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#machine learning Preprint Oct 2026

FRACTION: Fraud Explanation Generation via Agentic Graph Context Navigation

Graph fraud explanation generation emerges as an important task, which requires jointly leveraging graph structure and content information to produce human-readable explanations for a given risk label. An intuitive approach is to adapt existing graph-enhanced large language models (LLMs), which inject graph structure i...

Cecelia Soh, Jun Hu, Yuan Li et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Attention Sees, MLP Refuses: Tracing the Attention-to-MLP Safety Pathway in LLMs

Safety-aligned large language models (LLMs) remain vulnerable to jailbreaks and can over-refuse benign requests. Understanding these failures requires distinguishing harmfulness recognition from the computation that writes refusal. We reframe this as a pathway question and make it measurable on residual-stream updates...

Zi-Xuan Weng, Hao-Qi He, Wei-Fei Jin et al. · 0 citations

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