Multi-teacher on-policy distillation enables rectified-flow models with diffusion-transformer architectures to combine task-specific capabilities within a single student. However, matching model outputs does not guarantee that their internal representations are aligned, and it remains unclear where and when additional...
Xin-Rui Lin, Shu-Min Wang, Su-Qi Song et al.· 0 citations
Large language models (LLMs) now show strong reasoning and coding abilities, raising the prospect that they could assist in developing and optimizing the very infrastructure that powers them. Existing benchmarks, however, mainly focus on isolated kernels, predefined operators, or pre-specified optimization targets, and...
Lei-Lei Ding, Shu-Min Wang, Yu-Ting Huang et al.· 1 citation
This work presents $\Phi$-Bench, a benchmark for systematically evaluating LLMs on engineering the LLM infrastructure stack and spans tasks of varying complexity, ranging from localized kernel-level function completion to long-horizon implementation and end-to-end system optimization.
Lei-Lei Ding, Shu-Min Wang, Yu-Ting Huang et al.· 1 citation
The experimental results validate the effectiveness of the LabDex task design and demonstration data, and show that the benchmark supports the training and systematic evaluation of existing robotic policies across laboratory dexterous manipulation tasks at different levels, providing a foundation for further research a...
Zhipeng Tang, Sihan Chen, Sha Zhang et al.· 0 citations
Laboratory automation has made remarkable progress through robotic platforms and AI-driven scientific reasoning. However, many laboratory operations (e.g., solid--solid transfer) remain inherently dynamic and require real-time adaptation to different materials and experimental conditions. Such precision-critical manipu...
Yuhan Wu, Zhao Jin, Tao Li et al.· arXiv.org· 1 citation
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