Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic Design

A framework called Designer-RSI, submitted to arXiv on 18 Sep 2026, raised GenEval2 execution success on Claude-Sonnet-4 from 72.7% to 99.3% by evolving an external procedural memory of natural-language design skills, with no weight updates and no human labels. Over five rounds on 1,406 real user briefs and 1,869 automatically graded trajectories, the skill bank grew from 76 documentation-derived skills to 139, and the approach posted 61.8% and 67.6% win rates against a no-skill agent across four specialized design benchmarks on Claude-Sonnet-4 and Claude-Opus-4.6. On 200 held-out briefs, widening or deepening the memory alone reached 49.4% and 48.6% win rates over the no-skill agent, while their combination reached 58.5% (p = 0.025).

Computer Science Artificial Intelligence Submitted on 18 Sep 2026 Title:Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic Design View PDF http://arxiv.org/pdf/2609.22086v1 HTML experimental https://arxiv.org/html/2609.22086v1 Abstract: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. References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .