6 papers
CARE: Pre-Execution Command Verification for Shell-Executing LLM Agents
Wenxiao Zhang, Yu Liu, Zhiwei Yang +7
Large Language Model (LLM) agents are increasingly used for coding and terminal automation, making shell-command dispatch a high-stakes runtime control point. We study command-leve…
Efficient Convexification of Kolmogorov-Arnold Networks with Polynomial Functional Forms Via a Continuous Graham Scan Approach
Tianwei Li, Daniel Ovalle, Barnabas Poczos +3
Deterministic global optimization of nonlinear models is important in many scientific and engineering applications. This framework typically involves repeatedly solving convex rela…
Breaking the Bottlenecks: Scalable Diffusion Models for 3D Molecular Generation
Adrita Das, Peiran Jiang, Dantong Zhu +2
Diffusion models have emerged as a powerful class of generative models for molecular design, capable of capturing complex structural distributions and achieving high fidelity in 3D…
Learning from B Cell Evolution: Adaptive Multi-Expert Diffusion for Antibody Design via Online Optimization
Hanqi Feng, Peng Qiu, Mengchun Zhang +4
Recent advances in diffusion models have shown remarkable potential for antibody design, yet existing approaches apply uniform generation strategies that cannot adapt to each antig…
AmpLyze: A Deep Learning Model for Predicting the Hemolytic Concentration
Peng Qiu, Hanqi Feng, Meng-Chun Zhang +1
Red-blood-cell lysis (HC50) is the principal safety barrier for antimicrobial-peptide (AMP) therapeutics, yet existing models only say "toxic" or "non-toxic." AmpLyze closes this g…
Pharmacophore-Conditioned Diffusion Model for Ligand-Based De Novo Drug Design
Amira Alakhdar, Barnabas Poczos, Newell Washburn
Developing bioactive molecules remains a central, time- and cost-heavy challenge in drug discovery, particularly for novel targets lacking structural or functional data. Pharmacoph…