884731e805c76514d696f7a","observation_id":"14f71b86-a6b6-4fed-9d6c-8790f4d86d54","resolution":{"observed_at":"2026-08-06T17:20:43.456980Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.11071","last_updated":"2025-07-15T08:04:31Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T17:14:32.548539Z","submitted_at":"2025-07-15T08:04:31Z","title":"LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":8},"total_outbound_references":18},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2507.11071."}