Author

Xiao-Yuan Wang

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#natural language process... Preprint Sep 2026

PC-SubMax: Efficient Prompt Compression via Regularized Submodular Maximization

While large language models (LLMs) are increasingly deployed in long-context scenarios, lengthy prompts can increase inference costs and latency and exacerbate the ``lost-in-the-middle''phenomenon. Selective prompt compression offers a model-agnostic approach to alleviating these issues. However, methods based on fixed...

Zi-Yi Zhang, Shuang Cui, Hao-Tian Zhang et al. · 1 citation

#artificial intelligence Preprint Sep 2026

Which Tokens Should SFT Actually Learn? A Token-Trimming Perspective on Mathematical Reasoning

Supervised fine-tuning (SFT) applies a uniform cross-entropy loss to all target tokens, even though different tokens provide unequal learning signals for mathematical reasoning. This uniform treatment can over-sharpen already mastered tokens while amplifying learning pressure on uncertain, low-confidence tokens, leadin...

Ya-Ning Jia, Chun-Hui Zhang, Wen-Xuan Xu et al. · 0 citations