{"as_of":"2026-08-19T02:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fc4228dbcf0e7f2ca980efc8c38e8324f1ddc2039a270d929842a320f2b47270","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":32,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:40:57.778934Z","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":"2026-05-25T06:50:28.002244Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":"2402.17834","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2402.17834 (2024) 34","venue":null,"work_id":"5ed7b6c0-8393-4f50-a699-44ae533adc22","year":2024},"citing_paper":{"arxiv_id":"2408.10692","last_updated":"2025-10-21T08:51:46Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-20T09:42:26Z","title":"Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-23T22:12:30.050438Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2408.10692"},"observation_digest":"sha256:cfbe429c398a3e9358070e925a4698b9ed57eb73bd4057a1c86d1e566637ab9f","observation_id":"d7a7ef0e-6c73-4114-904a-d310705a180f","resolution":{"observed_at":"2026-05-23T22:13:30.082924Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":"2402.17834","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2402.17834 (2024) 34","venue":null,"work_id":"5ed7b6c0-8393-4f50-a699-44ae533adc22","year":2024},"citing_paper":{"arxiv_id":"2410.18451","last_updated":"2024-10-24T06:06:26Z","snapshot_observed_at":"2026-08-13T15:15:10.681693Z","submitted_at":"2024-10-24T06:06:26Z","title":"Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-17T16:18:01.560780Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2410.18451"},"observation_digest":"sha256:c3ea1b870975d0bb77e270a24f04b84aea85139f0fa7be9ff744ab76ddf026ec","observation_id":"f62f30ff-377d-4d6e-99aa-0e886c1e6b42","resolution":{"observed_at":"2026-05-17T16:18:01.596417Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-12T16:20:32.537094Z","title":"Stable lm 2 1.6 b tech- nical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.13676","last_updated":"2024-11-20T19:51:25Z","snapshot_observed_at":"2026-08-14T20:26:44.533985Z","submitted_at":"2024-11-20T19:51:25Z","title":"Hymba: A Hybrid-head Architecture for Small Language Models","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T16:20:32.537094Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2411.13676"},"observation_digest":"sha256:f496df2fefa9a3666cc97feae12025ddab1a1adffa921ef01d5e60534be98069","observation_id":"e3896155-8905-435a-9ee9-1aea20503140","resolution":{"observed_at":"2026-08-12T16:20:32.537094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-12T15:58:20.125957Z","title":"Stable lm 2 1.6 b technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.13766","last_updated":"2025-07-11T21:47:39Z","snapshot_observed_at":"2026-08-14T07:37:29.230681Z","submitted_at":"2024-11-21T00:29:58Z","title":"Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge","version":4},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T15:58:20.125957Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2411.13766"},"observation_digest":"sha256:a8007effe129af3df5b3a814f51763b897e43e0b05ba8f5f0a702630ed93f354","observation_id":"55c28788-9f44-4237-9d16-c63bb2a22ec0","resolution":{"observed_at":"2026-08-12T15:58:20.125957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-11T22:31:01.784752Z","title":"Stable lm 2 1.6 b technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03398","last_updated":"2024-12-04T15:27:39Z","snapshot_observed_at":"2026-08-13T13:38:24.401357Z","submitted_at":"2024-12-04T15:27:39Z","title":"RedStone: Curating General, Code, Math, and QA Data for Large Language Models","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T22:31:01.784752Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2412.03398"},"observation_digest":"sha256:81589b1d5def6341b89db2f39490f076253f9ea3bb440adc145a4d7bba62a06a","observation_id":"6079b528-de0f-4f1d-a31c-16eb814225a5","resolution":{"observed_at":"2026-08-11T22:31:01.784752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-11T21:37:26.912242Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.04277","last_updated":"2024-12-05T15:59:29Z","snapshot_observed_at":"2026-08-14T09:22:00.298043Z","submitted_at":"2024-12-05T15:59:29Z","title":"Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-11T21:37:26.912242Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2412.04277"},"observation_digest":"sha256:8ebacdc5109ffcc1b283cafc83229ef600696b28d8fb3daa745074ebca7d39ad","observation_id":"655011dd-1388-4e86-b44d-09e17173aff4","resolution":{"observed_at":"2026-08-11T21:37:26.912242Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-11T21:40:05.376053Z","title":"Stable lm 2 1.6 b technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.04315","last_updated":"2024-12-06T11:39:27Z","snapshot_observed_at":"2026-08-14T20:58:54.206883Z","submitted_at":"2024-12-05T16:31:13Z","title":"Densing Law of LLMs","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T21:40:05.376053Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2412.04315"},"observation_digest":"sha256:1dafc79e2fe1baa51835ed10d5df3f15e11b0a908b61349efde490195ed15160","observation_id":"7a95284a-301d-4196-b024-f622450fd23a","resolution":{"observed_at":"2026-08-11T21:40:05.376053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-11T12:05:48.401951Z","title":"Stable lm 2 1.6 b technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14689","last_updated":"2025-05-28T05:22:58Z","snapshot_observed_at":"2026-08-15T08:21:08.511659Z","submitted_at":"2024-12-19T09:43:39Z","title":"How to Synthesize Text Data without Model Collapse?","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T12:05:48.401951Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2412.14689"},"observation_digest":"sha256:b5826b1958c58323d41af3e3a697145c7df63c3645d5293e5a795b9680e501b4","observation_id":"a9de8daf-0c16-4526-8d8c-9c3a4fc1353b","resolution":{"observed_at":"2026-08-11T12:05:48.401951Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-11T05:17:55.502568Z","title":"Stable LM 2 1.6b technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.17743","last_updated":"2024-12-24T16:07:47Z","snapshot_observed_at":"2026-08-16T08:15:47.006654Z","submitted_at":"2024-12-23T17:47:53Z","title":"YuLan-Mini: An Open Data-efficient Language Model","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-11T05:17:55.502568Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2412.17743"},"observation_digest":"sha256:46f5f9836159189e911f75e2febe990f47b9b31b6e21ee8df4c440fa90c42fae","observation_id":"272b80f4-77c6-4adf-a15c-c943b17e1b84","resolution":{"observed_at":"2026-08-11T05:17:55.502568Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-10T23:08:26.271570Z","title":"Stable LM 2 1.6B technical report.arXiv preprint arXiv:2402.17834, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.21124","last_updated":"2025-03-16T21:10:15Z","snapshot_observed_at":"2026-08-17T20:30:23.005028Z","submitted_at":"2024-12-30T17:55:28Z","title":"Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T23:08:26.271570Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2412.21124"},"observation_digest":"sha256:a04b3a79b764d9adfffdba47a1bf6e97daed6c5d5a58f0bdf59c7dff606652dc","observation_id":"e7be17e8-052a-492e-8dfd-c091eb85b34f","resolution":{"observed_at":"2026-08-10T23:08:26.271570Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-10T21:42:12.399199Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.04322","last_updated":"2025-01-23T08:24:29Z","snapshot_observed_at":"2026-08-17T18:00:27.329541Z","submitted_at":"2025-01-08T07:42:54Z","title":"Eve: Efficient Multimodal Vision Language Models with Elastic Visual Experts","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-10T21:42:12.399199Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2501.04322"},"observation_digest":"sha256:2f45f5836bd08c6a25ee3452cdfa73a01775dcb0e5dd56017dcda9412622679e","observation_id":"a7c42f80-daee-48be-a582-c3d128abda0a","resolution":{"observed_at":"2026-08-10T21:42:12.399199Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-10T20:54:02.019512Z","title":"Stable lm 2 1.6 b technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.07124","last_updated":"2025-01-17T09:39:17Z","snapshot_observed_at":"2026-08-14T10:55:02.137567Z","submitted_at":"2025-01-13T08:26:43Z","title":"LLM360 K2: Building a 65B 360-Open-Source Large Language Model from Scratch","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-10T20:54:02.019512Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2501.07124"},"observation_digest":"sha256:466cff6861762785bcb817a00903dd2cd04c3d882cb50b719400e2ba6957c5cf","observation_id":"d32ee3df-d104-47ef-a4d0-887de42d0615","resolution":{"observed_at":"2026-08-10T20:54:02.019512Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-10T19:54:09.997708Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.09604","last_updated":"2025-01-16T15:24:41Z","snapshot_observed_at":"2026-08-13T21:51:10.694884Z","submitted_at":"2025-01-16T15:24:41Z","title":"From Scarcity to Capability: Empowering Fake News Detection in Low-Resource Languages with LLMs","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-10T19:54:09.997708Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2501.09604"},"observation_digest":"sha256:9dded27093080d58338953694cbe5a7e603a2d0040803aaa9554100129c22fac","observation_id":"2e22c561-d22e-4742-8580-1ad8a6f55997","resolution":{"observed_at":"2026-08-10T19:54:09.997708Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-10T15:50:21.836712Z","title":"Stable lm 2 1.6 b technical report","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.13629","last_updated":"2025-02-10T17:19:21Z","snapshot_observed_at":"2026-08-16T15:30:42.801825Z","submitted_at":"2025-01-23T12:58:14Z","title":"Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T15:50:21.836712Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2501.13629"},"observation_digest":"sha256:f70424f3cb6abdcb49503c2b2d7f19e591249d6025fc497785e3de0f88c2eca2","observation_id":"975c55ac-f182-4b34-9ee7-74e2f95ebfab","resolution":{"observed_at":"2026-08-10T15:50:21.836712Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-10T05:35:54.050974Z","title":"Stable lm 2 1.6 b technical report","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16937","last_updated":"2025-02-27T23:41:37Z","snapshot_observed_at":"2026-08-12T16:41:07.316871Z","submitted_at":"2025-01-28T13:31:18Z","title":"TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models","version":4},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-10T05:35:54.050974Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2501.16937"},"observation_digest":"sha256:6d2d39d658f5df28543ca15f7d3ba4950d456d39ade3a1d678b4ada9ca8be0a1","observation_id":"96268f02-0d74-4659-8c50-842a38acbad4","resolution":{"observed_at":"2026-08-10T05:35:54.050974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-08T10:23:06.043305Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08142","last_updated":"2025-02-12T05:48:57Z","snapshot_observed_at":"2026-08-18T03:45:11.621403Z","submitted_at":"2025-02-12T05:48:57Z","title":"Bridging the Safety Gap: A Guardrail Pipeline for Trustworthy LLM Inferences","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T10:23:06.043305Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2502.08142"},"observation_digest":"sha256:c6171883608c70f0fa0e6564c053310869df1244fdaae8d6d7d5f3dacfd76f57","observation_id":"edb123da-b620-4f13-a6f0-82dc76af68f5","resolution":{"observed_at":"2026-08-08T10:23:06.043305Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-16T04:40:57.778934Z","title":"Stable lm 2 1.6 b technical report,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.00776","last_updated":"2025-05-01T18:12:30Z","snapshot_observed_at":"2026-08-18T17:36:03.569325Z","submitted_at":"2025-05-01T18:12:30Z","title":"Reasoning Capabilities and Invariability of Large Language Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T04:40:57.778934Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2505.00776"},"observation_digest":"sha256:dff8f13c6d4e241add7e460dda4489359fee77674e7e52bcaf35cd04495c23bd","observation_id":"19793900-b9e0-428a-b8fe-e4f2f14c7d56","resolution":{"observed_at":"2026-08-16T04:40:57.778934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-15T22:44:35.165968Z","title":"Stable LM 2 1.6B Technical Report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.06461","last_updated":"2025-05-09T23:05:53Z","snapshot_observed_at":"2026-08-16T06:26:29.831350Z","submitted_at":"2025-05-09T23:05:53Z","title":"Challenging GPU Dominance: When CPUs Outperform for On-Device LLM Inference","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T22:44:35.165968Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2505.06461"},"observation_digest":"sha256:79b6ea2ba4004a4f3acf55c803e6b1458cb9d3fa73443e0c1807a393e185fcc9","observation_id":"0f08b15b-f967-451a-a830-2e387cf574ac","resolution":{"observed_at":"2026-08-15T22:44:35.165968Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-07T13:20:30.438921Z","title":"arXiv preprint arXiv:2402.17834 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23830","last_updated":"2025-05-28T08:38:39Z","snapshot_observed_at":"2026-08-17T18:11:33.461839Z","submitted_at":"2025-05-28T08:38:39Z","title":"EvoMoE: Expert Evolution in Mixture of Experts for Multimodal Large Language Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T13:20:30.438921Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2505.23830"},"observation_digest":"sha256:408fd4becfb36739e1b406f1b74462f46ed541fa68b5ed901e6c4f1379591da7","observation_id":"0949492e-850b-4757-a66f-5666ff7f2d6e","resolution":{"observed_at":"2026-08-07T13:20:30.438921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-07T12:35:26.936553Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24538","last_updated":"2025-05-30T12:44:54Z","snapshot_observed_at":"2026-08-18T21:55:41.428956Z","submitted_at":"2025-05-30T12:44:54Z","title":"Don't Erase, Inform! Detecting and Contextualizing Harmful Language in Cultural Heritage Collections","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.936553Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2505.24538"},"observation_digest":"sha256:79e0237d9c525a7a952198503456ffef983d836bef850a6caf9646fc451acf52","observation_id":"abbc2e96-8722-40f5-bccc-f16d7d3272db","resolution":{"observed_at":"2026-08-07T12:35:26.936553Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-07T10:29:43.136599Z","title":"Stable lm 2 1.6 b technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05209","last_updated":"2025-06-05T16:21:30Z","snapshot_observed_at":"2026-08-09T07:25:20.673180Z","submitted_at":"2025-06-05T16:21:30Z","title":"The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T10:29:43.136599Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2506.05209"},"observation_digest":"sha256:5ba26419616cca5bedd22090a3bd0fff205aa453a5770a9e7bdea435782fdb38","observation_id":"20f044fb-555d-4613-b3e4-788f55b446da","resolution":{"observed_at":"2026-08-07T10:29:43.136599Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-15T18:36:03.965294Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19697","last_updated":"2025-06-24T15:03:57Z","snapshot_observed_at":"2026-08-19T01:19:15.095271Z","submitted_at":"2025-06-24T15:03:57Z","title":"Outlier-Safe Pre-Training for Robust 4-Bit Quantization of Large Language Models","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T18:36:03.965294Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2506.19697"},"observation_digest":"sha256:782954cddd121baf2d1bf1cadd3f45da8cb14ac5b5d30ff3ad6ef3c16b8409bd","observation_id":"e3e79822-2eea-4b0a-95dc-96b90e14eb3b","resolution":{"observed_at":"2026-08-15T18:36:03.965294Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-06T18:37:43.188523Z","title":"Stable lm 2 1.6 b technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.07818","last_updated":"2025-08-13T13:45:40Z","snapshot_observed_at":"2026-08-13T14:16:59.615800Z","submitted_at":"2025-07-10T14:48:08Z","title":"MoSE: Skill-by-Skill Mixture-of-Experts Learning for Embodied Autonomous Machines","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T18:37:43.188523Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2507.07818"},"observation_digest":"sha256:67bbab6b84c288aae9453f56e5da9ebd728888e6bf4bf97cd7b4ff6b445e1d68","observation_id":"226d1cf1-0197-4c11-93c2-7c9e1ece8f0c","resolution":{"observed_at":"2026-08-06T18:37:43.188523Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-06T15:29:54.841438Z","title":"Stable lm 2 1.6b: Improving upon our previous language model with significantly improved training","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.15773","last_updated":"2025-07-22T13:27:02Z","snapshot_observed_at":"2026-08-15T13:24:22.754714Z","submitted_at":"2025-07-21T16:27:48Z","title":"Supernova: Achieving More with Less in Transformer Architectures","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T15:29:54.841438Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2507.15773"},"observation_digest":"sha256:54dc519a78eac7aac498e48d8b9a51e4e5c5af2df35f1fefe6d512cdc88e5416","observation_id":"02c786c9-768c-49f1-a0b8-4b6e3057179d","resolution":{"observed_at":"2026-08-06T15:29:54.841438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-15T18:22:34.571274Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17706","last_updated":"2025-07-23T17:12:19Z","snapshot_observed_at":"2026-08-15T18:16:06.562729Z","submitted_at":"2025-07-23T17:12:19Z","title":"HydraOpt: Navigating the Efficiency-Performance Trade-off of Adapter Merging","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T18:22:34.571274Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2507.17706"},"observation_digest":"sha256:60c46c70f91d3856b69a9d27ed8fa903b6e744f61967d353ea4f6b72c01f4a15","observation_id":"30470b48-4700-4024-baeb-aadd5cd97fa4","resolution":{"observed_at":"2026-08-15T18:22:34.571274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-05T23:01:46.837049Z","title":"Stable lm 2 1.6b technical report.arXiv:2402.17834, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.06014","last_updated":"2025-08-08T05:01:17Z","snapshot_observed_at":"2026-08-15T18:00:58.571548Z","submitted_at":"2025-08-08T05:01:17Z","title":"ExploreGS: Explorable 3D Scene Reconstruction with Virtual Camera Samplings and Diffusion Priors","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-05T23:01:46.837049Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2508.06014"},"observation_digest":"sha256:3c95b20ce9ae9bb01c83894e79ed475f5b172be380200924b7f1cc704d8f6999","observation_id":"31b7e6f0-c880-4f7d-9c4b-d42fd08931c2","resolution":{"observed_at":"2026-08-05T23:01:46.837049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-05T16:21:43.081686Z","title":"arXiv preprint arXiv:2402.17834 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.18687","last_updated":"2025-08-26T05:21:19Z","snapshot_observed_at":"2026-08-14T21:42:48.015545Z","submitted_at":"2025-08-26T05:21:19Z","title":"Knowing or Guessing? Robust Medical Visual Question Answering via Joint Consistency and Contrastive Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T16:21:43.081686Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2508.18687"},"observation_digest":"sha256:217d0242c3c3264123194491edb63086b70b1b0520ba7c05aa6fcb2f21d2cf98","observation_id":"81d137b3-ba22-44e8-8a5c-0dda87616818","resolution":{"observed_at":"2026-08-05T16:21:43.081686Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-02T21:01:57.284733Z","title":"Stable LM 2 1.6B technical report.arXiv preprint arXiv:2402.17834,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.13259","last_updated":"2026-07-03T09:17:51Z","snapshot_observed_at":"2026-08-11T10:23:07.674922Z","submitted_at":"2026-02-25T08:12:47Z","title":"How Transformers Reject Wrong Answers: Rotational Dynamics of Factual Constraint Processing","version":3},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-02T21:01:57.284733Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2603.13259"},"observation_digest":"sha256:287a8c27e338ceb1b9fcbea9de3075abf45b450666a5c91c1473eb58e6d478bf","observation_id":"133624e8-0c3d-4db5-b73f-01c34ea81e78","resolution":{"observed_at":"2026-08-02T21:01:57.284733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":"2402.17834","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2402.17834 (2024) 34","venue":null,"work_id":"5ed7b6c0-8393-4f50-a699-44ae533adc22","year":2024},"citing_paper":{"arxiv_id":"2605.02443","last_updated":"2026-05-22T15:24:30Z","snapshot_observed_at":"2026-08-02T13:58:05.076867Z","submitted_at":"2026-05-04T10:43:27Z","title":"HalluScan: A Systematic Benchmark for Detecting and Mitigating Hallucinations in Instruction-Following LLMs","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-25T06:49:16.755597Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2605.02443"},"observation_digest":"sha256:01eecdb3e33e945d10a9de6679bf7261e4034caf1ce6b6efbff6f47344e51ad8","observation_id":"2826ee43-8b90-4c87-a7af-cdd2f3f33e27","resolution":{"observed_at":"2026-05-25T06:50:28.006214Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-02T11:29:43.044747Z","title":"Stable lm 2 1.6 b technical report.arXiv preprint arXiv:2402.17834, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.14502","last_updated":"2026-07-23T02:50:21Z","snapshot_observed_at":"2026-08-16T03:36:56.551252Z","submitted_at":"2026-06-12T14:33:55Z","title":"From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI","version":2},"reference_index":250,"source":"pdf_text","source_observed_at":"2026-08-02T11:29:43.044747Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2606.14502"},"observation_digest":"sha256:7060f4a3f3607fe4ca203f8b4e3044bf0075d0ccffd8948477c845eb555e06b6","observation_id":"8621ed3d-6f1f-4805-be16-b53c2d3418d8","resolution":{"observed_at":"2026-08-02T11:29:43.044747Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-07-31T01:27:12.091976Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.27421","last_updated":"2026-07-29T19:42:33Z","snapshot_observed_at":"2026-08-16T15:18:54.928593Z","submitted_at":"2026-07-29T19:42:33Z","title":"Selecting Open-Weight Language Models for Zero-Shot Intent Classification: A Systematic Evaluation of 41 Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-31T01:27:12.091976Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2607.27421"},"observation_digest":"sha256:09a7ea21a9dadc82b5ea1c4734154146ebdbea6b2a08c32d157bfcae99758b27","observation_id":"99cb935d-8a34-4d25-a508-417e659b0802","resolution":{"observed_at":"2026-07-31T01:27:12.091976Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17834","snapshot_observed_at":"2026-08-15T14:28:03.438930Z","title":"Stable lm 2 1.6 b technical report.arXiv preprint arXiv:2402.17834, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.09907","last_updated":"2026-08-10T17:52:14Z","snapshot_observed_at":"2026-08-18T21:22:46.245965Z","submitted_at":"2026-08-10T17:52:14Z","title":"DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T14:28:03.438930Z"},"links":{"cited_paper":"/paper/2402.17834","citing_paper":"/paper/2608.09907"},"observation_digest":"sha256:18a902854f675884e5fb339ff6f6c7c761eb6ff8fa149a2cbc0c895be3b9ecd6","observation_id":"f58eccf1-afb2-4b72-b1f0-fa07de793470","resolution":{"observed_at":"2026-08-15T14:28:03.438930Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2402.17834/citation-record","integrity":"/paper/2402.17834/integrity","json":"/paper/2402.17834/citation-record.json","paper":"/paper/2402.17834"},"outbound":[],"paper":{"arxiv_id":"2402.17834","last_updated":"2024-02-27T19:00:07Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T14:14:46.903233Z","submitted_at":"2024-02-27T19:00:07Z","title":"Stable LM 2 1.6B Technical Report"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 32 inbound Pith citation observations for arXiv:2402.17834."}