Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRA
Authors: name: Mind Lab, name: Vin Bo, name: Asher Cai, name: Jingwei Cao, name: Song Cao, name: Vic Cao, name: Amelia Chen, name: Andrew Chen, name: Kaijie Chen, name: Cleon Cheng, name: Steven Chiang, name: Kaixuan Fan, name: Hera Feng, name: Huan Feng, name: Arthur Fu, name: Jun Gao, name: Pyke Han, name: Nolan Ho, name: Ori Hong, name: Hailee Hou, name: Piers Hua, name: Charles Huang, name: Miles Jiang, name: Nora Jiang, name: Yuyi Jiang, name: Qiuyu Jin, name: Fancy Kong, name: Kuss Koo, name: Jaron Lee, name: Andrew Lei, name: Alexy Li, name: Dawn Li, name: Lucian Li, name: Ray Li, name: Ricardo Li, name: Smith Li, name: Theo Li, name: Allen Lin, name: Elliot Lin, name: Fan Lin, name: Chen Ling, name: Kairus Liu, name: Kieran Liu, name: Logan Liu, name: Neo Liu, name: Xiang Liu, name: Yuxin Lu, name: Maeve Luo, name: Pony Ma, name: Verity Niu, name: Cole Qiao, name: Guian Qiu, name: Vince Qu, name: Sentry, name: Niko Song, name: Vincent Wang, name: Bo Wu, name: Rio Yang, name: Evelyn Ye, name: Fiona Ye, name: Ina Ye, name: Regis Ye, name: Josh Ying, name: Atlas Zeng, name: Danney Zeng, name: Salmon Zhan, name: Anya Zhang, name: Di Zhang, name: Mia Zhang, name: Sueky Zhang, name: Wei Zhao, name: Ada Zhou, name: Adrian Zhou, name: Yuhua Zhou, name: Juno Zhu, name: Murphy Zhuang
Organization: Mind Lab

Macaron-V1 is an open agent-model family for experiential intelligence: learning from experience in real environments and continuing to learn after deployment. It is organized around two system goals. Adaptation is pursued through recursive improvement of versioned model-harness pairs, where experience from one configuration is evaluated under an external contract and used to construct its successor. Collaboration is pursued via the Mixture-of-LoRA (MoL) architecture that freezes a base model, composes specialist LoRA adapters, and selects one LoRA per user turn. The flagship Macaron-V1-Venti combines a 744B GLM-5.2 base with four LoRAs for chat, agent, coding, and GenUI; the Qwen3.6-based Macaron-V1-Tall (50B) uses the same design for local deployment. This report presents Macaron-V1 as a co-designed system spanning architecture, algorithms, and infrastructure. The MoL architecture supports continual learning through extensible LoRA specialists. The algorithm combines Model-Harness Co-design and recursive self-improvement loop, including the UI4A component-native GenUI harness, a stateful action substrate, versioned HCP contract, and the agentic RL framework MindForge. The supporting infrastructure includes the post-training platform MinT, the long-context RL method LongStraw, and stability techniques for sparse MoE and DSA base models. We evaluate Macaron-V1 on Personal Intelligence, GenUI, and general capability benchmarks against frontier baselines. Our results validate the current system, while compounding gains from continual learning and collective intelligence remain open questions.