Author

Yu-Hang Xu

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#artificial intelligence Preprint Oct 2026

LoopAhead: Speculating Ahead for Faster Decoding in Looped Language Models

Looped Transformers improve parameter efficiency by repeatedly applying the same Transformer layers. However, generating each token still involves sequential computation and repeated access to model weights, leading to high decoding latency. Recent pipelined self-speculative decoding methods reduce this delay by drafti...

Zi-Xuan Liu, Nan Xue, Rui Bao et al. · 0 citations

Preprint Jul 2026

Unified Hallucination Fuzzing for Multimodal Large Language Models

This work introduces UniHall, a fine-grained dataset grounded in a unified taxonomy spanning Object, Instruction, and Knowledge dimensions, and proposes Self-Adaptive Multimodal Fuzzing (SAMF), a self-adaptive framework that employs evolutionary mutation strategies to explore the boundaries of model hallucinations.

P. Zhou, Jiajun Song, Zhiwei Tang et al. · 1 citation