Ziqi Liu

It is tested whether models in this deployment-relevant 2--8B range carry genuine taxonomic knowledge, evaluating four such VLMs against the domain-specific specialist BioCLIP on a 96-species task, and comparing clean iNaturalist photographs against camera-trap imagery from 6 LILA.

Optimal learning rate and batch size in MoEs depend directly on the expert activation ratio rather than active or total parameter counts, unlocking predictable hyperparameter transfer even at 1/64 sparsity.

VisCAD-M1 outperforms existing models in CAD design by effectively bridging the gap between user intent and executable design, achieving a new state of the art.

Shrinkage Bias in E2M1 formats could be the hidden culprit behind training instability in LLMs, but uniform grids like E1M2/INT4 offer a robust solution.

Ling-2.6 and Ring-2.6 achieve unprecedented efficiency in agentic intelligence, enabling instant responses and deep reasoning at trillion-parameter scale.

Code agents are only eliminating 50% of code smells, revealing critical gaps in their understanding of maintainability and cross-file dependencies.