Author

Meimingwei Li

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#artificial intelligence Preprint Sep 2026

Logit Refiner: Improving Visual Autoregressive Models via Intra-Scale Dependency Modeling

The Logit Refiner is introduced, a lightweight autoregressive module that restores intra-scale dependencies by sequentially sampling tokens conditioned on frozen backbone features and generalizes to text-to-image generation, confirming that the mean-field bottleneck persists across VAR variants and is effectively allev...

Meimingwei Li, S. Baumann, F. Krause et al. · 0 citations

#natural language process... Jun 2026

Breaking the Likelihood Trap: Variance-Calibrated Modulation for Large Language Model Decoding

In experiments across open-ended generation, factual QA, and mathematical reasoning, it is shown that VCM consistently mitigates the likelihood trap, and integrates with existing decoding strategies, improving diversity and coherence and, particularly at higher decoding temperatures, reasoning accuracy.

Yuanhao Ding, Meimingwei Li, E. Arias et al. · arXiv.org · 2 citations