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Kehan Guo

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#artificial intelligence Preprint Sep 2026

SecProbe: Adaptive Evaluation of Coding Agents on Cybersecurity Vulnerabilities

Assessing cybersecurity vulnerability awareness in coding agents requires evaluations that reveal capability gaps and remain informative as models evolve. Static benchmarks offer fixed coverage and difficulty, while scarce vulnerable repositories and costly expert authoring limit their renewal at scale. We introduce Se...

Xiao-Nan Luo, Yue Huang, Ke-Han Guo et al. · 0 citations
Book Open access Aug 2026

From VAEs to Diffusion and LLMs: Modern Generative Models for Molecular Discovery

This tutorial provides a unified introduction to modern generative modeling approaches for molecular generation, covering their theoretical foundations, algorithmic design, and practical considerations for molecular representations such as 1D SMILES strings, 2D molecular graphs, and 3D structures.

Ke-Han Guo, Yili Shen, Jeehyun Hwang et al. · 0 citations
Book Open access Aug 2026

From VAEs to Diffusion and LLMs: Modern Generative Models for Molecular Discovery

This tutorial provides a unified introduction to modern generative modeling approaches for molecular generation, covering their theoretical foundations, algorithmic design, and practical considerations for molecular representations such as 1D SMILES strings, 2D molecular graphs, and 3D structures.

Kehan Guo, Yili Shen, Jeeyhun Hwang et al. · 0 citations
Jul 2026

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning

KITE (Knowledge-boundary Instruction Tuning via Exploration), a two-stage framework that combines failure-guided data generation with boundary-aware uncertainty curation, is proposed, showing that KITE yields more stable improvement than strong synthetic-data baselines.

Xiaonan Luo, Yue Huang, Kehan Guo et al. · 1 citation

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