# Macaron-V1：面向开放持续学习的开源智能体模型家族

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-10 08:00
- AIHOT 分数：68
- AIHOT 链接：https://aihot.virxact.com/items/cmso2m6os082orofwmyy8pmdt
- 原文链接：https://arxiv.org/abs/2608.09819

## AI 摘要

Macaron-V1 是一个开源智能体模型家族，通过 Mixture-of-LoRA（MoL）架构冻结基座模型、组合专家 LoRA 适配器，并在每次用户交互时选择其中一个 LoRA，以支持部署后的持续学习。

## 正文

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.