8 citations · 19 across the 25 of their papers we have counts for
10 papers · 1 filter
Getting Better at Working With You: Compiling User Corrections into Runtime Enforcement for Coding Agents
Yujun Zhou, Kehan Guo, Haomin Zhuang +8
Interactive LLM agents are becoming part of daily work, but they do not reliably become easier to work with over time: a correction remembered in one session may still be violated…
Genotype-Conditioned Molecular Generation via Evidence-Grounded Multi-Objective Latent Perturbation in Diffusion Models
Brenda Nogueira, Gisela A. Gonzalez-Montiel, Nitesh V. Chawla +1
Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across cancer subtypes. Generative mod…
Alignment Risks from Capability-Seeking RL Training
Yujun Zhou, Yue Huang, Han Bao +8
While most AI alignment research focuses on preventing models from generating explicitly harmful content, a more subtle risk arises from capability-seeking RL training in vulnerabl…
SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression
Brenda Nogueira, Gisela A. Gonzalez-Montiel, Meng Jiang +2
Molecular property regression struggles with cases in chemically relevant target ranges that are underrepresented in datasets. Standard average error minimization approaches underp…
Onboarding Without Forgetting: Hypernetwork Personalization with Data-Free Replay for Personalized Federated Learning
Thinh Nguyen, Le Huy Khiem, Van-Tuan Tran +3
Federated Learning (FL) enables collaborative training across distributed clients without sharing raw data, offering strong privacy benefits. However, most methods assume all clien…
Spectral Manifold Harmonization for Graph Imbalanced Regression
Brenda Nogueira, Gabe Gomes, Meng Jiang +2
Graph-structured data is ubiquitous in scientific domains, where models often face imbalanced learning settings. In imbalanced regression, domain preferences focus on specific targ…