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...
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
This work introduces ThinkAfford, which decouples high-recall affordance proposal generation from instruction-grounded reasoning and outperforming comparable 3D open-vocabulary and vision-language-model-based 2D-to-3D baselines.
Xinrui Lin, Sha Zhang, Shu-Min Wang et al.· 0 citations
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