So the agent, aka the model, doesn't truly learn in a continuous sense, which remains an unsolved problem. But it writes better instructions for itself after each run and can pull them up the next time around. It's not elegant, and it's probably more error-prone than real learning, but the study shows it's an effective workaround.
The work draws on a perspective from Andrej Karpathy about the idea of an "LLM Wiki," which argues for compiling experience into persistent, cumulative knowledge. WikiSkill applies that thinking to automatic skill development for AI agents.
Three layers separate experience, knowledge, and action
WikiSkill organizes an AI agent's workspace into three tiers. The "Raw Layer" stores complete execution traces, from tool calls to results. This data is immutable and serves as raw material.
Above it sits the "Wiki Layer," where raw data gets distilled into structured insights like documented failure patterns and successful strategies. This knowledge layer never resets and only grows with each iteration, according to the researchers.
