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Paper Citation Record · LEDGER

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD

As of 13 August 2026, this Paper Citation Record lists 100 of 128 outbound references and 0 inbound Pith citation observations for arXiv:2607.20145.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.20145 v2

Coverage vector

measured 100 of 128 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T10:42:47.058369Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

100 of 128 outbound references displayed

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  • verified fuzzy0
  • unresolved99
  • parse uncertain1
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6eefd2aa-ae09-4e8f-8c9a-17bfcd45177f · outbound

This paper cites YouTube-8M: A Large-Scale Video Classification Benchmark.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD YouTube-8M: A Large-Scale Video Classification Benchmark

Reference 1

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source=arxiv_source observed=2026-08-01T10:42:34.395951Z digest=sha256:0e21770865e0135ca63414ebb095e0342b206409a6d23d5e2b52b3213ed8ef87

Observation 8208a9ee-5e0a-436c-b810-72276e1534d7 · outbound

This paper cites Findings of the Association for Computational Linguistics: ACL 2025 , year =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Findings of the Association for Computational Linguistics: ACL 2025 , year =

Reference 2

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Observation 79b801c9-061f-4453-8d1b-24affb66e7fd · outbound

This paper cites , institution =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD , institution =

Reference 3

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Observation 154e4279-dc39-44ca-9731-d13c3e66f8ec · outbound

This paper cites an unresolved cited work.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Unresolved cited work

Reference 4

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source=arxiv_source observed=2026-08-01T10:42:34.756027Z digest=sha256:7aa539a7cbd3f7f5305ad0ad46719b30fb7a04c95eb3096edad8501a42ab9af0

Observation 04f19e51-a960-46d7-834f-551da8269f6b · outbound

This paper cites arXiv preprint arXiv:2602.09003 , year =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD arXiv preprint arXiv:2602.09003 , year =

Reference 5

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Observation 69397945-f4b1-4735-a598-37d86ec667d0 · outbound

This paper cites an unresolved cited work.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Unresolved cited work

Reference 6

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Observation d2d396f0-29b2-4c9e-a5e9-628f9f8a70c5 · outbound

This paper cites an unresolved cited work.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Unresolved cited work

Reference 7

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source=arxiv_source observed=2026-08-01T10:42:35.169126Z digest=sha256:a93cc917df71cfa8cb9e818da2468f0bd5a892863936e457ece868be0e466c57

Observation 3a5054b5-6eef-4019-9bd2-92c19f72735d · outbound

This paper cites an unresolved cited work.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Unresolved cited work

Reference 8

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source=arxiv_source observed=2026-08-01T10:42:35.250421Z digest=sha256:c34f04da8b3cdd7a42780ac7cde3f8a9de8b9b6f79c10016abcd0d11024cce4b

Observation b8ccaa0f-370c-4dd1-9201-89544f60ff63 · outbound

This paper cites Le and Geoffrey E.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Le and Geoffrey E

Reference 9

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Observation b9eb0a12-f3b2-430d-a395-10d991ef7626 · outbound

This paper cites 9th International Conference on Learning Representations, ICLR 2021 , publisher =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD 9th International Conference on Learning Representations, ICLR 2021 , publisher =

Reference 10

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source=arxiv_source observed=2026-08-01T10:42:35.452315Z digest=sha256:94d47f7b84b4678eeadbeb80cb5f78abca44ca9f21f844b25ae8d1f8e5acb836

Observation 52745584-23ac-4c7b-bb3c-a426d845b93b · outbound

This paper cites Journal of Machine Learning Research , volume =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Journal of Machine Learning Research , volume =

Reference 11

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Observation db183193-2bae-4f75-90c0-dea1d9eb7d75 · outbound

This paper cites Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2020 , pages =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2020 , pages =

Reference 13

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Observation e8d259df-08ea-40fd-a384-ac3c16a94b73 · outbound

This paper cites DeepSeek-V3 Technical Report.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD DeepSeek-V3 Technical Report

Reference 14

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Observation 20d5b6b3-ce37-4082-b824-c756f25ef0a8 · outbound

This paper cites Operations Research , volume =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Operations Research , volume =

Reference 15

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Observation a033cb60-25fa-4c53-8662-0b4b7f3c80c3 · outbound

This paper cites Thirty-Ninth AAAI Conference on Artificial Intelligence, AAAI 2025 , pages =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Thirty-Ninth AAAI Conference on Artificial Intelligence, AAAI 2025 , pages =

Reference 16

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Observation aa58dfc8-99dd-4924-9822-936c109b5ebe · outbound

This paper cites Proceedings of the NeurIPS 2022 Competitions Track , pages =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Proceedings of the NeurIPS 2022 Competitions Track , pages =

Reference 17

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Observation 4bf77e72-c30b-4597-ba83-15c495a34d12 · outbound

This paper cites CoRR , volume =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD CoRR , volume =

Reference 18

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Observation d5bb94f7-9dad-4d91-b8a7-dab5011bb25a · outbound

This paper cites 2606.19348 , archivePrefix=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD 2606.19348 , archivePrefix=

Reference 19

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Observation 9a5d2e95-95a4-4c9d-85f2-00f7c5b57e3f · outbound

This paper cites Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence , articleno =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence , articleno =

Reference 20

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Observation b498da15-0299-4c1c-964e-52520059d5c8 · outbound

This paper cites CoRR , volume =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD CoRR , volume =

Reference 21

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Observation 2911d236-a25f-4d04-b852-0b579b7ad06b · outbound

This paper cites French , title =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD French , title =

Reference 22

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Observation 0b9fc82f-75c8-4a3a-ad94-bc6bd2bf6f85 · outbound

This paper cites Don't Stop Pretraining: Adapt Language Models to Domains and Tasks , booktitle =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Don't Stop Pretraining: Adapt Language Models to Domains and Tasks , booktitle =

Reference 23

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Observation 99f93e6a-c6c4-410d-9d37-7a1d6681e838 · outbound

This paper cites D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models

Reference 24

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Observation d8bb39ec-05e0-4511-99db-d98c06b3f4f5 · outbound

This paper cites Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages =

Reference 25

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Observation 416b8a08-a33f-4710-a2db-18ba5e2dd20b · outbound

This paper cites A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio

Reference 26

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Observation b222587a-b2cc-4fb1-809c-de46f66724da · outbound

This paper cites International Conference on Learning Representations , volume=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD International Conference on Learning Representations , volume=

Reference 27

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Observation e71e7fcf-2e47-40de-8eae-506038b2a3ba · outbound

This paper cites Proceedings of the 42nd International Conference on Machine Learning , series =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Proceedings of the 42nd International Conference on Machine Learning , series =

Reference 28

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Observation 380a2f7e-98a9-431c-a51f-648c7fe4dc6c · outbound

This paper cites Models Can Model, But Can't Bind: Structured Grounding in Text-to-Optimization.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Models Can Model, But Can't Bind: Structured Grounding in Text-to-Optimization

Reference 29

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Observation e242bd29-f583-43fa-81d5-72eedc20f9f5 · outbound

This paper cites FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization

Reference 30

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Observation 23df44b5-0d74-4fe3-968a-94a6eff89c5c · outbound

This paper cites Proceedings of the 41st International Conference on Machine Learning , series =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Proceedings of the 41st International Conference on Machine Learning , series =

Reference 31

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Observation f8940e7f-112c-4a7e-882b-7aa58bea7bd4 · outbound

This paper cites 2025 , howpublished =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD 2025 , howpublished =

Reference 32

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Observation 385e47be-37ba-4c22-ae8d-6d6c5f3ea384 · outbound

This paper cites LLMs for Mathematical Modeling: Towards Bridging the Gap between Natural and Mathematical Languages.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD LLMs for Mathematical Modeling: Towards Bridging the Gap between Natural and Mathematical Languages

Reference 33

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Observation cf9773a1-cc42-423c-bd10-39213bc92e38 · outbound

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SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD 2026 , note =

Reference 34

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Observation 9a0b9cba-3d46-40ff-b8ef-69143f4f2f5f · outbound

This paper cites 2024 , howpublished =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD 2024 , howpublished =

Reference 35

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source=arxiv_source observed=2026-08-01T10:42:38.428107Z digest=sha256:9664263a2090ed1d4368302d0eb652e13a5609c83fb439758d02dc3d3cb6f94b

Observation 8c536f30-fb4f-44f0-9160-81503247bc6b · outbound

This paper cites Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 36

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Observation 7f1059b7-b842-469e-bd39-0481fada8adb · outbound

This paper cites FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

Reference 37

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source=arxiv_source observed=2026-08-01T10:42:38.706619Z digest=sha256:246ba887dcc158d4a5a0e7706e12b3fe41ad85352925b2197d3c33dadbb48407

Observation ad47effb-6913-4034-9b36-084bf8e83cc7 · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 38

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source=arxiv_source observed=2026-08-01T10:42:38.861814Z digest=sha256:d2564c2f8bb722f884179cfa8ac8f5dcdaeb7f6e1eb7dff5cb034efbd1667eac

Observation 44ab689c-0f5a-4fd3-9dbb-5236b8176c5f · outbound

This paper cites FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision

Reference 39

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source=arxiv_source observed=2026-08-01T10:42:38.990693Z digest=sha256:f2ccb12ba7c650f4a7d409f0cecd8091c8b197ff39db4360bea969cde0f8422c

Observation 9f9ef4d6-2709-4bc6-b948-23ba6defbbbd · outbound

This paper cites an unresolved cited work.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Unresolved cited work

Reference 40

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source=arxiv_source observed=2026-08-01T10:42:39.156942Z digest=sha256:a8205a79071f9d34aea5d70fa71eb04bf6e86450313033ae120ba8a01af2b341

Observation ac74de85-89ad-49d8-82c2-2622d1e69176 · outbound

This paper cites Liger Kernel: Efficient Triton Kernels for LLM Training.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Liger Kernel: Efficient Triton Kernels for LLM Training

Reference 41

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source=arxiv_source observed=2026-08-01T10:42:39.291065Z digest=sha256:ecb3c72e255d900e3ed1eec1eb7a5b89f8b4ae4a6dfc2f230486824da05a8a16

Observation 63e72ea7-bfb6-4f7e-927c-625aa377e615 · outbound

This paper cites FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving

Reference 42

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source=arxiv_source observed=2026-08-01T10:42:39.443501Z digest=sha256:b629c5ec842a5d1f9c907d05adaa267777d1512c40b39546277b7a4e6481c450

Observation fc494aab-5587-4a4e-bc24-02bb551dc123 · outbound

This paper cites CoRR , volume =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD CoRR , volume =

Reference 43

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source=arxiv_source observed=2026-08-01T10:42:39.608074Z digest=sha256:2857b1e046b84f2948b9666763f9c41a1d4f3e861dace64a72852fb1aaae3c5f

Observation 099871ff-4128-4c28-9e05-6b5b26590d0b · outbound

This paper cites CoRR , volume =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD CoRR , volume =

Reference 44

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source=arxiv_source observed=2026-08-01T10:42:39.749155Z digest=sha256:d0eff5eb92ddb8a0873701a23ea6df9b52259dd05ec8790ee40e1e0ba3dbd9c2

Observation 00da7b69-8be1-4464-ae4f-fdaf423434fb · outbound

This paper cites FullStack Bench: Evaluating LLMs as Full Stack Coders.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD FullStack Bench: Evaluating LLMs as Full Stack Coders

Reference 45

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source=arxiv_source observed=2026-08-01T10:42:39.936078Z digest=sha256:57f345caa97433f7d4518aa0ca7aeca1d453771b9aa20f2a058100828773cf2c

Observation 8f70b291-131c-4bcb-af34-b4b1ede46179 · outbound

This paper cites AscendOptimizer: Episodic Agent for Ascend NPU Operator Optimization.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD AscendOptimizer: Episodic Agent for Ascend NPU Operator Optimization

Reference 46

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no resolver link, observed 2026-08-01T10:42:40.072916Z

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source=arxiv_source observed=2026-08-01T10:42:40.072916Z digest=sha256:29247412ab51ab6216896562f0e3a5a54c5d9387b2f1f44c1330e922be39a293

Observation 30c95f43-8e8d-4f2c-9a16-6f9a634db1b9 · outbound

This paper cites AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units

Reference 47

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source=arxiv_source observed=2026-08-01T10:42:40.202768Z digest=sha256:79178daeb3272494de8bb29e49846961901099a683e3ec9e7b910775cff58562

Observation 3e6e1acb-a462-42c0-bf8b-c6f28c6cd6a1 · outbound

This paper cites 2601.22760 , archivePrefix=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD 2601.22760 , archivePrefix=

Reference 48

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no resolver link, observed 2026-08-01T10:42:40.328346Z

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source=arxiv_source observed=2026-08-01T10:42:40.328346Z digest=sha256:9d7defae0efdda5d9d86c5c3352daaaf5ae34e6abbfa14af626732400ec28fd4

Observation 43d178ee-cc4e-497d-b8a4-f68008f42d0e · outbound

This paper cites Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence , pages =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence , pages =

Reference 49

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source=arxiv_source observed=2026-08-01T10:42:40.451257Z digest=sha256:2f6008ec45ecf22e266e35550e7d3281feb7bca644c00fd268e2078dcccaed58

Observation 9f1727a9-d708-476f-b026-8f3a487ae30c · outbound

This paper cites CoRR , volume =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD CoRR , volume =

Reference 50

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source=arxiv_source observed=2026-08-01T10:42:40.613279Z digest=sha256:9d74ccaf6736a50fcac60bc9f788844d87650f47f1207023899ffce04ec77c38

Observation dab65f1b-0784-4b09-b9fd-f8ea70212a38 · outbound

This paper cites Assured autonomy: How operations research powers and orchestrates generative AI systems.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Assured autonomy: How operations research powers and orchestrates generative AI systems

Reference 51

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no resolver link, observed 2026-08-01T10:42:40.769321Z

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source=arxiv_source observed=2026-08-01T10:42:40.769321Z digest=sha256:d8034a26335657e0d5c82aeddf8a3c964f65190acd55226df2b2b255e41fe905

Observation 5a7c6e1c-cff2-41d6-8c11-e59427dd4536 · outbound

This paper cites Chain-of-Experts: When.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Chain-of-Experts: When

Reference 52

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source=arxiv_source observed=2026-08-01T10:42:40.933327Z digest=sha256:04c44558fcfbaadb491990b05f23cf14de9e34e0a562475e772a75484237c2a6

Observation 9a8deb74-f374-46f6-a84a-19af9fcb5fc3 · outbound

This paper cites an unresolved cited work.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Unresolved cited work

Reference 53

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source=arxiv_source observed=2026-08-01T10:42:41.061243Z digest=sha256:5846389bccb60cb6a6813bfa22be96e26b6081cd875771e95cae4c2842681241

Observation d4dd7da5-8ba9-4bd6-b4c0-830aaa0bed47 · outbound

This paper cites 2026 , url =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD 2026 , url =

Reference 54

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source=arxiv_source observed=2026-08-01T10:42:41.166854Z digest=sha256:4a18a63e0705cb7e8dbc407af0ea420341b7b8655e2ce777f1f4bdfda5370594

Observation 2b463597-5de0-43e7-9562-384e0d5bc831 · outbound

This paper cites an unresolved cited work.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Unresolved cited work

Reference 55

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source=arxiv_source observed=2026-08-01T10:42:41.294341Z digest=sha256:7c1e32abfb4d51f32b542866038a37c0f5173f6a38ba78a148c7edf2b778929d

Observation b7cbb21b-0bbc-453e-9235-562f0b29ee0c · outbound

This paper cites RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Reference 56

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source=arxiv_source observed=2026-08-01T10:42:41.407957Z digest=sha256:9c1b34e004127c12135c03e0d93d2f212ac4d36bf223eec72bff376edcb3f4f6

Observation 4d3d733f-3125-44cb-85c9-e1b6c0bdc1c9 · outbound

This paper cites Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 57

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source=arxiv_source observed=2026-08-01T10:42:41.553486Z digest=sha256:ea57977af6e8c4022c46506ffbd12fc179b7f04fc8db5de41f50e7aa18320916

Observation e20b470a-1b56-46e8-be0d-e40b7a3a415f · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 58

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source=arxiv_source observed=2026-08-01T10:42:41.659929Z digest=sha256:078fbe92cbd6d7fbedc92bd3f93377f947974402e63b63d62c5f636bd75d5645

Observation a3ca89dd-67e0-4436-8f0c-67f71f67dd6e · outbound

This paper cites Findings of the Association for Computational Linguistics: EMNLP 2025 , pages=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Findings of the Association for Computational Linguistics: EMNLP 2025 , pages=

Reference 59

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source=arxiv_source observed=2026-08-01T10:42:41.763344Z digest=sha256:e9850ef6b53592bc0e70fabbbed6a4fb767b68e734ff3308a7cfc15c035dac10

Observation dc1d0779-6de3-4648-a384-1f85d88e5cc3 · outbound

This paper cites Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline

Reference 60

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source=arxiv_source observed=2026-08-01T10:42:41.859432Z digest=sha256:b5067cb640b77e6be98e0121bf8120e3e951ebf78705c27dc105bc68217feca7

Observation abdf7e4e-c888-4ede-8e7c-d2db27fb109c · outbound

This paper cites Findings of the Association for Computational Linguistics: EMNLP 2025 , pages=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Findings of the Association for Computational Linguistics: EMNLP 2025 , pages=

Reference 61

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source=arxiv_source observed=2026-08-01T10:42:41.973882Z digest=sha256:c546fade706d16ec5c1140790a6dab7937dc9719c892db5c76fb3aa658d3d1d7

Observation 081183ca-e9d8-4ee0-ab2c-bb958653f164 · outbound

This paper cites Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models

Reference 62

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source=arxiv_source observed=2026-08-01T10:42:42.084760Z digest=sha256:784d4709e587ee4e01262b4ca7af1fd18c707d02ea599a5315abc6abfca5a0ad

Observation 9fa4c218-5ace-4029-a7dc-c89356643ed4 · outbound

This paper cites Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 63

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source=arxiv_source observed=2026-08-01T10:42:42.216041Z digest=sha256:b9e6db9578e0bc25df0fbc90cbe6aa001e20ba3ffc8c0d68fe6fcac161eaba08

Observation ef98c0ad-fcd7-4e87-932d-1480ab1027d9 · outbound

This paper cites Demystifying Long Chain-of-Thought Reasoning in LLMs.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Demystifying Long Chain-of-Thought Reasoning in LLMs

Reference 64

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source=arxiv_source observed=2026-08-01T10:42:42.355955Z digest=sha256:31325daedd77661c8de2696ebafe1caecfd52eb5da48d41819d4766bb36cc8b0

Observation 2e37fe7c-c8a6-4ea2-a4b5-d614faa5b3c6 · outbound

This paper cites Findings of the Association for Computational Linguistics: ACL 2024 , pages=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Findings of the Association for Computational Linguistics: ACL 2024 , pages=

Reference 65

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source=arxiv_source observed=2026-08-01T10:42:42.469044Z digest=sha256:f97cf6649251bc93fab7a37271a2aac08120559d03896c28a5cd50542d7a81eb

Observation f440cc73-8ee8-4458-8d52-2f187547da0a · outbound

This paper cites arXiv preprint arXiv:2510.10071 , year=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD arXiv preprint arXiv:2510.10071 , year=

Reference 66

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source=arxiv_source observed=2026-08-01T10:42:42.566298Z digest=sha256:b5c3a28ee929f2c80cd5d2f4091f2ac710af33bbbfb18e65d2fe097237543d3e

Observation fc374ee4-8db5-4941-94ef-28f47914c581 · outbound

This paper cites Investigating Continual Pretraining in Large Language Models: Insights and Implications.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Investigating Continual Pretraining in Large Language Models: Insights and Implications

Reference 67

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source=arxiv_source observed=2026-08-01T10:42:42.659428Z digest=sha256:4b3715c37ccbb2cb421b0552bff231befc5e5bd1709eec929f4225a39f915666

Observation 215f0dd9-9288-46a3-b800-061f4952be67 · outbound

This paper cites From Blind Spots to Gains: Diagnostic-Driven Iterative Training for Large Multimodal Models.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD From Blind Spots to Gains: Diagnostic-Driven Iterative Training for Large Multimodal Models

Reference 68

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source=arxiv_source observed=2026-08-01T10:42:42.797506Z digest=sha256:ba4cfeb1fac50ce9bf32d20305fb925bbb389e84b439b96b08e0c3024fe19d25

Observation b49d8a0b-7902-4577-b39e-fea2e11d5355 · outbound

This paper cites Lane , booktitle=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Lane , booktitle=

Reference 69

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source=arxiv_source observed=2026-08-01T10:42:42.937048Z digest=sha256:d6697df73c94ed77547ae834a7c7f8567e698c565a0e3b300bc8a277f9934558

Observation 49fd6153-1497-439b-b2a4-022511519601 · outbound

This paper cites Forty-second International Conference on Machine Learning , year=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Forty-second International Conference on Machine Learning , year=

Reference 70

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source=arxiv_source observed=2026-08-01T10:42:43.068810Z digest=sha256:c24a56e8eeb87c12b09af4ed5482ed3cbc7615443ccd127ff0b78f79ae2ec7dd

Observation 2a7033c5-e85e-4fc8-b70d-0421cbb52b88 · outbound

This paper cites Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations) , pages=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations) , pages=

Reference 71

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source=arxiv_source observed=2026-08-01T10:42:43.124903Z digest=sha256:cf819c4f192c92559193c3ead902389598546b24a49033ba1171e0bb82a012aa

Observation ef5071dd-bc34-4973-b261-7779dca9f623 · outbound

This paper cites ICML 2025 Workshop on Computer Use Agents , year=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD ICML 2025 Workshop on Computer Use Agents , year=

Reference 72

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source=arxiv_source observed=2026-08-01T10:42:43.194774Z digest=sha256:21b84fe464301e9bbcbde280bcbf2f68b192a5fcd13aa6b44dcab8e76138aaa5

Observation 831bc3c3-c25d-43c1-9916-595f77b77de7 · outbound

This paper cites an unresolved cited work.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Unresolved cited work

Reference 73

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source=arxiv_source observed=2026-08-01T10:42:43.255095Z digest=sha256:e132428889cd69df6ef9423b918e9bedd30e6f03a02e0d90a501556bc1588103

Observation a39531f3-d382-473b-9d7e-9436a9160120 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Advances in Neural Information Processing Systems , volume=

Reference 74

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source=arxiv_source observed=2026-08-01T10:42:43.332072Z digest=sha256:bdec1b14a831ef58edb1528189df8ed74b8a419ee58b5ff496e6a5239d4f1b7f

Observation be1ca4bd-3208-4026-be9f-395e38733f56 · outbound

This paper cites Xing and Sham M.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Xing and Sham M

Reference 75

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source=arxiv_source observed=2026-08-01T10:42:43.476271Z digest=sha256:3967cbda83f83713ac33e48db1ba4f32216c7aa70ad7429da40282484a8e9bcb

Observation 5eb6b9ee-2964-4e3d-bf0b-36dbd861058a · outbound

This paper cites Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V

Reference 76

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source=arxiv_source observed=2026-08-01T10:42:43.601381Z digest=sha256:b61a9e8571d5ee81fd2cab3c30501b08f8ea2dbe6323f7539e64c6dfb7855351

Observation c9bc9770-30b3-4df5-83ce-cfc5e46e8b78 · outbound

This paper cites 2025 , eprint=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD 2025 , eprint=

Reference 77

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source=arxiv_source observed=2026-08-01T10:42:43.715467Z digest=sha256:e25214a07d5f728539a5666f75450ab8396fec2e29aa0d3f78b697802eb43c94

Observation c4a85417-e82e-485d-8cbe-500e90f32ffa · outbound

This paper cites GLM-5: from Vibe Coding to Agentic Engineering.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD GLM-5: from Vibe Coding to Agentic Engineering

Reference 78

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source=arxiv_source observed=2026-08-01T10:42:43.806846Z digest=sha256:c9bfc943dafc5f40689a9fdc123abae6962406b1a38d299a10d2249c47907f38

Observation eee5d0c8-66c9-4e4e-8fe6-68b2f258f44f · outbound

This paper cites Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 79

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source=arxiv_source observed=2026-08-01T10:42:43.937634Z digest=sha256:b51b6043ba0848a0031b0579cc45a12ba95b8688fabecd3dc60c06910c0539d2

Observation ede0abb8-06ae-41fe-9950-9982a24e22f9 · outbound

This paper cites Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation

Reference 80

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source=arxiv_source observed=2026-08-01T10:42:44.093552Z digest=sha256:a35365d1eb4db6a34cbbbe9bc9bf6a66d19abc92caf2eaab95f226cff03fd597

Observation 12b2fd59-b2b6-41a1-bd79-30176f71cf93 · outbound

This paper cites International Conference on Learning Representations , year=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD International Conference on Learning Representations , year=

Reference 81

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no resolver link, observed 2026-08-01T10:42:44.133315Z

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source=arxiv_source observed=2026-08-01T10:42:44.133315Z digest=sha256:c86ea79e7183700757f7a4400c7f8becc3384299f2706c72e66c505a05986407

Observation 526d2e1e-e3f0-4863-b979-54a6f212851b · outbound

This paper cites Multitask Prompted Training Enables Zero-Shot Task Generalization.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Multitask Prompted Training Enables Zero-Shot Task Generalization

Reference 82

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source=arxiv_source observed=2026-08-01T10:42:44.204080Z digest=sha256:c7e31f607722ac7173b447662ec925d441e9e88cc43ee02a80cc94699135fdaf

Observation 35fdd07e-9b13-431a-8797-12a7a3ed843e · outbound

This paper cites Balancing Continuous Pre-Training and Instruction Fine-Tuning: Optimizing Instruction-Following in LLMs.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Balancing Continuous Pre-Training and Instruction Fine-Tuning: Optimizing Instruction-Following in LLMs

Reference 83

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source=arxiv_source observed=2026-08-01T10:42:44.262269Z digest=sha256:7bf8ccba187a5d04c9c08c82e904781af7be9aba36d28fe7b8464afe2d065ad3

Observation c630c723-5371-471b-8def-cabeead4b7a2 · outbound

This paper cites 2024 , month =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD 2024 , month =

Reference 84

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source=arxiv_source observed=2026-08-01T10:42:44.321204Z digest=sha256:f7dc944f66ac20b2a2758924fe75ca698a8720fb99643c5d7bf71d7dac4da290

Observation 674980c7-15b6-4354-9227-b7b401164277 · outbound

This paper cites 2021 , eprint=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD 2021 , eprint=

Reference 85

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source=arxiv_source observed=2026-08-01T10:42:44.348961Z digest=sha256:08fd27a2a8546e7994166415189e5ad9960a81ade4a7ed60c340d48d68c44575

Observation 9733e3d0-9b34-44c7-9442-a90667f5f927 · outbound

This paper cites 2024 , journal =.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD 2024 , journal =

Reference 86

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source=arxiv_source observed=2026-08-01T10:42:44.479396Z digest=sha256:48dfbef388e0a2244d54bfaeb4a2f3959ae2a8128f725b9f0cd55cc1187e6c1b

Observation 9b1d7e50-d6ea-42da-a779-d3d2020757fd · outbound

This paper cites Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning

Reference 87

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source=arxiv_source observed=2026-08-01T10:42:44.601346Z digest=sha256:eb410831a9c7feb48de1679c17971118d5b278a2cb8b242c45c19f07e87846da

Observation ba80b6e8-93d5-4d1c-9416-0a83bc9468e8 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 88

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source=arxiv_source observed=2026-08-01T10:42:44.841286Z digest=sha256:740aed515bea19b0efcf2343e32d6475d2010e8a3293390bd73ece708edc7fa8

Observation 2243f076-7255-4a67-b447-f5bd22ef75cf · outbound

This paper cites Proximal Policy Optimization Algorithms.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Proximal Policy Optimization Algorithms

Reference 89

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source=arxiv_source observed=2026-08-01T10:42:45.054508Z digest=sha256:b33c89de9d2024b77d0586ad6998fb13eefdb6f40d9b2efb01250602b0524e32

Observation f3356456-8aeb-45cc-9ad7-ebb5c79df6df · outbound

This paper cites Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe

Reference 90

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source=arxiv_source observed=2026-08-01T10:42:45.279835Z digest=sha256:c383b1c628986ae87f2c4e31217de5f3507c1bbbcfc11388924f7280aec9cb2a

Observation eab94ea7-2f03-4a6d-9aad-5f69086fd2cb · outbound

This paper cites an unresolved cited work.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Unresolved cited work

Reference 91

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source=arxiv_source observed=2026-08-01T10:42:45.439960Z digest=sha256:91d34b50c6e82b1fbcd8b34c90b237615d2e5a42409f96820b812a7dbc034975

Observation f6633865-6df6-43b4-933d-93d8433465ff · outbound

This paper cites Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence

Reference 92

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source=arxiv_source observed=2026-08-01T10:42:45.592909Z digest=sha256:826837a6c8b2a2b77afdfbfa7750d39b6b1fc7e180de0f5560201201f505ecfd

Observation 81003c2e-060c-46ab-a2fc-515efcc9848e · outbound

This paper cites International Conference on Learning Representations , volume=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD International Conference on Learning Representations , volume=

Reference 93

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source=arxiv_source observed=2026-08-01T10:42:45.744353Z digest=sha256:dfecda71608d17abc55081bbd05e583a2a7f4123374ae2a68b813de54f1fe1ce

Observation e3addb22-6af7-47c0-8aa4-da72e40bf2aa · outbound

This paper cites International Conference on Learning Representations , volume=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD International Conference on Learning Representations , volume=

Reference 94

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source=arxiv_source observed=2026-08-01T10:42:45.952338Z digest=sha256:b81517c2ffde58bd31e6b321f54080f28992d9d5dad8102a7001410ad31ebdbe

Observation af92d206-c799-4da7-9524-5552aff8346c · outbound

This paper cites arXiv e-prints , pages=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD arXiv e-prints , pages=

Reference 95

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source=arxiv_source observed=2026-08-01T10:42:46.143917Z digest=sha256:21673ea82dc4ffa3060aa4f4a71710860694edb8dcd6358aa40feba3a5c4015f

Observation 1fa4eb63-ea05-4dad-81e5-2ffa56454117 · outbound

This paper cites Proceedings of the International Conference on Learning Representations (ICLR) , year=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Proceedings of the International Conference on Learning Representations (ICLR) , year=

Reference 96

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source=arxiv_source observed=2026-08-01T10:42:46.334512Z digest=sha256:42e73ad71717756fe514b0c7a477404a2d18144d7c573cf15e9ddccfe8e10e40

Observation 98c645b8-5d39-4ee5-bc5a-29126cc293e7 · outbound

This paper cites an unresolved cited work.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Unresolved cited work

Reference 97

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source=arxiv_source observed=2026-08-01T10:42:46.503777Z digest=sha256:c63a1641c94a225a610cd3a5bb262b23de034ba4291bd89312dd46dc17a3080f

Observation 8e600000-dad5-44ec-94a1-90b2dd47866e · outbound

This paper cites CMMLU : Measuring massive multitask language understanding in C hinese.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD CMMLU : Measuring massive multitask language understanding in C hinese

Reference 98

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source=arxiv_source observed=2026-08-01T10:42:46.724457Z digest=sha256:c936951670391648ce1c612de21b2c5b65c20684ff0b5f2c3f480839698c8b2b

Observation 462999da-7234-4ce3-baf2-cb147238f77f · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Training Verifiers to Solve Math Word Problems

Reference 99

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source=arxiv_source observed=2026-08-01T10:42:46.854625Z digest=sha256:a82f349351c90f09529e3d1b79b056d815c31210ffee8b0c2883e07d4d5d706b

Observation d9ae0598-52fa-4e3f-89e3-7e26bc30dbaf · outbound

This paper cites 2021 , eprint=.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD 2021 , eprint=

Reference 100

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no resolver link, observed 2026-08-01T10:42:46.984968Z

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source=arxiv_source observed=2026-08-01T10:42:46.984968Z digest=sha256:f268e6e3fc1851c4628c288a8cb8b3f0a2d0faacea58ce294747b3d5b8f1729a

Observation 10fc9f2f-4c12-42de-a634-3b4f1154a4aa · outbound

This paper cites an unresolved cited work.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Unresolved cited work

Reference 101

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no resolver link, observed 2026-08-01T10:42:47.058369Z

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source=arxiv_source observed=2026-08-01T10:42:47.058369Z digest=sha256:2acfbb4765ef6ab7cf60377eb20a78e964dc390524b9332a7d34f36a0dc63545

Pith citing papers

No inbound Pith citation observations are available.