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However, they remain constrained by prohibitive data demands and are limited to in\u2010distribution interpolation designs. Efficient out\u2010of\u2010distribution inverse design under sparse data constraints remains a critical bottleneck. Here we present an inverse design framework integrating Transformer encoder with reinforcement learning (RL) agent. By combining adaptable structural parameterization and hybrid iterative optimization, this framework efficiently navigates the complex, high\u2010dimensional multi\u2010material design landscape with extremely low data dependency (&lt;10\n                    <jats:sup>3<\/jats:sup>\n                    samples). The inverse design framework leverages reward\u2010driven mechanism to autonomously evolves multi\u2010material architectures that achieve unseen target thermal deformation behaviors while strictly adhering to manufacturing requirements. Uniquely, the versatility of this framework is demonstrated through the realization of multiple thermal deformation targets, including near\u2010zero, negative, and highly anisotropic thermal expansion, as well as complex multimodal deformations. Experimental validation of fabricated structures further confirms the efficacy of our framework for accelerated on\u2010demand design of multi\u2010material functional architectures.\n                  <\/jats:p>","DOI":"10.1002\/adma.75546","type":"journal-article","created":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T05:51:21Z","timestamp":1793253081000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Autonomous Discovery of Multi\u2010Material Metamaterials With Programmable Multimodal Thermal Responses via Reinforcement Learning"],"prefix":"10.1002","author":[{"given":"Chenxi","family":"Lu","sequence":"first","affiliation":[{"name":"Research Centre for Laser Extreme Manufacturing Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences  Ningbo 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