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Xinlin Zhuang

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#machine learning Preprint Sep 2026

What Makes Recurrence Effective in Looped Language Models?

Looped language models (LoopLMs) increase computational depth through parameter sharing, offering a path to scale inference computation without adding parameters. However, it remains unclear when additional recurrence is beneficial and how architectural choices affect its effectiveness. Through controlled experiments,...

Xin-Lin Zhuang, Si-Yuan Wang, Imran Razzak et al. · 0 citations

#small language model Preprint Aug 2026

Grounding Isn't Knowing: Do VLMs Need Object Localization for Spatial Reasoning?

This work investigates two representative model families, LLaVA-1.5 and Qwen2.5, and provides a token-, layer-, and head-level account of how VLMs transform object grounding into spatial relations, showing that knowing where objects are is not equivalent to knowing how they relate.

Xi-Wei Liu, Yu-Long Li, Xinlin Zhuang et al. · 1 citation