Mind Lab puts continual learning to the test with Macaron-V1

Chen Kaijie, founder of Macaron AI. Photo source: Macaron AI.The AI company said its model surpassed GLM-5.2 by training four billion additional parameters through specialized LoRA adapters.<br>Chen Kaijie is a serial entrepreneur who left Duke University before graduating. He previously built MidReal, an artificial intelligence-powered interactive storytelling platform, and launched Macaron, a personal agent app that topped Product Hunt’s daily rankings on its first day. 36Kr spoke with him to learn more about Macaron and Mind Lab, the company behind it.

Mind Lab was founded in October 2025 and has more than 30 employees. Its founder, Andrew Chen, co-authored the FireAct paper with Shunyu Yao. The company’s team largely comes from xAI, DeepMind, DeepSeek, ByteDance Seed, MIT, Tsinghua University, and other companies and academic institutions.

Mind Lab’s direction closely aligns with the continual learning approach championed by Richard Sutton, a Turing Award winner widely regarded as the father of reinforcement learning, and Mira Murati, OpenAI’s former CTO.

At the main forum on the opening day of this year’s World Artificial Intelligence Conference, Sutton said the central path for the next generation of AI would be driven by experience, while the static, labeled-data paradigm had reached its ceiling.

DeepSeek has also brought continual learning to a wider audience. It has said continual learning is the problem the industry needs to solve after agents, and that it is a capability the next generation of models must possess.

Post-training and continual learning are becoming more important measures of model capability as the industry moves into its next phase.

Mind Lab released the Macaron-V1-Preview model in June. The business quickly gained momentum. Just two weeks after commercialization began, its annual recurring revenue reached USD 10 million.

Macaron-V1-Preview was built by attaching five LoRA (low-rank adaptation) expert modules to GLM-5.1. Each module had about one billion parameters.

Macaron-V1-Preview performed strongly across several benchmarks. It not only outperformed its GLM-5.1 base model but also surpassed models including GPT-5.4 and Claude Opus 4.6, according to benchmarks cited by the company. Mind Lab attributed the model’s improvement over the base model to the LoRA expert modules attached to it.

What drew attention was Mind Lab’s approach to post-training through MoL, or a mixture of LoRA adapters.

When the model performs different tasks, the system can dynamically switch to the expert module best suited to the task. As a user continues using the model, the accumulated data can also be distilled into a dedicated LoRA adapter that is continually updated as the model is called.

The preview version showed that the technical approach could work and provided initial market validation.

Mind Lab released and open-sourced the full version of Macaron-V1 on July 21. According to benchmarks published by the company, Macaron-V1 achieved state-of-the-art results in six of 12 tests. Its remaining scores were also relatively close to those of frontier models.

The release includes two models:

The flagship version, Venti, is a 748 billion-parameter model post-trained on GLM-5.2. Of those parameters, 744 billion come from the frozen GLM-5.2 base model. The remaining four billion come from four LoRA adapters trained by Mind Lab, each with about a billion parameters and responsibility for one of four capabilities: chat, agents, coding, and user interface generation.

The other model, Tall, is a lightweight version intended for local deployment. It has 50 billion parameters and was post-trained on Qwen 3.6.

Both versions natively support context windows of two million tokens.

In effect, the team enabled GLM-5.2 to exceed its previous capabilities by changing just four billion parameters.

For Mind Lab, entrepreneurship has been a process of repeatedly holding to a core direction while tearing down and rebuilding everything around it.

Beneath the external noise, a more far-reaching technical path has continued to evolve within the company: continual learning.

Mindverse, Mind Lab’s parent company, has raised USD 60 million since its founding. In early 2026, it completed a nearly USD 50 million Series A round led by Meituan’s investment arm, with participation from Oriza Hua, Shokz, Var Capital, and existing investors. Backers from earlier rounds include Ant Group, HSG, Being Capital, ZhenFund, and Gaorong Ventures.

Helping AI models improve over time

The company’s starting point can be traced to the FireAct paper that Andrew Chen wrote with Shunyu Yao in 2023.

At the time, they believed an agent’s task performance could be improved more effectively by training relevant data directly into the model than by relying on prompt engineering. That became the starting point for their bet on continual learning and post-training, although...