{"as_of":"2026-08-07T01:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f181e254ed78d9aa143ff28c2d76d0e08a3a1918d048f9bb61b5fad9e60f2c16","coverage":[{"denominator":128,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T10:42:47.058369Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+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/2607.20145/citation-record","integrity":"/paper/2607.20145/integrity","json":"/paper/2607.20145/citation-record.json","paper":"/paper/2607.20145"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1609.08675","last_updated":"2016-09-27T21:21:49Z","snapshot_observed_at":"2026-08-06T22:24:48.588621Z","submitted_at":"2016-09-27T21:21:49Z","title":"YouTube-8M: A Large-Scale Video Classification Benchmark","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.08675","snapshot_observed_at":"2026-08-01T10:42:34.395951Z","title":"arXiv , author =:1609.08675 , primaryclass =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:34.395951Z"},"links":{"cited_paper":"/paper/1609.08675","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:e3c439090ffb694f4245d2b136e3f5df5dc348629577aea56853ee8afb7d2222","observation_id":"6eefd2aa-ae09-4e8f-8c9a-17bfcd45177f","resolution":{"observed_at":"2026-08-01T10:42:34.395951Z","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":"2026-08-01T10:42:34.505442Z","title":"Findings of the Association for Computational Linguistics: ACL 2025 , year =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:34.505442Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:734b490a002b65a11c6700b83e4e0148bd1b527bb1f97817b3fda389d5babe20","observation_id":"8208a9ee-5e0a-436c-b810-72276e1534d7","resolution":{"observed_at":"2026-08-01T10:42:34.505442Z","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":"2026-08-01T10:42:34.629095Z","title":", institution =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:34.629095Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:0843f9c7952d22364950ee8773dd4f04245f746e2b08e0bc26bc9458ff1f2180","observation_id":"79b801c9-061f-4453-8d1b-24affb66e7fd","resolution":{"observed_at":"2026-08-01T10:42:34.629095Z","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":"2026-08-01T10:42:34.756027Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:34.756027Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:238dad3a23fb3ebbedee9d9b6537db088e7fec4620ac360adcaa491a5d12c31d","observation_id":"154e4279-dc39-44ca-9731-d13c3e66f8ec","resolution":{"observed_at":"2026-08-01T10:42:34.756027Z","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":"2026-08-01T10:42:34.888041Z","title":"arXiv preprint arXiv:2602.09003 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:34.888041Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:4d5991199ff14fb06fcaff1010dc52b31b0c0b7e1a68b3c3a0f128af940eb076","observation_id":"04f19e51-a960-46d7-834f-551da8269f6b","resolution":{"observed_at":"2026-08-01T10:42:34.888041Z","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":"2026-08-01T10:42:35.007541Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:35.007541Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:ca8b1f5b5eeb92f57a5c10b33020b99f0b365cd55f9af69f9e0287d53abde5b1","observation_id":"69397945-f4b1-4735-a598-37d86ec667d0","resolution":{"observed_at":"2026-08-01T10:42:35.007541Z","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":"2026-08-01T10:42:35.169126Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:35.169126Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:2b80047459f25f47b701b5c0a01cdb9241d1cb82dc6520b4927e17694faded3d","observation_id":"d2d396f0-29b2-4c9e-a5e9-628f9f8a70c5","resolution":{"observed_at":"2026-08-01T10:42:35.169126Z","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":"2026-08-01T10:42:35.250421Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:35.250421Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:2c9f9632918ab2be28bde0a6f19ad197cd5fe6df9af0359aa8f8a7d66067652a","observation_id":"3a5054b5-6eef-4019-9bd2-92c19f72735d","resolution":{"observed_at":"2026-08-01T10:42:35.250421Z","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":"2026-08-01T10:42:35.337613Z","title":"Le and Geoffrey E","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:35.337613Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:5aa4ca5eb66b14f9d1375a578cde18bff4967cd6a8a3daf6d963368b3d2654e7","observation_id":"b8ccaa0f-370c-4dd1-9201-89544f60ff63","resolution":{"observed_at":"2026-08-01T10:42:35.337613Z","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":"2026-08-01T10:42:35.452315Z","title":"9th International Conference on Learning Representations, ICLR 2021 , publisher =","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:35.452315Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:a20891fc9f4d114ae94a38ffb156402e186b455665103ce87eeb795656ce0a31","observation_id":"b9eb0a12-f3b2-430d-a395-10d991ef7626","resolution":{"observed_at":"2026-08-01T10:42:35.452315Z","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":"2026-08-01T10:42:35.574131Z","title":"Journal of Machine Learning Research , volume =","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:35.574131Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:bb5ed9ded77c9375d0559ef41cc22d8d2ffc0378ceeb9f5ef0b0ddde3504c49c","observation_id":"52745584-23ac-4c7b-bb3c-a426d845b93b","resolution":{"observed_at":"2026-08-01T10:42:35.574131Z","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":"2026-08-01T10:42:35.833233Z","title":"Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2020 , pages =","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:35.833233Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:c8332deb437268f352931ba57482555cff1c8a51545b6de3239a109ba873eb8f","observation_id":"db183193-2bae-4f75-90c0-dea1d9eb7d75","resolution":{"observed_at":"2026-08-01T10:42:35.833233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-27T04:03:16Z","title":"DeepSeek-V3 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19437","snapshot_observed_at":"2026-08-01T10:42:35.990033Z","title":"Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Haowei Zhang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Li and Hui Qu and J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:35.990033Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:ea6592a7bd81ed89aebd79958c86879e7e5c779bd436142848ad1049839a0489","observation_id":"e8d259df-08ea-40fd-a384-ac3c16a94b73","resolution":{"observed_at":"2026-08-01T10:42:35.990033Z","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":"2026-08-01T10:42:36.162156Z","title":"Operations Research , volume =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:36.162156Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:693f0d4647c2519b23ef04ba0213ae96ce028f5b8a6095d8dd10b44772a7a1b7","observation_id":"20d5b6b3-ce37-4082-b824-c756f25ef0a8","resolution":{"observed_at":"2026-08-01T10:42:36.162156Z","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":"2026-08-01T10:42:36.267678Z","title":"Thirty-Ninth AAAI Conference on Artificial Intelligence, AAAI 2025 , pages =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:36.267678Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:bb81f0becd2c8615ec5e83c020d8827969299407b2c05160dc9845c637132b74","observation_id":"a033cb60-25fa-4c53-8662-0b4b7f3c80c3","resolution":{"observed_at":"2026-08-01T10:42:36.267678Z","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":"2026-08-01T10:42:36.399044Z","title":"Proceedings of the NeurIPS 2022 Competitions Track , pages =","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:36.399044Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:694fc84d9e94c649bd7cb73cdc4bff93f4344c3a80d6d195119a4eba6a67243f","observation_id":"aa58dfc8-99dd-4924-9822-936c109b5ebe","resolution":{"observed_at":"2026-08-01T10:42:36.399044Z","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":"2026-08-01T10:42:36.516159Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:36.516159Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:b819fbeff70d36234b24ff1c55df77848ff01d2c3f211519e4ec7f7d1f377ce5","observation_id":"4bf77e72-c30b-4597-ba83-15c495a34d12","resolution":{"observed_at":"2026-08-01T10:42:36.516159Z","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":"2026-08-01T10:42:36.636257Z","title":"2606.19348 , archivePrefix=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:36.636257Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:f13dc54934de03db385b894f36788675da88275605ffe7e6d6848921ca6cf84a","observation_id":"d5bb94f7-9dad-4d91-b8a7-dab5011bb25a","resolution":{"observed_at":"2026-08-01T10:42:36.636257Z","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":"2026-08-01T10:42:36.713124Z","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence , articleno =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:36.713124Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:90e7401a0649447266e7c5843a6f2438080f8df4c17945b07c2c0aa4fb26198f","observation_id":"9a5d2e95-95a4-4c9d-85f2-00f7c5b57e3f","resolution":{"observed_at":"2026-08-01T10:42:36.713124Z","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":"2026-08-01T10:42:36.815873Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:36.815873Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:2555905f135a6d58532b61ef5785b94b5b52f825de800dfe597c63a95063dc77","observation_id":"b498da15-0299-4c1c-964e-52520059d5c8","resolution":{"observed_at":"2026-08-01T10:42:36.815873Z","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":"2026-08-01T10:42:36.902236Z","title":"French , title =","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:36.902236Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:30875a44b539213c4bace7f957ec3b0fbd036b9259741189ea0fdbe568402ce3","observation_id":"2911d236-a25f-4d04-b852-0b579b7ad06b","resolution":{"observed_at":"2026-08-01T10:42:36.902236Z","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":"2026-08-01T10:42:36.976169Z","title":"Don't Stop Pretraining: Adapt Language Models to Domains and Tasks , booktitle =","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:36.976169Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:1a6eecf32b3ff51c22d214f1d9ecd9ffb0bf49263f5ac5573c593ced1e498844","observation_id":"0b9fc82f-75c8-4a3a-ad94-bc6bd2bf6f85","resolution":{"observed_at":"2026-08-01T10:42:36.976169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01375","last_updated":"2024-06-03T14:40:31Z","snapshot_observed_at":"2026-08-03T14:53:38.066418Z","submitted_at":"2024-06-03T14:40:31Z","title":"D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01375","snapshot_observed_at":"2026-08-01T10:42:37.077298Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:37.077298Z"},"links":{"cited_paper":"/paper/2406.01375","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:6e95a743f460c1037c1a94f122009d3510f1fe676b626d82f312f75073687693","observation_id":"99f93e6a-c6c4-410d-9d37-7a1d6681e838","resolution":{"observed_at":"2026-08-01T10:42:37.077298Z","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":"2026-08-01T10:42:37.155902Z","title":"Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:37.155902Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:59a30fad9e7a3d7a8aa67dddc98560adecaf7ebe81bafb2d7daa641b216f9e3c","observation_id":"d8bb39ec-05e0-4511-99db-d98c06b3f4f5","resolution":{"observed_at":"2026-08-01T10:42:37.155902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.06624","last_updated":"2026-04-29T15:56:44Z","snapshot_observed_at":"2026-07-06T19:13:16.067395Z","submitted_at":"2024-09-10T16:26:43Z","title":"A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.06624","snapshot_observed_at":"2026-08-01T10:42:37.269767Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:37.269767Z"},"links":{"cited_paper":"/paper/2409.06624","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:ac78b3902c1fe20fddd8cd33f1c641d61ff648428584b45c0fd0337933e18618","observation_id":"416b8a08-a33f-4710-a2db-18ba5e2dd20b","resolution":{"observed_at":"2026-08-01T10:42:37.269767Z","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":"2026-08-01T10:42:37.436480Z","title":"International Conference on Learning Representations , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:37.436480Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:b8e5cd4f500ac788b488d89f509a0157c2118cfb6734e5ad8f32de40c96cfa63","observation_id":"b222587a-b2cc-4fb1-809c-de46f66724da","resolution":{"observed_at":"2026-08-01T10:42:37.436480Z","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":"2026-08-01T10:42:37.629309Z","title":"Proceedings of the 42nd International Conference on Machine Learning , series =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:37.629309Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:9907b7fcb9a23efbf30c260df965c983596eec36640b206067f2c8ff6b058f9f","observation_id":"e71e7fcf-2e47-40de-8eae-506038b2a3ba","resolution":{"observed_at":"2026-08-01T10:42:37.629309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.21751","last_updated":"2026-07-15T23:57:38Z","snapshot_observed_at":"2026-08-02T13:34:12.988897Z","submitted_at":"2026-05-20T21:25:41Z","title":"Models Can Model, But Can't Bind: Structured Grounding in Text-to-Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.21751","snapshot_observed_at":"2026-08-01T10:42:37.752074Z","title":"Bastian and Frederic Sala , title =","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:37.752074Z"},"links":{"cited_paper":"/paper/2605.21751","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:85f1fee6827af99cef4c60ffab2a589ccf268bd3fe59dccd3f77106fae2a7ead","observation_id":"380a2f7e-98a9-431c-a51f-648c7fe4dc6c","resolution":{"observed_at":"2026-08-01T10:42:37.752074Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.25246","last_updated":"2026-05-30T22:51:15Z","snapshot_observed_at":"2026-08-02T13:42:34.770355Z","submitted_at":"2026-05-24T20:10:42Z","title":"FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.25246","snapshot_observed_at":"2026-08-01T10:42:37.861003Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:37.861003Z"},"links":{"cited_paper":"/paper/2605.25246","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:82fd26c51057fc8d490ebbcd81fa2ad059e7ee12296f8f1de368625da0321abe","observation_id":"e242bd29-f583-43fa-81d5-72eedc20f9f5","resolution":{"observed_at":"2026-08-01T10:42:37.861003Z","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":"2026-08-01T10:42:37.963864Z","title":"Proceedings of the 41st International Conference on Machine Learning , series =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:37.963864Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:faa44818d0c760c08be38d55050196f0ee541908ea64c087faf00bd36237e51b","observation_id":"23df44b5-0d74-4fe3-968a-94a6eff89c5c","resolution":{"observed_at":"2026-08-01T10:42:37.963864Z","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":"2026-08-01T10:42:38.057431Z","title":"2025 , howpublished =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:38.057431Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:6f22824f21d7be738aadfb13734536efb3c00bfeb23e6338d3255eb34f6c851f","observation_id":"f8940e7f-112c-4a7e-882b-7aa58bea7bd4","resolution":{"observed_at":"2026-08-01T10:42:38.057431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13144","last_updated":"2025-02-15T13:45:56Z","snapshot_observed_at":"2026-07-06T18:17:34.028834Z","submitted_at":"2024-05-21T18:29:54Z","title":"LLMs for Mathematical Modeling: Towards Bridging the Gap between Natural and Mathematical Languages","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13144","snapshot_observed_at":"2026-08-01T10:42:38.162881Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:38.162881Z"},"links":{"cited_paper":"/paper/2405.13144","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:6e538a826b7f8e2624003b8850042c4c1aef09aa632e964795dfd46183ca3593","observation_id":"385e47be-37ba-4c22-ae8d-6d6c5f3ea384","resolution":{"observed_at":"2026-08-01T10:42:38.162881Z","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":"2026-08-01T10:42:38.275491Z","title":"2026 , note =","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:38.275491Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:b0a14961d03ff42441c4fcb678b3b722a8d259630551910c7ed390cd5f682138","observation_id":"cf9773a1-cc42-423c-bd10-39213bc92e38","resolution":{"observed_at":"2026-08-01T10:42:38.275491Z","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":"2026-08-01T10:42:38.428107Z","title":"2024 , howpublished =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:38.428107Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:e134b17d702e4b6a6bc123933b2833080679c7cd9d8836cd93b4451667fcb240","observation_id":"9a0b9cba-3d46-40ff-b8ef-69143f4f2f5f","resolution":{"observed_at":"2026-08-01T10:42:38.428107Z","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":"2026-08-01T10:42:38.539823Z","title":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:38.539823Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:fedc52220f3c7da9658671c32e63c431380c6a34d2a5bde3a8dff7ada6215bd0","observation_id":"8c536f30-fb4f-44f0-9160-81503247bc6b","resolution":{"observed_at":"2026-08-01T10:42:38.539823Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.14135","last_updated":"2022-06-23T17:53:32Z","snapshot_observed_at":"2026-07-06T13:14:48.753329Z","submitted_at":"2022-05-27T17:53:09Z","title":"FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.14135","snapshot_observed_at":"2026-08-01T10:42:38.706619Z","title":"Fu and Stefano Ermon and Atri Rudra and Christopher R\\'","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:38.706619Z"},"links":{"cited_paper":"/paper/2205.14135","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:4723a78eb9160ce207c40653ee1754c4a48cf4730a6ea415a2cbe3b9a0ab4f2a","observation_id":"7f1059b7-b842-469e-bd39-0481fada8adb","resolution":{"observed_at":"2026-08-01T10:42:38.706619Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.08691","last_updated":"2023-07-17T17:50:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-17T17:50:36Z","title":"FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.08691","snapshot_observed_at":"2026-08-01T10:42:38.861814Z","title":"The Twelfth International Conference on Learning Representations (ICLR 2024) , year =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:38.861814Z"},"links":{"cited_paper":"/paper/2307.08691","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:8a5dbabd5cd0eca70833c654a6919c125e0d82bc168f7be206c6ad1f15d2ba6d","observation_id":"ad47effb-6913-4034-9b36-084bf8e83cc7","resolution":{"observed_at":"2026-08-01T10:42:38.861814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.08608","last_updated":"2024-07-12T22:15:02Z","snapshot_observed_at":"2026-07-06T18:44:53.587276Z","submitted_at":"2024-07-11T15:44:48Z","title":"FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.08608","snapshot_observed_at":"2026-08-01T10:42:38.990693Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:38.990693Z"},"links":{"cited_paper":"/paper/2407.08608","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:cca5755eb8bde887a3515d72ee6ad5cbe6292ebfdaaaed58b136d170541098c4","observation_id":"44ab689c-0f5a-4fd3-9dbb-5236b8176c5f","resolution":{"observed_at":"2026-08-01T10:42:38.990693Z","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":"2026-08-01T10:42:39.156942Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:39.156942Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:1b5a1a1a83311614db2c0cd045c1256c105ac80b3c297b1fbc360218c746daf7","observation_id":"9f9ef4d6-2709-4bc6-b948-23ba6defbbbd","resolution":{"observed_at":"2026-08-01T10:42:39.156942Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10989","last_updated":"2025-01-24T00:14:55Z","snapshot_observed_at":"2026-07-06T19:33:26.078722Z","submitted_at":"2024-10-14T18:17:01Z","title":"Liger Kernel: Efficient Triton Kernels for LLM Training","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10989","snapshot_observed_at":"2026-08-01T10:42:39.291065Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:39.291065Z"},"links":{"cited_paper":"/paper/2410.10989","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:ac39c061d1b7f7f32279e7a418d48b814526c0eddaca1ae9f9de344296be701e","observation_id":"ac74de85-89ad-49d8-82c2-2622d1e69176","resolution":{"observed_at":"2026-08-01T10:42:39.291065Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.01005","last_updated":"2025-04-21T20:10:11Z","snapshot_observed_at":"2026-07-06T20:15:36.280948Z","submitted_at":"2025-01-02T02:02:20Z","title":"FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.01005","snapshot_observed_at":"2026-08-01T10:42:39.443501Z","title":"Proceedings of Machine Learning and Systems 7 (MLSys 2025) , year =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:39.443501Z"},"links":{"cited_paper":"/paper/2501.01005","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:2e2b52fb2e2833c059294056c83471dc33cd5baf643ab6e4d787349b344b0c52","observation_id":"63e72ea7-bfb6-4f7e-927c-625aa377e615","resolution":{"observed_at":"2026-08-01T10:42:39.443501Z","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":"2026-08-01T10:42:39.608074Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:39.608074Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:27618a8bd01b2a3ac16cc7cb0333f3af965c8612531dfd2597c077d5b4ebe094","observation_id":"fc494aab-5587-4a4e-bc24-02bb551dc123","resolution":{"observed_at":"2026-08-01T10:42:39.608074Z","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":"2026-08-01T10:42:39.749155Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:39.749155Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:3dfbb322da75eae06c576a81037e969505ca3bcf903538350e931c5d5415301e","observation_id":"099871ff-4128-4c28-9e05-6b5b26590d0b","resolution":{"observed_at":"2026-08-01T10:42:39.749155Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.00535","last_updated":"2025-05-12T10:24:14Z","snapshot_observed_at":"2026-07-06T19:59:28.832182Z","submitted_at":"2024-11-30T16:58:42Z","title":"FullStack Bench: Evaluating LLMs as Full Stack Coders","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.00535","snapshot_observed_at":"2026-08-01T10:42:39.936078Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:39.936078Z"},"links":{"cited_paper":"/paper/2412.00535","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:a6d35cc15b2cfdd7c2f0620b6b984b01ca187b9a06909b906645a1fc9882f3a1","observation_id":"00da7b69-8be1-4464-ae4f-fdaf423434fb","resolution":{"observed_at":"2026-08-01T10:42:39.936078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2603.23566","last_updated":"2026-05-16T02:26:58Z","snapshot_observed_at":"2026-07-06T22:50:23.931381Z","submitted_at":"2026-03-24T08:54:53Z","title":"AscendOptimizer: Episodic Agent for Ascend NPU Operator Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.23566","snapshot_observed_at":"2026-08-01T10:42:40.072916Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:40.072916Z"},"links":{"cited_paper":"/paper/2603.23566","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:c15c23c86f44a235cd29e8d8ce7d33d3c8fcd626eb0cfec7e15b4881ccf18ed7","observation_id":"8f70b291-131c-4bcb-af34-b4b1ede46179","resolution":{"observed_at":"2026-08-01T10:42:40.072916Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.07160","last_updated":"2026-04-17T02:12:07Z","snapshot_observed_at":"2026-07-06T22:41:24.948774Z","submitted_at":"2026-01-12T03:12:58Z","title":"AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.07160","snapshot_observed_at":"2026-08-01T10:42:40.202768Z","title":"2601.07160 , archivePrefix=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:40.202768Z"},"links":{"cited_paper":"/paper/2601.07160","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:ecf1306a38362bb9b126186163463b6b9fc40db9690c349bf716c96a91f0076a","observation_id":"30c95f43-8e8d-4f2c-9a16-6f9a634db1b9","resolution":{"observed_at":"2026-08-01T10:42:40.202768Z","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":"2026-08-01T10:42:40.328346Z","title":"2601.22760 , archivePrefix=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:40.328346Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:0ecdf58cb1331353695c696afa055e78fa4585b9b4a03960c77429188c7367c0","observation_id":"3e6e1acb-a462-42c0-bf8b-c6f28c6cd6a1","resolution":{"observed_at":"2026-08-01T10:42:40.328346Z","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":"2026-08-01T10:42:40.451257Z","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence , pages =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:40.451257Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:381f2a05df113214208dfe005837d55d0f706f4542660711d265289d71975571","observation_id":"43d178ee-cc4e-497d-b8a4-f68008f42d0e","resolution":{"observed_at":"2026-08-01T10:42:40.451257Z","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":"2026-08-01T10:42:40.613279Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:40.613279Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:05619469cccf2fc152f85039886166127effc8a5495eac5f66c604fcfa62c7f3","observation_id":"9f1727a9-d708-476f-b026-8f3a487ae30c","resolution":{"observed_at":"2026-08-01T10:42:40.613279Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2512.23978","last_updated":"2026-05-16T18:48:22Z","snapshot_observed_at":"2026-08-04T06:10:06.211828Z","submitted_at":"2025-12-30T04:24:06Z","title":"Assured autonomy: How operations research powers and orchestrates generative AI systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2512.23978","snapshot_observed_at":"2026-08-01T10:42:40.769321Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:40.769321Z"},"links":{"cited_paper":"/paper/2512.23978","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:19620d725bb3ab99bd8e647945443174b2de89cab5189487ab90889ff768365f","observation_id":"dab65f1b-0784-4b09-b9fd-f8ea70212a38","resolution":{"observed_at":"2026-08-01T10:42:40.769321Z","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":"2026-08-01T10:42:40.933327Z","title":"Chain-of-Experts: When","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:40.933327Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:4951505c11bcd5c33debc6ce49498e89cb88baeaf29edf83347f5448b91c6293","observation_id":"5a7c6e1c-cff2-41d6-8c11-e59427dd4536","resolution":{"observed_at":"2026-08-01T10:42:40.933327Z","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":"2026-08-01T10:42:41.061243Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:41.061243Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:0c0fb61b4cdd77b33eb911434adb6e91f9753c80c24355610945eaf3ae60e920","observation_id":"9a8deb74-f374-46f6-a84a-19af9fcb5fc3","resolution":{"observed_at":"2026-08-01T10:42:41.061243Z","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":"2026-08-01T10:42:41.166854Z","title":"2026 , url =","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:41.166854Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:ef36407330c3b5aadbd1d6bb9a423b789eb63e4e44b316e72d5a8d2bc5769022","observation_id":"d4dd7da5-8ba9-4bd6-b4c0-830aaa0bed47","resolution":{"observed_at":"2026-08-01T10:42:41.166854Z","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":"2026-08-01T10:42:41.294341Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:41.294341Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:ecb1ab3b0a55bd014a80b33ed563e7cf3fc05930fc400eb8b371d8cef108039a","observation_id":"2b463597-5de0-43e7-9562-384e0d5bc831","resolution":{"observed_at":"2026-08-01T10:42:41.294341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.20073","last_updated":"2025-05-26T17:19:30Z","snapshot_observed_at":"2026-07-06T21:15:59.063396Z","submitted_at":"2025-04-24T17:57:08Z","title":"RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.20073","snapshot_observed_at":"2026-08-01T10:42:41.407957Z","title":"arXiv preprint arXiv:2504.20073 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:41.407957Z"},"links":{"cited_paper":"/paper/2504.20073","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:8eaabc9c01c433dd2a9dd67effdec6bfcb2f6c282c5147002507d463b86b01b7","observation_id":"b7cbb21b-0bbc-453e-9235-562f0b29ee0c","resolution":{"observed_at":"2026-08-01T10:42:41.407957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.09516","last_updated":"2025-08-05T19:08:38Z","snapshot_observed_at":"2026-07-06T20:51:28.022519Z","submitted_at":"2025-03-12T16:26:39Z","title":"Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.09516","snapshot_observed_at":"2026-08-01T10:42:41.553486Z","title":"arXiv preprint arXiv:2503.09516 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:41.553486Z"},"links":{"cited_paper":"/paper/2503.09516","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:632fe7ff7da81c2ddc475ca30bbab8947a86fd274960e5ef2a04cd9c441bfb3c","observation_id":"4d3d733f-3125-44cb-85c9-e1b6c0bdc1c9","resolution":{"observed_at":"2026-08-01T10:42:41.553486Z","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":"2026-08-01T10:42:41.659929Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:41.659929Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:01b7bde27b817a4abe69f497b179d2f69e988a94636fce5941742e3da77cb686","observation_id":"e20b470a-1b56-46e8-be0d-e40b7a3a415f","resolution":{"observed_at":"2026-08-01T10:42:41.659929Z","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":"2026-08-01T10:42:41.763344Z","title":"Findings of the Association for Computational Linguistics: EMNLP 2025 , pages=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:41.763344Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:400a8cd7f16bba92cb9f1381e47c36f3f1f2075ae6ace8e571d160b1e170be9c","observation_id":"a3ca89dd-67e0-4436-8f0c-67f71f67dd6e","resolution":{"observed_at":"2026-08-01T10:42:41.763344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.26132","last_updated":"2026-05-20T17:26:10Z","snapshot_observed_at":"2026-08-01T16:46:46.937730Z","submitted_at":"2026-05-20T17:26:10Z","title":"Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.26132","snapshot_observed_at":"2026-08-01T10:42:41.859432Z","title":"arXiv preprint arXiv:2605.26132 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:41.859432Z"},"links":{"cited_paper":"/paper/2605.26132","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:98432e7bf75067579fee4b8e6fd87899d289cb6869a70daa544ee32e9de92dbd","observation_id":"dc1d0779-6de3-4648-a384-1f85d88e5cc3","resolution":{"observed_at":"2026-08-01T10:42:41.859432Z","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":"2026-08-01T10:42:41.973882Z","title":"Findings of the Association for Computational Linguistics: EMNLP 2025 , pages=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:41.973882Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:b275a5666cef9183dfb22d1a4f2880fa49d7a18490d7421d54657c5445713d1c","observation_id":"abdf7e4e-c888-4ede-8e7c-d2db27fb109c","resolution":{"observed_at":"2026-08-01T10:42:41.973882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.03469","last_updated":"2025-05-21T06:17:56Z","snapshot_observed_at":"2026-08-06T16:07:27.746663Z","submitted_at":"2025-05-06T12:18:11Z","title":"Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.03469","snapshot_observed_at":"2026-08-01T10:42:42.084760Z","title":"arXiv preprint arXiv:2505.03469 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:42.084760Z"},"links":{"cited_paper":"/paper/2505.03469","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:13503d7d358c28ec5a658231eae1b0b274ccfb706c82e5633be619baf8421747","observation_id":"081183ca-e9d8-4ee0-ab2c-bb958653f164","resolution":{"observed_at":"2026-08-01T10:42:42.084760Z","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":"2026-08-01T10:42:42.216041Z","title":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:42.216041Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:dcf9c3f59d05938bec95f79854c31fe8575d73594dfbba63cd74835ccab7cfa5","observation_id":"9fa4c218-5ace-4029-a7dc-c89356643ed4","resolution":{"observed_at":"2026-08-01T10:42:42.216041Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.03373","last_updated":"2025-02-05T17:13:32Z","snapshot_observed_at":"2026-07-06T20:31:41.231839Z","submitted_at":"2025-02-05T17:13:32Z","title":"Demystifying Long Chain-of-Thought Reasoning in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.03373","snapshot_observed_at":"2026-08-01T10:42:42.355955Z","title":"arXiv preprint arXiv:2502.03373 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:42.355955Z"},"links":{"cited_paper":"/paper/2502.03373","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:e9183f9cd10f1bcc5532f3abcefb555ff7c0825186d44c759211e32d967a22ae","observation_id":"ef98c0ad-fcd7-4e87-932d-1480ab1027d9","resolution":{"observed_at":"2026-08-01T10:42:42.355955Z","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":"2026-08-01T10:42:42.469044Z","title":"Findings of the Association for Computational Linguistics: ACL 2024 , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:42.469044Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:d935a272637a9f55208c07ff03959031dd730da303d6b8d55f9d8b73c9714a9e","observation_id":"2e37fe7c-c8a6-4ea2-a4b5-d614faa5b3c6","resolution":{"observed_at":"2026-08-01T10:42:42.469044Z","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":"2026-08-01T10:42:42.566298Z","title":"arXiv preprint arXiv:2510.10071 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:42.566298Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:70fce3ac7fb4bf99666c75084ab5ef05835d05642ecced99ded34f6afe041148","observation_id":"f440cc73-8ee8-4458-8d52-2f187547da0a","resolution":{"observed_at":"2026-08-01T10:42:42.566298Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17400","last_updated":"2025-02-12T14:46:43Z","snapshot_observed_at":"2026-07-06T17:36:05.934286Z","submitted_at":"2024-02-27T10:47:24Z","title":"Investigating Continual Pretraining in Large Language Models: Insights and Implications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17400","snapshot_observed_at":"2026-08-01T10:42:42.659428Z","title":"arXiv preprint arXiv:2402.17400 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:42.659428Z"},"links":{"cited_paper":"/paper/2402.17400","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:e22f50c4a7aa021ca33d4f904559a423b4aaf3247a07c5e77f7c9ac812be46e5","observation_id":"fc374ee4-8db5-4941-94ef-28f47914c581","resolution":{"observed_at":"2026-08-01T10:42:42.659428Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2602.22859","last_updated":"2026-05-07T13:23:50Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-26T10:53:57Z","title":"From Blind Spots to Gains: Diagnostic-Driven Iterative Training for Large Multimodal Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2602.22859","snapshot_observed_at":"2026-08-01T10:42:42.797506Z","title":"arXiv preprint arXiv:2602.22859 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:42.797506Z"},"links":{"cited_paper":"/paper/2602.22859","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:66bee53a9e48f013773b6821ecbfff78c2965601cbfde521fd3bea2a4114a442","observation_id":"215f0dd9-9288-46a3-b800-061f4952be67","resolution":{"observed_at":"2026-08-01T10:42:42.797506Z","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":"2026-08-01T10:42:42.937048Z","title":"Lane , booktitle=","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:42.937048Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:7d971b613dc74ea902103b0de818b6b39d1e8804a8ae8f38a2a0e67cff159f67","observation_id":"b49d8a0b-7902-4577-b39e-fea2e11d5355","resolution":{"observed_at":"2026-08-01T10:42:42.937048Z","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":"2026-08-01T10:42:43.068810Z","title":"Forty-second International Conference on Machine Learning , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:43.068810Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:a59c0c60ddac9791e42a9646a94512f50190ac7af285bd301eadf9e459532f0e","observation_id":"49fd6153-1497-439b-b2a4-022511519601","resolution":{"observed_at":"2026-08-01T10:42:43.068810Z","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":"2026-08-01T10:42:43.124903Z","title":"Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations) , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:43.124903Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:d770805ea24f09e3c97f8175c76ec05c60a510e329829185ae80cccfe2b688ef","observation_id":"2a7033c5-e85e-4fc8-b70d-0421cbb52b88","resolution":{"observed_at":"2026-08-01T10:42:43.124903Z","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":"2026-08-01T10:42:43.194774Z","title":"ICML 2025 Workshop on Computer Use Agents , year=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:43.194774Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:41a12756db60271dd8f02b08dc9ef39612af47239073cd1f071f3e8450d00f59","observation_id":"ef5071dd-bc34-4973-b261-7779dca9f623","resolution":{"observed_at":"2026-08-01T10:42:43.194774Z","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":"2026-08-01T10:42:43.255095Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:43.255095Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:bd0b45898b990dcfd04ec42d9d62bd19aa4cdfe0db8294e12565bef4fbb6a842","observation_id":"831bc3c3-c25d-43c1-9916-595f77b77de7","resolution":{"observed_at":"2026-08-01T10:42:43.255095Z","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":"2026-08-01T10:42:43.332072Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:43.332072Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:e2850342b21acbcd88fe99593a839f9384e3d69d67e13a31b03e16b4db95f17c","observation_id":"a39531f3-d382-473b-9d7e-9436a9160120","resolution":{"observed_at":"2026-08-01T10:42:43.332072Z","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":"2026-08-01T10:42:43.476271Z","title":"Xing and Sham M","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:43.476271Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:1b35f45fa45b1498107cbc42b942d0377afa849137b94dd08287b8fa0dc9d4f9","observation_id":"be1ca4bd-3208-4026-be9f-395e38733f56","resolution":{"observed_at":"2026-08-01T10:42:43.476271Z","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":"2026-08-01T10:42:43.601381Z","title":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:43.601381Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:359480eb320b50a8f3b07cb90f88ee6146dc90076db52dc872f50d88eaad9fa2","observation_id":"5eb6b9ee-2964-4e3d-bf0b-36dbd861058a","resolution":{"observed_at":"2026-08-01T10:42:43.601381Z","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":"2026-08-01T10:42:43.715467Z","title":"2025 , eprint=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:43.715467Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:b2d2a50b137f25000ee7dbf77bb8c64d32a144ab24863edfc10a6ee3d847db51","observation_id":"c9bc9770-30b3-4df5-83ce-cfc5e46e8b78","resolution":{"observed_at":"2026-08-01T10:42:43.715467Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2602.15763","last_updated":"2026-02-24T10:44:44Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-02-17T17:50:56Z","title":"GLM-5: from Vibe Coding to Agentic Engineering","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2602.15763","snapshot_observed_at":"2026-08-01T10:42:43.806846Z","title":"2602.15763 , archivePrefix=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:43.806846Z"},"links":{"cited_paper":"/paper/2602.15763","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:b01b24d4b3eb5706e69071b7b3dcb8f71aa0db107c140ac57a83af7ed1bcf767","observation_id":"c4a85417-e82e-485d-8cbe-500e90f32ffa","resolution":{"observed_at":"2026-08-01T10:42:43.806846Z","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":"2026-08-01T10:42:43.937634Z","title":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:43.937634Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:68568df8192fd4038da968700285641515df799d6d7ffbbd83688dc35e46357e","observation_id":"eee5d0c8-66c9-4e4e-8fe6-68b2f258f44f","resolution":{"observed_at":"2026-08-01T10:42:43.937634Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.14971","last_updated":"2024-06-21T08:29:31Z","snapshot_observed_at":"2026-08-05T10:40:17.519980Z","submitted_at":"2024-06-21T08:29:31Z","title":"Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.14971","snapshot_observed_at":"2026-08-01T10:42:44.093552Z","title":"arXiv preprint arXiv:2406.14971 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:44.093552Z"},"links":{"cited_paper":"/paper/2406.14971","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:a804494c18c94088bd042e83dd274919ad71157758391e44719f063145512272","observation_id":"ede0abb8-06ae-41fe-9950-9982a24e22f9","resolution":{"observed_at":"2026-08-01T10:42:44.093552Z","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":"2026-08-01T10:42:44.133315Z","title":"International Conference on Learning Representations , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:44.133315Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:922dcb0fb624b204850f5c0dbcd69b30fe00638243c290cb57638c3acc4f4635","observation_id":"12b2fd59-b2b6-41a1-bd79-30176f71cf93","resolution":{"observed_at":"2026-08-01T10:42:44.133315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.08207","last_updated":"2022-03-17T17:53:01Z","snapshot_observed_at":"2026-07-06T11:58:21.596920Z","submitted_at":"2021-10-15T17:08:57Z","title":"Multitask Prompted Training Enables Zero-Shot Task Generalization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.08207","snapshot_observed_at":"2026-08-01T10:42:44.204080Z","title":"arXiv preprint arXiv:2110.08207 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:44.204080Z"},"links":{"cited_paper":"/paper/2110.08207","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:323446e259dce91629e0a55814e7c63cff35e3d4d08f5c05913e3863cd30e563","observation_id":"526d2e1e-e3f0-4863-b979-54a6f212851b","resolution":{"observed_at":"2026-08-01T10:42:44.204080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10739","last_updated":"2024-10-14T17:20:30Z","snapshot_observed_at":"2026-08-05T08:16:52.696697Z","submitted_at":"2024-10-14T17:20:30Z","title":"Balancing Continuous Pre-Training and Instruction Fine-Tuning: Optimizing Instruction-Following in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10739","snapshot_observed_at":"2026-08-01T10:42:44.262269Z","title":"arXiv preprint arXiv:2410.10739 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:44.262269Z"},"links":{"cited_paper":"/paper/2410.10739","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:d3cfd27e2cdfb7594da8e69a08db4dbc21528fc50e1bbfef2a971b8d73f60c85","observation_id":"35fdd07e-9b13-431a-8797-12a7a3ed843e","resolution":{"observed_at":"2026-08-01T10:42:44.262269Z","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":"2026-08-01T10:42:44.321204Z","title":"2024 , month =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:44.321204Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:48e4284e069fa89d87f6b3f48900512da506da0d75aaaaf11f522f0c87829bfc","observation_id":"c630c723-5371-471b-8def-cabeead4b7a2","resolution":{"observed_at":"2026-08-01T10:42:44.321204Z","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":"2026-08-01T10:42:44.348961Z","title":"2021 , eprint=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:44.348961Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:7fbdc46a3a20a8dcc96cc5fe9a66583f70e2d7b4229edac6127f794602cad619","observation_id":"674980c7-15b6-4354-9227-b7b401164277","resolution":{"observed_at":"2026-08-01T10:42:44.348961Z","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":"2026-08-01T10:42:44.479396Z","title":"2024 , journal =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:44.479396Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:b5825e69c0c58594d533999b042e2ec2c6faec4aed840e6555c746da26543fe6","observation_id":"9733e3d0-9b34-44c7-9442-a90667f5f927","resolution":{"observed_at":"2026-08-01T10:42:44.479396Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00213","last_updated":"2024-04-02T20:09:45Z","snapshot_observed_at":"2026-07-06T17:53:19.196215Z","submitted_at":"2024-03-30T01:56:07Z","title":"Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00213","snapshot_observed_at":"2026-08-01T10:42:44.601346Z","title":"arXiv preprint arXiv:2404.00213 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:44.601346Z"},"links":{"cited_paper":"/paper/2404.00213","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:9e5ec4ad93caafb49815258aa8eeb203d7821f6684a2b80c2bf0be4646b7e231","observation_id":"9b1d7e50-d6ea-42da-a779-d3d2020757fd","resolution":{"observed_at":"2026-08-01T10:42:44.601346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-08-06T14:58:42.911363Z","submitted_at":"2024-02-05T18:55:32Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-08-01T10:42:44.841286Z","title":"arXiv preprint arXiv:2402.03300 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:44.841286Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:01cebf72d90551d5a1c3b66e8104fbe523bf68d9f498e05b326f5399706f511b","observation_id":"ba80b6e8-93d5-4d1c-9416-0a83bc9468e8","resolution":{"observed_at":"2026-08-01T10:42:44.841286Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-01T10:42:45.054508Z","title":"arXiv preprint arXiv:1707.06347 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:45.054508Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:8f4677637aa17b2e9c9d7594f8f19fa0cee0a85ee59975a4632ac366f3b22896","observation_id":"2243f076-7255-4a67-b447-f5bd22ef75cf","resolution":{"observed_at":"2026-08-01T10:42:45.054508Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.13016","last_updated":"2026-04-15T17:48:51Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-14T17:54:28Z","title":"Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.13016","snapshot_observed_at":"2026-08-01T10:42:45.279835Z","title":"arXiv preprint arXiv:2604.13016 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:45.279835Z"},"links":{"cited_paper":"/paper/2604.13016","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:f164dbb6f0c04bd0857a5e8a77552e1a5847a34ba1c11f0e45b295fb8b83344a","observation_id":"f3356456-8aeb-45cc-9ad7-ebb5c79df6df","resolution":{"observed_at":"2026-08-01T10:42:45.279835Z","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":"2026-08-01T10:42:45.439960Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:45.439960Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:d594f2431e8eb22fca6afd7f8ff90f0027dd9ebe2c893f969eacdcf23f444407","observation_id":"eab94ea7-2f03-4a6d-9aad-5f69086fd2cb","resolution":{"observed_at":"2026-08-01T10:42:45.439960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.18292","last_updated":"2026-04-20T14:01:10Z","snapshot_observed_at":"2026-07-06T23:05:13.178333Z","submitted_at":"2026-04-20T14:01:10Z","title":"Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.18292","snapshot_observed_at":"2026-08-01T10:42:45.592909Z","title":"arXiv preprint arXiv:2604.18292 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:45.592909Z"},"links":{"cited_paper":"/paper/2604.18292","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:8b4f9eab427e7c3172f543f4baca6be8f2871695420faa6467c655e844c0335b","observation_id":"f6633865-6df6-43b4-933d-93d8433465ff","resolution":{"observed_at":"2026-08-01T10:42:45.592909Z","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":"2026-08-01T10:42:45.744353Z","title":"International Conference on Learning Representations , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:45.744353Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:da4d14e0dd655d268f3e6d06ef26615ed29e22e477ac72a3b3f41e5c49fe6c5e","observation_id":"81003c2e-060c-46ab-a2fc-515efcc9848e","resolution":{"observed_at":"2026-08-01T10:42:45.744353Z","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":"2026-08-01T10:42:45.952338Z","title":"International Conference on Learning Representations , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":94,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:45.952338Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:11f375eb22216da41ef54c9fa5227d173595488e0756cfb7d74e8e016cf1080e","observation_id":"e3addb22-6af7-47c0-8aa4-da72e40bf2aa","resolution":{"observed_at":"2026-08-01T10:42:45.952338Z","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":"2026-08-01T10:42:46.143917Z","title":"arXiv e-prints , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":95,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:46.143917Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:da5a7ba6210878de8f212b2d67de300b1ca05434e63f497739f0e6dfd8061b75","observation_id":"af92d206-c799-4da7-9524-5552aff8346c","resolution":{"observed_at":"2026-08-01T10:42:46.143917Z","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":"2026-08-01T10:42:46.334512Z","title":"Proceedings of the International Conference on Learning Representations (ICLR) , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":96,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:46.334512Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:5e811416c40805a60f21332b4a17a26057c13556fc0358aa1d7b385e87054451","observation_id":"1fa4eb63-ea05-4dad-81e5-2ffa56454117","resolution":{"observed_at":"2026-08-01T10:42:46.334512Z","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":"2026-08-01T10:42:46.503777Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":97,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:46.503777Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:305f0c811677885f199b801671b5f4777d07ab838561e0ec9914ffdbdaec8444","observation_id":"98c645b8-5d39-4ee5-bc5a-29126cc293e7","resolution":{"observed_at":"2026-08-01T10:42:46.503777Z","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":"2026-08-01T10:42:46.724457Z","title":"CMMLU : Measuring massive multitask language understanding in C hinese","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":98,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:46.724457Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:a3f69413beeb0c4cc3df1c904193cd8adab8ac044f74eaede4283fb727df0ced","observation_id":"8e600000-dad5-44ec-94a1-90b2dd47866e","resolution":{"observed_at":"2026-08-01T10:42:46.724457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-04T15:46:25.710484Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-08-01T10:42:46.854625Z","title":"arXiv preprint arXiv:2110.14168 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":99,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:46.854625Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:e73da5e20b33ad37f35131a2cf119ce3b6624235482a9d1de1eec32419dfe70c","observation_id":"462999da-7234-4ce3-baf2-cb147238f77f","resolution":{"observed_at":"2026-08-01T10:42:46.854625Z","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":"2026-08-01T10:42:46.984968Z","title":"2021 , eprint=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:46.984968Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:bfab26508e794862af32a83958d8e76bf6aaf3a27334bcea9d98d805c2319444","observation_id":"d9ae0598-52fa-4e3f-89e3-7e26bc30dbaf","resolution":{"observed_at":"2026-08-01T10:42:46.984968Z","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":"2026-08-01T10:42:47.058369Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","version":2},"reference_index":101,"source":"arxiv_source","source_observed_at":"2026-08-01T10:42:47.058369Z"},"links":{"citing_paper":"/paper/2607.20145"},"observation_digest":"sha256:8c0b18f268e98b0913f2fb42c605dfa9676acea3f0a60906cc17c36963b2d123","observation_id":"10fc9f2f-4c12-42de-a634-3b4f1154a4aa","resolution":{"observed_at":"2026-08-01T10:42:47.058369Z","resolver_source":null,"status":"parse_uncertain"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.20145","last_updated":"2026-07-30T09:30:44Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-03T18:47:24.558536Z","submitted_at":"2026-07-22T13:49:17Z","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":99,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":128},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 100 of 128 outbound references and 0 inbound Pith citation observations for arXiv:2607.20145."}