This paper examines how skills may help address bottlenecks of current agents and how they may expand agent capabilities through reusable domain procedures loaded at inference time and outlines open questions in skill construction, composition, evaluation, portability, governance, and security.
Hanwen Xing, Haomin Zhuang, Xuandong Zhao et al.· Proceedings of the 32nd ACM...· 7 citations· ⚡1
DRNOISE, a 100-task benchmark for answer recovery under misleading evidence, is introduced, a 100-task benchmark for answer recovery under misleading evidence that requires active reconciliation of direct claims with record-level evidence.
Jun Nie, Zhiqin Yang, Zhenheng Tang et al.· arXiv.org· 2 citations
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
SmartNIC-based in-network prediction is a practical complement to partitioning and compression techniques for communicationefficient full-graph GNN training at scale, and results indicate SmartNIC-based in-network prediction is a practical complement to partitioning and compression techniques for communicationefficient full-graph GNN training at scale.
Guofan Yu, Sitian Chen, Zhenheng Tang et al.· IEEE International Conferenc...· 0 citations
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