Benchmarks are essential for evaluating and improving large language models (LLMs). Yet many widely used benchmarks are redundant, making evaluation unnecessarily expensive. Benchmark compression lowers this cost by estimating full-benchmark scores and rankings from a partial evaluation. Research in this area has grown...
Guo-Ming Ling, Yupei Lin, Zhong-Zhan Huang et al.· 0 citations
This work presents ZipBench, a simple and low-cost BCM with theoretical error and rank-consistency guarantees that reduces the cost of both LLM evaluation and compact benchmark construction, lowering the barrier to broad LLM research for compute-constrained researchers.
Zhongzhan Huang, Jun-Xin Li, Guo-Ming Ling et al.· 0 citations
This work proposes AtomicVLA, a unified planning-and-execution framework that jointly generates task-level plans, atomic skill abstractions, and fine-grained actions, and introduces a flexible routing encoder that automatically assigns dedicated atomic experts to new skills, enabling continual learning.
Likui Zhang, Tao Tang, Zhihao Zhan et al.· arXiv.org· 19 citations· ⚡3
OutLangSplat is presented which adapts language Gaussian representations to UAV outdoor scenes by improving feature representation and aggregation reliability, and is the first accessible dataset of open-vocabulary 3D scene understanding for UAV outdoor scenes.
Xiaosheng Yan, Hefeng Wu, Yanghui Xu et al.· 1 citation
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