As Large Language Model (LLM) agents have demonstrated broad competence, but they still struggle in specialized, real-world workflows. Existing approaches such as RAG, fine-tuning and tool integration improve knowledge access, model adaptation, and external functionality, yet they do not fully address a central gap: the absence of reusable procedural knowledge for carrying out domain tasks reliably. This paper examines the emerging notion of agent skills as a possible abstraction for addressing that gap. Agent Skills are modular packages of domain-specific procedural knowledge that can be injected at inference time. Intuitively, a skill is like a cooking recipe for an agent: it does not provide new ingredients or tools, but specifies how available resources should be combined to achieve a desired outcome. A community-driven skills ecosystem is already emerging at remarkable speed, with early evidence of meaningful performance gains across multiple domains. However, their value and limits remain open questions. We examine how skills may help address bottlenecks of current agents and how they may expand agent capabilities through reusable domain procedures loaded at inference time. We then outline open questions in skill construction, composition, evaluation, portability, governance, and security, and conclude with a call for contribution. Our goal is not to present skills as a settled solution, but to clarify their promise, limits, and the questions that must be answered before they can become a principled foundation for future agent systems.
Hanwen Xing, Haomin Zhuang, Xuandong Zhao et al.· Proceedings of the 32nd ACM...· 6 citations· ⚡1
Generative models are emerging as a key technology for accelerating molecular discovery in drug design, materials science, and catalysis by enabling efficient exploration of the vast chemical space of possible molecules. Recent advances in deep generative modeling—including variational autoencoders (VAEs), diffusion models, flow matching methods, and autoregressive transformer-based approaches—have produced a diverse toolkit for generating molecular structures and optimizing their properties. However, these paradigms are often studied independently, leaving many machine learning researchers without a clear understanding of their connections, strengths, and limitations in molecular applications. 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. While the tutorial primarily focuses on generative models for de novo molecular design, we also briefly discuss how similar modeling paradigms extend to reaction prediction and retrosynthesis. By presenting these models within a common framework, the tutorial aims to equip ML researchers and AI-for-science practitioners with a clear conceptual map of the generative modeling landscape for molecular discovery and identify emerging research opportunities in this rapidly evolving area.
Kehan Guo, Yili Shen, Jeeyhun Hwang et al.· Proceedings of the 32nd ACM...· 0 citations