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pith:2025:FU3QJMJLKBVLXZFJLB6OAC5I22
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Kling-Omni Technical Report

Borui Liao, Boyuan Jiang, Chao Wang, Chenyu Wang, Da Xie, Fangyuan Kong, Feng Han, Guohao Wu, Guosheng Zhu, Hang Li, Hangyu Mao, Haodong Ouyang, Haozhi Sun, Jiajun Liang, Jie Li, Jingbin He, Kang He, Kling Team: Jialu Chen, Kun Gai, Lianghao Su, Meng Wang, Min Wei, Peiqin Sun, Pengfei Wan, Qingyu Li, Qiulin Wang, Quande Liu, Ruiliang Zhou, Runqi Wang, Sainan Guo, Shenglong Zhang, Shen Li, Shuaiyu Zhang, Shun Lu, Sile Yang, Tiancheng Wen, Wanqi Shi, Weicai Ye, Weihong Lin, Wenyu Qin, Wenzheng Zhao, Xiangyu Du, Xiaohan Li, Xiao Hu, Xiaohua Hu, Xiaokun Liu, Xiaoshi Wu, Xiaoyu Shi, Xintao Wang, Xuebo Wang, Yan Li, Yan Zhou, Yilun Liu, Yingtong Xiong, Yiqiao Liao, Yongjie Zhu, Yuanxing Zhang, Yuanzheng Ci, Yufan Zhang, Yuliang Liu, Yulong Xu, Yunyao Mao, Zekun Wang, Zhenhua Wu, Zikang Yang, Zipeng Feng, Ziyang Yuan

Kling-Omni unifies video generation, editing, and reasoning into a single end-to-end framework that accepts text, images, and video inputs to produce high-fidelity cinematic content.

arxiv:2512.16776 v1 · 2025-12-18 · cs.CV

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4 Citations open
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Claims

C1strongest claim

Kling-Omni demonstrates exceptional capabilities in in-context generation, reasoning-based editing, and multimodal instruction following.

C2weakest assumption

The assumption that the constructed comprehensive data system and large-scale pre-training strategies are sufficient to deliver the claimed integration of generation, editing, and reasoning without hidden performance trade-offs or evaluation biases.

C3one line summary

Kling-Omni is a unified multimodal generative system that produces cinematic videos from diverse inputs by integrating generation, editing, and intelligent reasoning in a single end-to-end model.

References

36 extracted · 36 resolved · 14 Pith anchors

[1] Video generation models as world simulators.OpenAI, 2024 2024
[2] GPT-4 Technical Report 2023 · arXiv:2303.08774
[3] From structure to detail: Hierarchical distillation for efficient diffusion model.arXiv preprint arXiv:2511.08930, 2025 2025
[4] https://deepmind.google/models/gemini-image/pro/
[5] Patch n’pack: Navit, a vision transformer for any aspect ratio and resolution.Advances in Neural Information Processing Systems, 36:2252–2274 2023

Formal links

2 machine-checked theorem links

Cited by

31 papers in Pith

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First computed 2026-05-17T23:38:50.183410Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

2d3704b12b506abbe4a9587ce00ba8d69d6bab89ddb5116c82d23207fb5ff959

Aliases

arxiv: 2512.16776 · arxiv_version: 2512.16776v1 · doi: 10.48550/arxiv.2512.16776 · pith_short_12: FU3QJMJLKBVL · pith_short_16: FU3QJMJLKBVLXZFJ · pith_short_8: FU3QJMJL
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/FU3QJMJLKBVLXZFJLB6OAC5I22 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 2d3704b12b506abbe4a9587ce00ba8d69d6bab89ddb5116c82d23207fb5ff959
Canonical record JSON
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