LLMAgentPapers
Basic Information
A curated and regularly maintained reading list of important academic and technical papers on large language model agents. The repository collects must-read works covering the design, capabilities, evaluation and application of LLM-based agents and multi-agent systems. Content is organized into focused topics such as agent personality, memory, planning, tool use, reinforcement learning training, multiple-agent communication, applications and frameworks. The README enumerates individual papers with authors and publication dates, highlights surveys and benchmarks, and notes code or dataset pointers when available. The project also publishes news items and welcomes community contributions to keep the list current. It is intended as a central literature resource for researchers, students and practitioners exploring LLM agents and related evaluation resources.
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