Author

Ling Chen

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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

Book Open access Aug 2026

Training a Generalist Hallucination Detector across Multiple Domains via Adaptive Layer Aggregation

Stability-Aware Adaptive Layer Aggregation (SALA) defines a layer-wise instability index computed from the training data, which measures how separability varies across domains and provides an upper bound on the possible separability drop from training to test.

Xinyi Li, Zhen Fang, Yadan Luo et al. · Proceedings of the 32nd ACM... · 0 citations