Author

Zhen Yang

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#artificial intelligence Preprint Sep 2026

FUSE: An Evaluating Framework for Dangerous Capabilities of LLMs

A modular framework that evaluates each model through three orthogonal pipelines under a unified protocol, aggregating results into a standardized dangerous-capability profile $\phi$, showing that scaling and alignment progress do not uniformly translate into safety.

Zheng-Yi Jin, Ru Zhang, Xiao Chen et al. · 0 citations

Preprint Aug 2026

MemSIF: From Structured Interactions to Dual-Track Fact Memory for LLM Agents

Experiments on LoCoMo and LongMemEval-S across five backbone LLMs show that MemSIF achieves the highest Total ACC in all settings, outperforming the strongest baseline by 2.29%-8.79% on LoCoMo and 2.87%-6.15% on LongMemEval-S.

Yufei Luo, Xiu-Cheng Xu, Zhen Yang · 1 citation