DeepSeek-V4.1-Flash 技术报告 / 附录

参考文献

参考文献按原文语言保留。

参考文献 Ainslie–Dosovitskiy

J. Ainslie, J. Lee-Thorp, M. de Jong, Y. Zemlyanskiy, F. Lebrón, and S. Sanghai. Gqa: Training generalized multi-query transformer models from multi-head checkpoints. arXiv preprint arXiv:2305.13245, 2023.

E. Alvarez, O. Almog, E. Chung, S. Layton, D. Stosic, R. Krashinsky, and K. Aubrey. Introducing nvfp4 for efficient and accurate low-precision inference, 2025. URL https://developer.nvidia.com/blog/introducing-nvfp4-for-efficient-and-accurate-low-precision-inference/.

Anomaly. Opencode. https://github.com/anomalyco/opencode, 2026.

Anthropic. Claude code. https://code.claude.com/docs/en/overview, 2026.

Y. Bai, S. Tu, J. Zhang, H. Peng, X. Wang, X. Lv, S. Cao, J. Xu, L. Hou, Y. Dong, et al. Longbench v2: Towards deeper understanding and reasoning on realistic long-context multitasks. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3639–3664, 2025.

Y. Bai, Q. Dong, T. Jiang, X. Lv, Z. Du, A. Zeng, J. Tang, and J. Li. Indexcache: Accelerating sparse attention via cross-layer index reuse. arXiv preprint arXiv:2603.12201, 2026.

W. Brandon, M. Mishra, A. Nrusimha, R. Panda, and J. Ragan-Kelley. Reducing transformer key-value cache size with cross-layer attention. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang, editors, Advances in Neural Information Processing Systems, volume 37, pages 86927–86957. Curran Associates, Inc., 2024. doi: 10.52202/079017-2758. URL https://proceedings.neurips.cc/paper_files/paper/2024/file/9e23d020c18e4c40d81c6a0fc7a46f68-Paper-Conference.pdf.

L. Chen, D. Xu, C. An, X. Wang, Y. Zhang, J. Chen, Z. Liang, F. Wei, J. Liang, Y. Xiao, et al. Powerattention: exponentially scaling of receptive fields for effective sparse attention. arXiv preprint arXiv:2503.03588, 2025.

L. Chen, W. Xie, Y. Liang, H. He, H. Zhao, Z. Yang, Z. Huang, H. Wu, H. Lu, Y. Bao, et al. Babyvision: Visual reasoning beyond language. arXiv preprint arXiv:2601.06521, 2026.

M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. de Oliveira Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, A. Ray, R. Puri, G. Krueger, M. Petrov, H. Khlaaf, G. Sastry, P. Mishkin, B. Chan, S. Gray, N. Ryder, M. Pavlov, A. Power, L. Kaiser, M. Bavarian, C. Winter, P. Tillet, F. P. Such, D. Cummings, M. Plappert, F. Chantzis, E. Barnes, A. Herbert-Voss, W. H. Guss, A. Nichol, A. Paino, N. Tezak, J. Tang, I. Babuschkin, S. Balaji, S. Jain, W. Saunders, C. Hesse, A. N. Carr, J. Leike, J. Achiam, V. Misra, E. Morikawa, A. Radford, M. Knight, M. Brundage, M. Murati, K. Mayer, P. Welinder, B. McGrew, D. Amodei, S. McCandlish, I. Sutskever, and W. Zaremba. Evaluating large language models trained on code. CoRR, abs/2107.03374, 2021. URL https://arxiv.org/abs/2107.03374.

X. Cheng, X. Yu, C. Shao, J. Li, Y. Xiong, Y. Qian, J. Zhu, S. Ma, X. Zhang, J. Ye, et al. Dspark: Confidence-scheduled speculative decoding with semi-autoregressive generation. arXiv preprint arXiv:2607.05147, 2026a.

X. Cheng, W. Zeng, D. Dai, Q. Chen, B. Wang, Z. Xie, K. Huang, X. Yu, Z. Hao, Y. Li, H. Zhang, H. Zhang, D. Zhao, and W. Liang. Conditional memory via scalable lookup: A new axis of sparsity for large language models. CoRR, abs/2601.07372, 2026b. doi: 10.48550/ARXIV.2601.07372. URL https://doi.org/10.48550/arXiv.2601.07372.

X. Cheng, W. Zeng, D. Dai, Q. Chen, B. Wang, Z. Xie, K. Huang, X. Yu, Z. Hao, H. Zhang, et al. Conditional memory via scalable lookup: A new axis of sparsity for large language models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4968–4990, 2026c.

K. Cobbe, V. Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021.

D. Dai, C. Deng, C. Zhao, R. X. Xu, H. Gao, D. Chen, J. Li, W. Zeng, X. Yu, Y. Wu, Z. Xie, Y. K. Li, P. Huang, F. Luo, C. Ruan, Z. Sui, and W. Liang. Deepseekmoe: Towards ultimate expert specialization in mixture-of-experts language models. CoRR, abs/2401.06066, 2024. URL https://doi.org/10.48550/arXiv.2401.06066.

DataCurve. Deepswe v1.1, 2026. URL https://deepswe.datacurve.ai/.

DeepSeek-AI. Deepseek-v3 technical report. CoRR, abs/2412.19437, 2024. URL https://doi.org/10.48550/arXiv.2412.19437.

DeepSeek-AI. Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model. CoRR, abs/2405.04434, 2024. URL https://doi.org/10.48550/arXiv.2405.04434.

DeepSeek-AI. Deepseek-v3.2: Pushing the frontier of open large language models, 2025. URL https://arxiv.org/abs/2512.02556.

DeepSeek-AI. Deepseek harness: Everything is a plugin. https://github.com/deepseek-ai/deepseek-harness, 2026a.

DeepSeek-AI. Deepseek-v4: Towards highly efficient million-token context intelligence. CoRR, abs/2606.19348, 2026b. URL https://doi.org/10.48550/arXiv.2606.19348.

J. Dekoninck, N. Jovanovi´c, I. Petrov, and M. Vechev. Matharena apex: Unconquered final-answer problems, 2025. URL https://matharena.ai/apex/.

S. Deng, Z. Ouyang, T. Pang, Z. Liu, R. Jin, S. Yu, and Y. Yang. Rmnp: Row-momentum normalized preconditioning for scalable matrix-based optimization. arXiv preprint arXiv:2603.20527, 2026.

J. Ding, S. Long, C. Pu, H. Zhou, H. Gao, X. Gao, C. He, Y. Hou, F. Hu, Z. Li, et al. Nl2repo-bench: Towards long-horizon repository generation evaluation of coding agents. arXiv preprint arXiv:2512.12730, 2025.

A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=YicbFdNTTy.

参考文献 Du–Mathew

X. Du, Y. Yao, K. Ma, B. Wang, T. Zheng, K. Zhu, M. Liu, Y. Liang, X. Jin, Z. Wei, et al. Supergpqa: Scaling llm evaluation across 285 graduate disciplines. arXiv preprint arXiv:2502.14739, 2025.

D. Dua, Y. Wang, P. Dasigi, G. Stanovsky, S. Singh, and M. Gardner. DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs. In J. Burstein, C. Doran, and T. Solorio, editors, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pages 2368– 2378. Association for Computational Linguistics, 2019. doi: 10.18653/V1/N19-1246. URL https://doi.org/10.18653/v1/n19-1246.

Y. Gao, J. Wei, Q. Zhang, Y. Cheng, S. Chen, Z. Tang, Z. Jiang, Y. Song, H. Zhang, L. Zhao, B. Yang, G. Wang, S. Cao, and F. Luo. Hysparse: A hybrid sparse attention architecture with oracle token selection and kv cache sharing. arXiv preprint arXiv:2602.03560, 2026.

S. Garre, C. Mutty, S. Mehta, and E. Chen. Chartography: A benchmark for professional chart understanding. arXiv preprint arXiv:2608.10677, 2026.

A. Glentis, J. Li, A. Han, and M. Hong. Memory-efficient llm pretraining via minimalist optimizer design. arXiv preprint arXiv:2506.16659, 2025.

Y. Gu, L. Dong, F. Wei, and M. Huang. Minillm: Knowledge distillation of large language models. In The Twelfth International Conference on Learning Representations, 2024.

L. Haas, G. Yona, G. D’Antonio, S. Goldshtein, and D. Das. Simpleqa verified: A reliable factuality benchmark to measure parametric knowledge. arXiv preprint arXiv:2509.07968, 2025.

D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, and J. Steinhardt. Measuring mathematical problem solving with the math dataset. arXiv preprint arXiv:2103.03874, 2021.

Y. Huang, Y. Bai, Z. Zhu, J. Zhang, J. Zhang, T. Su, J. Liu, C. Lv, Y. Zhang, J. Lei, et al. C-Eval: A multi-level multi-discipline chinese evaluation suite for foundation models. arXiv preprint arXiv:2305.08322, 2023.

D. Hupkes and N. Bogoychev. Multiloko: a multilingual local knowledge benchmark for llms spanning 31 languages. CoRR, abs/2504.10356, 2025. doi: 10.48550/ARXIV.2504.10356. URL https://doi.org/10.48550/arXiv.2504.10356.

B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.

K. Jordan, Y. Jin, V. Boza, J. You, F. Cesista, L. Newhouse, and J. Bernstein. Muon: An optimizer for hidden layers in neural networks. Cited on, page 10, 2024.

M. Kazemi, B. Fatemi, H. Bansal, J. Palowitch, C. Anastasiou, S. V. Mehta, L. K. Jain, V. Aglietti, D. Jindal, Y. P. Chen, et al. Big-bench extra hard. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 26473–26501, 2025.

S. Kazemzadeh, V. Ordonez, M. Matten, and T. Berg. Referitgame: Referring to objects in photographs of natural scenes. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 787–798, 2014.

R. Kondra, S. Mhatre, A. Kumar, E. Chu, B. B. Ahmad, A. Nangia, R. Agarwal, A. Dasgupta, A. Sinha, B. Sridharan, K. Dave, B. Graham, G. Song, A. Rahul, W. H. Lim, A. Thangamuthu, R. Singh, D. Liu, N. Pour, C. Chen, and J. Mattern. Frontierswe v2. Proximal Blog, 2026. https://frontierswe.com/blog/v2.

H. Lee, J. Liu, D. Kim, W. Xia, Z. Zhang, C. S. Xia, and L. Zhang. Sec-bench pro: Can language models solve long-horizon software security tasks? arXiv preprint arXiv:2605.26548, 2026.

J. Li and S. Liu. Flashmla: Efficient multi-head latent attention kernels. https://github.com/deepseek-ai/FlashMLA, 2025.

J. Liu, J. Su, X. Yao, Z. Jiang, G. Lai, Y. Du, Y. Qin, W. Xu, E. Lu, J. Yan, Y. Chen, H. Zheng, Y. Liu, S. Liu, B. Yin, W. He, H. Zhu, Y. Wang, J. Wang, M. Dong, Z. Zhang, Y. Kang, H. Zhang, X. Xu, Y. Zhang, Y. Wu, X. Zhou, and Z. Yang. Muon is scalable for LLM training. CoRR, abs/2502.16982, 2025. URL https://doi.org/10.48550/arXiv.2502.16982.

I. Loshchilov and F. Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2019. URL https://openreview.net/forum?id=Bkg6RiCqY7.

K. Lu and T. M. Lab. On-policy distillation. Thinking Machines Lab: Connectionism, 2025. doi: 10.64434/tml.20251026. https://thinkingmachines.ai/blog/on-policy-distillation.

J. Mao, J. Huang, A. Toshev, O. Camburu, A. L. Yuille, and K. Murphy. Generation and comprehension of unambiguous object descriptions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 11–20, 2016.

A. Marafioti, O. Zohar, M. Farré, M. Noyan, E. Bakouch, P. Cuenca, C. Zakka, L. B. Allal, A. Lozhkov, N. Tazi, V. Srivastav, J. Lochner, H. Larcher, M. Morlon, L. Tunstall, L. von Werra, and T. Wolf. Smolvlm: Redefining small and efficient multimodal models. arXiv preprint arXiv:2504.05299, 2025.

R. Marten, A. Shaw, I. Bercovich, B. Droste, T. Cerruti, S. Dillmann, R. Wang, D. Wahdany, A. Hart, K. Krauth, ScaleAI, Snorkel AI, Turing, gNucleus AI, Boolean AI, N. Carlini, S. Lyu, A. Wei, A. Khatua, B. Plüster, C. Dwivedi, C. Sutcliffe, Yuming, D. Tivris, D. Wang, H. W. Goh, H. Xing, H. Lin, I. Salia, J. Seol, J. Bao, J. Ouyang, J. Park, L. Walsh, L. Kong, M. Ivanov, M. Ubl, M. Liamets, O. Menis, P. Migdal, Q. Bao, R. Movva, R. Ben Chaim, N. Srinath, S. Bogdanik, S. Yadav, S. Benjamin, T. Kung, W. Hughes, X. Lan, H. Gupta, S. Mishra, C. Wang, H. He, J. Tu, K. Montgomery, Z. Tu, A. Naik, D. Mortensen, I. Zhang, Y. Mathur, E. Liu, K. Singh, M. Yu, S. Feng, V. Gangal, Z. Tao, S. Ruan, J. Mueller, J. Cabezas, J. Bauer, K. X. Li, R. Zhang, A. Feller, A. Madayan, L. Chen, B. Feuer, X. Li, B. Li, H. Raj, S. Galler, L. Shi, I. Segal, K. Buchanan, S. P., R. Desai, A. Schneider, C. Settles, X. Lin, M. Nezhurina, A. Wang, M. Kowalczyk, J.-X. Zhao, S. Satia, J. Hu, S. Atef, K. Chen, S. Vance, G. Segato, J. Jitsev, A. Dimakis, M. Merrill, A. Konwinski, and L. Schmidt. Terminal-Bench, Aug. 2026a. URL https://github.com/harbor-framework/terminal-bench.

R. Marten, A. Shaw, A. Konwinski, and Terminal-Bench Contributors. Terminal-bench 3.0: Harder tasks for better agents. https://www.tbench.ai/news/terminal-bench-3-0, 2026b. An Open Benchmark for Agent Work in Terminal Environments.

M. Mathew, D. Karatzas, and C. Jawahar. Docvqa: A dataset for vqa on document images. In 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 2199–2208. IEEE, 2021.

参考文献 Merrill–Wang

M. A. Merrill, A. G. Shaw, N. Carlini, B. Li, H. Raj, I. Bercovich, L. Shi, J. Y. Shin, T. Walshe, E. K. Buchanan, et al. Terminal-bench: Benchmarking agents on hard, realistic tasks in command line interfaces. arXiv preprint arXiv:2601.11868, 2026.

V. K. Nagaraja, V. I. Morariu, and L. S. Davis. Modeling context between objects for referring expression understanding. In European conference on computer vision, pages 792–807. Springer, 2016.

Y. Nesterov. A method of solving a convex programming problem with convergence rate 𝑂(1/𝑘2). Soviet Mathematics Doklady, 27:372–376, 1983.

OpenAI. gpt-oss-120b & gpt-oss-20b model card. CoRR, abs/2508.10925, 2025. doi: 10.48550/A RXIV.2508.10925. URL https://doi.org/10.48550/arXiv.2508.10925.

OpenAI. Codex. https://github.com/openai/codex, 2026.

L. Phan, A. Gatti, Z. Han, N. Li, J. Hu, H. Zhang, C. B. C. Zhang, M. Shaaban, J. Ling, S. Shi, et al. Humanity’s last exam. arXiv preprint arXiv:2501.14249, 2025.

Y. Qian, S. Liu, and Y. Li. Deepselect: High-performance topk kernels for deepseek sparse attention and sampling. https://github.com/deepseek-ai/DeepSelect, 2026.

D. Rein, B. L. Hou, A. C. Stickland, J. Petty, R. Y. Pang, J. Dirani, J. Michael, and S. R. Bowman. GPQA: A graduate-level google-proof q&a benchmark. arXiv preprint arXiv:2311.12022, 2023.

J. Roberts, M. R. Taesiri, A. Sharma, A. Gupta, S. Roberts, I. Croitoru, S.-V. Bogolin, J. Tang, F. Langer, V. Raina, et al. Zerobench: An impossible visual benchmark for contemporary large multimodal models. arXiv preprint arXiv:2502.09696, 2025.

B. D. Rouhani, R. Zhao, A. More, M. Hall, A. Khodamoradi, S. Deng, D. Choudhary, M. Cornea, E. Dellinger, K. Denolf, S. Dusan, V. Elango, M. Golub, A. Heinecke, P. James-Roxby, D. Jani, G. Kolhe, M. Langhammer, A. Li, L. Melnick, M. Mesmakhosroshahi, A. Rodriguez, M. Schulte, R. Shafipour, L. Shao, M. Siu, P. Dubey, P. Micikevicius, M. Naumov, C. Verrilli, R. Wittig, D. Burger, and E. Chung. Microscaling data formats for deep learning, 2023.

M. Scetbon, C. Ma, W. Gong, and E. Meeds. Gradient multi-normalization for efficient LLM training. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https://openreview.net/forum?id=oanhUGY6un.

N. Shazeer. Glu variants improve transformer. arXiv preprint arXiv:2002.05202, 2020.

N. Shazeer and M. Stern. Adafactor: Adaptive learning rates with sublinear memory cost. In International conference on machine learning, pages 4596–4604. PMLR, 2018.

D. Shepard and R. Salimans. Automationbench. arXiv preprint arXiv:2604.18934, 2026.

F. Shi, M. Suzgun, M. Freitag, X. Wang, S. Srivats, S. Vosoughi, H. W. Chung, Y. Tay, S. Ruder, D. Zhou, D. Das, and J. Wei. Language models are multilingual chain-of-thought reasoners. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. URL https://openreview.net/forum?id=fR3wGCk-IXp.

Y. Sun, L. Dong, Y. Zhu, S. Huang, W. Wang, S. Ma, Q. Zhang, J. Wang, and F. Wei. You only cache once: Decoder-decoder architectures for language models. Advances in Neural Information Processing Systems, 37:7339–7361, 2024.

Y. Sun, X. Han, W. Zhang, Y. Pang, T. Wang, Y. Cao, Y. Huang, C. Duroiu, H. Zhang, J. Lin, et al. Agents’ last exam. arXiv preprint arXiv:2606.05405, 2026a.

Y. Sun, Y. Zhang, L. Dong, J. Wang, and F. Wei. You only index once: Cross-layer sparse attention with shared routing. arXiv preprint arXiv:2606.06467, 2026b.

M. Suzgun, N. Scales, N. Schärli, S. Gehrmann, Y. Tay, H. W. Chung, A. Chowdhery, Q. V. Le, E. H. Chi, D. Zhou, et al. Challenging big-bench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09261, 2022.

K. Team, T. Bai, Y. Bai, Y. Bao, J. Cai, X. Cai, P. Cao, Y. Cao, Z. Chai, Y. Charles, et al. Kimi k3: Open frontier intelligence. arXiv preprint arXiv:2607.24653, 2026a.

K. Team, T. Bai, Y. Bai, Y. Bao, S. Cai, Y. Cao, Z. Chai, Y. Charles, H. Che, C. Chen, et al. Kimi k2.5: Visual agentic intelligence. arXiv preprint arXiv:2602.02276, 2026b.

M. L. Team, B. Wang, B. Xiao, B. Zhang, B. Rong, B. Chen, C. Wan, C. Zhang, C. Huang, C. Chen, et al. Longcat-flash-omni technical report. arXiv preprint arXiv:2511.00279, 2025.

S. Tong, E. Brown, P. Wu, S. Woo, M. Middepogu, S. C. Akula, J. Yang, S. Yang, A. Iyer, X. Pan, A. Wang, R. Fergus, Y. LeCun, and S. Xie. Cambrian-1: A fully open, vision-centric exploration of multimodal llms, 2024.

L. Wang, H. Gao, C. Zhao, X. Sun, and D. Dai. Auxiliary-loss-free load balancing strategy for mixture-of-experts. CoRR, abs/2408.15664, 2024a. URL https://doi.org/10.48550/arXiv.2408.15664.

X. Wang, C. Xu, H. Cao, R. Tian, W. Zhao, K. Yu, and C. Zhao. Tilekernels. https://github.com/deepseek-ai/TileKernels, 2026a.

参考文献 Wang–Zhuo

Y. Wang, X. Ma, G. Zhang, Y. Ni, A. Chandra, S. Guo, W. Ren, A. Arulraj, X. He, Z. Jiang, T. Li, M. Ku, K. Wang, A. Zhuang, R. Fan, X. Yue, and W. Chen. Mmlu-pro: A more robust and challenging multi-task language understanding benchmark. CoRR, abs/2406.01574, 2024b. URL https://doi.org/10.48550/arXiv.2406.01574.

Z. Wang, N. Schiller, H. Li, S. S. Narayana, M. Nasr, N. Carlini, X. Qi, E. Wallace, E. Bursztein, L. Invernizzi, et al. Exploitgym: Can ai agents turn security vulnerabilities into real attacks? arXiv preprint arXiv:2605.11086, 2026b.

Z. Wang, T. Shi, J. He, M. Cai, J. Zhang, and D. Song. Cybergym: Evaluating ai agents’ real-world cybersecurity capabilities at scale. In International Conference on Learning Representations, volume 2026, pages 123341–123386, 2026c.

Z. Wen, Y. Shi, J. Wang, P. Luo, L. Qiao, D. Li, and T. Sun. Sron: State-free llm training via row-wise gradient normalization. 2025.

Z. Xie, Y. Wei, H. Cao, C. Zhao, C. Deng, J. Li, D. Dai, H. Gao, J. Chang, K. Yu, L. Zhao, S. Zhou, Z. Xu, Z. Zhang, W. Zeng, S. Hu, Y. Wang, J. Yuan, L. Wang, and W. Liang. mhc: Manifold-constrained hyper-connections, 2026. URL https://arxiv.org/abs/2512.24880.

R. Xu, J. Li, and Y. Lu. On the width scaling of neural optimizers under matrix operator norms i: Row/column normalization and hyperparameter transfer. arXiv preprint arXiv:2603.09952, 2026a.

Y. Xu, F. Meng, F. Jiang, Y. Wang, R. Zhou, Z. Wang, J. Wu, Z. Pan, X. Tang, W. Pei, et al. Hisa: Efficient hierarchical indexing for fine-grained sparse attention. arXiv preprint arXiv:2603.28458, 2026b.

J. Yang, C. E. Jimenez, A. Wettig, K. Lieret, S. Yao, K. R. Narasimhan, and O. Press. SWE-agent: Agent-computer interfaces enable automated software engineering. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. URL https://arxiv.org/abs/2405.15793.

J. Yang, K. Lieret, J. Ma, P. Thakkar, D. Pedchenko, S. Sootla, E. McMilin, P. Yin, R. Hou, G. Synnaeve, D. Yang, and O. Press. Programbench: Can language models rebuild programs from scratch?, 2026. URL https://arxiv.org/abs/2605.03546.

L. Yu, P. Poirson, S. Yang, A. C. Berg, and T. L. Berg. Modeling context in referring expressions. In European conference on computer vision, pages 69–85. Springer, 2016.

J. Yuan, J. Zou, S. Wang, Y. Liu, and F. Nie. Nora: Normalized orthogonal row alignment for scalable matrix optimizer. arXiv preprint arXiv:2605.03769, 2026.

X. Yue, T. Zheng, Y. Ni, Y. Wang, K. Zhang, S. Tong, Y. Sun, B. Yu, G. Zhang, H. Sun, et al. Mmmu-pro: A more robust multi-discipline multimodal understanding benchmark. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15134–15186, 2025.

M. Zechner. Pi: The coding-agent harness you can make your own. https://github.com/earendil-works/pi, 2026.

R. Zellers, A. Holtzman, Y. Bisk, A. Farhadi, and Y. Choi. HellaSwag: Can a machine really finish your sentence? In A. Korhonen, D. R. Traum, and L. Màrquez, editors, Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pages 4791–4800. Association for Computational Linguistics, 2019. doi: 10.18653/v1/p19-1472. URL https://doi.org/10.18653/v1/p19-1472.

A. Zeng, X. Lv, Z. Hou, Z. Du, Q. Zheng, B. Chen, D. Yin, C. Ge, C. Huang, C. Xie, et al. Glm-5: from vibe coding to agentic engineering. arXiv preprint arXiv:2602.15763, 2026.

X. Zhai, B. Mustafa, A. Kolesnikov, and L. Beyer. Sigmoid loss for language image pre-training. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 11941–11952. IEEE, 2023.

B. Zhang and R. Sennrich. Root mean square layer normalization. Advances in neural information processing systems, 32, 2019.

Y. Zhang. On the principles behind neural network optimizers. arXiv preprint arXiv:2608.16760, 2026.

Y. Zhang, C. Chen, T. Ding, Z. Li, R. Sun, and Z.-Q. Luo. Why transformers need adam: A hessian perspective. Advances in neural information processing systems, 37:131786–131823, 2024.

Y. Zhang, C. Chen, Z. Li, T. Ding, C. Wu, D. D. Kingma, Y. Ye, Z.-Q. Luo, and R. Sun. Adam-mini: Use fewer learning rates to gain more. In International Conference on Learning Representations, volume 2025, pages 28033–28063, 2025a.

Z. Zhang, Y. Zhong, Y. Jiang, H. Hu, J. Sun, Z. Ge, Y. Zhu, D. Jiang, and X. Jin. Disttrain: Addressing model and data heterogeneity with disaggregated training for multimodal large language models. In Proceedings of the ACM SIGCOMM 2025 Conference, pages 24–38, 2025b.

C. Zhao, L. Zhao, J. Li, Z. Xu, and C. Xu. Deepgemm: clean and efficient fp8 gemm kernels with fine-grained scaling. https://github.com/deepseek-ai/DeepGEMM, 2025.

W. Zhong, R. Cui, Y. Guo, Y. Liang, S. Lu, Y. Wang, A. Saied, W. Chen, and N. Duan. AGIEval: A human-centric benchmark for evaluating foundation models. CoRR, abs/2304.06364, 2023. doi: 10.48550/arXiv.2304.06364. URL https://doi.org/10.48550/arXiv.2304.06364.

T. Y. Zhuo, M. C. Vu, J. Chim, H. Hu, W. Yu, R. Widyasari, I. N. B. Yusuf, H. Zhan, J. He, I. Paul, S. Brunner, C. Gong, J. Hoang, A. R. Zebaze, X. Hong, W. Li, J. Kaddour, M. Xu, Z. Zhang, P. Yadav, and et al. Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net, 2025. URL https://openreview.net/forum?id=YrycTjllL0.