PROUD: PaRetO-gUided Diffusion Model for Multi-objective Generation
Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局前沿人工智能研究中心）；Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Lingnan University（岭南大学）
Yinghua Yao, Yuangang Pan, Jing Li, Ivor Tsang, Xin Yao
Recent advancements in the realm of deep generative models focus on generating samples that satisfy multiple desired properties. However, prevalent approaches optimize these property functions independently, thus omitting the trade-offs among them. In addition, the property optimization is often improperly integrated into the generative models, resulting in an unnecessary compromise on generation quality (i.e., the quality of generated samples). To address these issues, we formulate a constrained optimization problem. It seeks to optimize generation quality while ensuring that generated samples reside at the Pareto front of multiple property objectives. Such a formulation enables the generation of samples that cannot be further improved simultaneously on the conflicting property functions and preserves good quality of generated samples. Building upon this formulation, we introduce the PaRetO-gUided Diffusion model (PROUD), wherein the gradients in the denoising process are dynamically adjusted to enhance generation quality while the generated samples adhere to Pareto optimality. Experimental evaluations on image generation and protein generation tasks demonstrate that our PROUD consistently maintains superior generation quality while approaching Pareto optimality across multiple property functions compared to various baselines.
Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design
Department of Biomedical Engineering, Duke University（杜克大学生物医学工程系）；Department of Computer Science, Fudan University（复旦大学计算机科学系）；Center of Computational Biology, Duke-NUS Medical School（杜克-新加坡国立大学医学学校计算生物学中心）；Management and Technology Program, University of Pennsylvania（宾夕法尼亚大学管理与技术项目）；Department of Computer Science, Duke University（杜克大学计算机科学系）；Department of Biostatistics and Bioinformatics, Duke University（杜克大学生物统计与生物信息学系）
提出 MOG-DFM 框架，通过混合秩方向分数和自适应超锥滤波引导预训练离散流匹配模型，实现多目标帕累托高效的肽与增强子 DNA 序列设计。
AP-BMM: Approximating Capability-Cost Pareto Sets of LLMs via Asynchronous Prior-Guided Bayesian Model Merging
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东新型安全智能技术重点实验室）；Institute of Cyberspace Security（网络空间安全研究院）；School of Computer Science and Technology（计算机科学与技术学院）
mRNAutilus: Multi-Objective-Guided Discrete Generation of mRNA with Optimized Therapeutic Properties
Atom Bioworks Inc.（Atom Bioworks公司）；Department of Computer and Information Science University of Pennsylvania（宾夕法尼亚大学计算机与信息科学系）；Department of Bioengineering University of Pennsylvania（宾夕法尼亚大学生物工程系）；Center of Computational Biology Duke-NUS Medical School（杜克-新加坡国立大学医学学院计算生物学中心）；GenScript USA Inc.（GenScript美国公司）
When Smaller Wins: Dual-Stage Distillation and Pareto-Guided Compression of Liquid Neural Networks for Edge Battery Prognostics
Singapore Institute of Technology（新加坡科技学院）；Institute of Materials Research and Engineering（材料研究与工程研究所）；Agency for Science, Technology and Research（科技研究局）；Institute of High Performance Computing（高性能计算研究所）
LLM-PDESR: Robust PDE Discovery via Subdomain Weighted Residuals and LLM-Guided Symbolic Hypothesis Generation
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；School of Big Data and Artificial Intelligence, Guangdong University of Finance and Economics（广东财经大学大数据与人工智能学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（吉林大学符号计算与知识工程教育部重点实验室）；School of Computer Science, Zhuhai College of Science and Technology（珠海科技学院计算机科学学院）；Department of Mechanical Engineering, National University of Singapore（新加坡国立大学机械工程系）
VC-Soup: Value-Consistency Guided Multi-Value Alignment for Large Language Models
Key Laboratory of Knowledge Engineering with Big Data（知识工程与大数据重点实验室）；Hefei University of Technology（合肥工业大学）；Innovation School of Artificial Intelligence（人工智能创新学院）
A Nested Weighted Tchebycheff Multi-Objective Bayesian Optimization Approach for Flexibility of Unknown Utopia Estimation in Expensive Black-box Design Problems
Oregon State University（俄勒冈州立大学）；Oak Ridge National Laboratory（橡树岭国家实验室）
Divide and Learn: Multi-Objective Combinatorial Optimization at Scale
Department of Computer Science & Engineering UC San Diego（计算机科学与工程系，圣地亚哥大学）；Department of Statistics Stanford University（统计学系，斯坦福大学）；Halıcıoğlu Data Science Institute UC San Diego（Halıcıoğlu数据科学研究所，圣地亚哥大学）
