Author

Huan Sun

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#natural language process... Preprint Nov 2026

ARLoop: Fully-Open Pre-trained Looped Transformers with Attention Residuals

Looped transformers increase effective depth by repeatedly applying a shared set of transformer layers, offering a parameter-efficient way to scale computation. However, standard residual connections aggregate information across recurrent depth into a single stream, providing limited control over which intermediate rep...

Yue-Kun Yao, Yu-Pei Du, Da-Wei Zhu et al. · 0 citations

#artificial intelligence Jun 2026

Can Language Model Agents be Helpful Circuit Explainers in Mechanistic Interpretability?

This work proposes HyVE (Hypothesize, Validate, Explain), an agentic explainer that analyzes each component through an iterative loop of observation, hypothesis generation, and causal validation, eventually producing a component-level explanation and a circuit-level task description.

Ayan Antik Khan, Harsh Kohli, Yue-Kun Yao et al. · arXiv.org · 1 citation