AI 设计师越用越强：不更新模型，只靠记笔记
让 AI 用专业设计软件干活，难的不是单个操作，而是几百步连起来还不跑偏。这篇让一个固定不变的大模型去操作 230 多个设计工具，同时在外面挂一本「经验笔记」:遇到没见过的子任务就记下新招，做砸了就回头改旧招，而且只允许改完不破坏已会技能的修改。5 轮、1406 个真实设计需求跑下来，笔记从 76 条长到 139 条，执行成功率从 72.7% 提到 99.3%，在 4 个专业设计基准上赢过不记笔记的 AI。它不是你明天能用的工具，但它指了一条路：AI 不用每次升级模型，靠积累经验就能越用越顺手——这可能是让 AI 在真实工作里持续进步的最省力方式。
📄 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.