MindLab launches Macaron-V1, a 748B LoRA-MoE agent model aimed at long-horizon tasks
智东西 · wechat · 2026-07-23
MindLab’s Macaron-V1 arrives as a MoE agent model family built around Mixture-of-LoRA, with two releases: the 748B Venti and the 50B Tall for local deployment.
Key points:
- The series is designed for long-horizon agent tasks, with benchmarks for chat, coding, agents, and GenUI, plus new internal tests like ChatBench and MacaronLivingBench.
- The article claims Venti is broadly competitive with top-tier models on public agent benchmarks, while remaining especially strong on long-term autonomous tasks.
- MindLab says the LoRA-heavy design cuts the cost of RL and adaptation to about one-tenth of full-parameter training in large sparse MoE systems.
- All variants are open-weight on Hugging Face.
- The company also says it began commercialization in July and reached $10 million ARR in two weeks, while raising nearly $50 million in June.
The piece argues that the next wave of AI will be less about one universal model and more about a shared base model plus many evolving LoRA adapters that preserve memory, preference, and long-term identity.
Related event: MindLab Open-Sources Macaron-V1, Betting on Continual Learning(4 posts)→
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