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# SemanticVLA · LIBERO
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> 🎉 **Accepted to [CVPR 2026](https://cvpr.thecvf.com/virtual/2026/poster/39352).**
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> ✍️ Fei Ni¹, Zhuo Chen², Yifu Yuan³, Zibin Dong³, Xianze Yao³, Shan Luo², Jianye Hao³, Jiankang Deng¹†, Stefanos Zafeiriou¹†
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[SemanticVLA](https://github.com/Fei-Ni/SemanticVLA_Offcial) finetuned on the [LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO) benchmark. The unified OXE LAM is used as the latent-action tokenizer, and the trace + latent-action auxiliary heads are supervised in the VLM's language stream.
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# SemanticVLA · LIBERO
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> 🎉 **Accepted to [CVPR 2026](https://cvpr.thecvf.com/virtual/2026/poster/39352).**
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> ✍️ Fei Ni¹, Zhuo Chen², Yifu Yuan³, Zibin Dong³, Xianze Yao³, Shan Luo², Jianye Hao³, Jiankang Deng¹†, Stefanos Zafeiriou¹†<br>
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> 🏫 ¹Imperial College London ²King's College London ³Tianjin University<br>
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> ✉️ Primary contact: [f.ni@imperial.ac.uk](mailto:f.ni@imperial.ac.uk)
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[SemanticVLA](https://github.com/Fei-Ni/SemanticVLA_Offcial) finetuned on the [LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO) benchmark. The unified OXE LAM is used as the latent-action tokenizer, and the trace + latent-action auxiliary heads are supervised in the VLM's language stream.
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