AI设计师靠“记笔记”从72%练到99%
让AI用专业设计软件干活，最大的坑不是它不会用工具，而是它做完一件事就忘。这篇论文给AI加了个“工作笔记”:每完成一个设计任务，它就把自己摸索出来的步骤写成一条条自然语言技能存下来；下次遇到类似活儿，先翻笔记再动手。笔记还会自我修订——做砸了就改，做成了就留着，但只允许“修好旧错、不添新乱”的改动进库。5轮、1406个真实设计需求跑下来，技能库从76条涨到139条，执行成功率从72.7%提到99.3%。它不是你明天能装上的插件，但“让AI在干活中积累经验”这条路，比堆参数省钱得多。
📄 原文摘要(英文)
Professional graphic design is a long-horizon agentic task in which structured, editable artifacts emerge from many interdependent actions, yet outcomes admit no reliable programmatic oracle. We introduce a continual adaptation framework in which a frozen frontier model operates professional design software through more than 230 tools, while an external procedural memory of natural-language skills accumulates and refines reusable design procedures from experience. The memory widens by acquiring procedures for recurring uncovered subtasks and deepens by revising existing procedures against their own successful and failed executions, while a matched replay gate admits only changes that repair failures without regressing observed successes. Five rounds over 1,406 real user briefs and 1,869 automatically graded trajectories, with no weight updates and no human labels, grow the bank from 76 documentation-derived skills to 139 and raise GenEval2 execution success on Claude-Sonnet-4 from 72.7% to 99.3% (+11.99 points in generation quality), with 61.8% and 67.6% win rates against the no-skill agent across four specialized design benchmarks on Claude-Sonnet-4 and Claude-Opus-4.6. We further show the two mechanisms are effective in combination: on 200 held-out briefs from user-traffic benchmark, widening or deepening alone reaches a 49.4% / 48.6% win rate over the no-skill agent, while their combination reaches 58.5% (p = 0.025). Procedural memory offers a practical route to continual adaptation of agents under noisy, unverifiable feedback.