Author

Qing-Wen Zeng

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#artificial intelligence Preprint Oct 2026

ScopeSAE: Model-Scope Feature Discovery with Interpretable Layer Selection

Sparse autoencoders (SAEs) are a central tool in mechanistic interpretability. However, existing SAEs are primarily trained per layer. The modeling subspace is therefore fixed by layer identity, independent of which token-layer states actually drive each prediction. We argue that this constraint contributes to several...

Qing-Wen Zeng, Ze-Hao Fu, Shu-Yu Meng et al. · 0 citations

#artificial intelligence May 2026

Latent Action Reparameterization for Efficient Agent Inference

Large language model (LLM) agents often rely on long sequences of low-level textual actions, resulting in large effective decision horizons and high inference cost. While prior work has focused on improving inference efficiency through system-level optimizations or prompt engineering, we argue that a key bottleneck lie...

Wenhao Huang, Qing-Wen Zeng, Qi-Yue Chen et al. · arXiv.org · 0 citations