This repository aggregates research papers on AI agents from Arxiv, selecting only those introducing novel approaches or distinctively new concepts. Papers are organized by agent capabilities including planning, reasoning, memory, tool use, self-correction, perception, and safety, as well as by architectural patterns for single-agent, multi-agent, and Agent-Ops systems. The collection serves as a research survey resource for tracking advances in LLM-powered autonomous systems.