Author

Yi-Xun Wei

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#artificial intelligence Preprint Oct 2026

DeFA: Dependency-Guided Failure Attribution for LLM Agents

Errors in LLM agent executions and their visible consequences can be separated by many steps, making decisive-error localization a matter of understanding both step content and step dependencies. We introduce DeFA, a dependency-guided framework for agent failure attribution. DeFA first combines protocol relations and s...

Bo Deng, Xin-Lei Zheng, Yi-Xun Wei et al. · 0 citations

#artificial intelligence Preprint Sep 2026

KITE: KV-Invariant Transformer Expansion for Efficient Agentic LLM Scaling

KV-Invariant Transformer Expansion (KITE) is introduced, a scaling paradigm that trains the model from a smaller size to a larger size (i.e., saving training costs via upcycling), while places newly added parameters in regions that do not affect attention KV.

Zhi-Heng Hu, Yi-Xun Wei, Jian Zhou et al. · 0 citations