---
id: 20261029-T0-16
title: "The Trinity：多模态智能体的能力驱动递归自我改进框架"
title_en: "The Trinity: Capability-Driven Recursive Self-Improvement for Multimodal Agents"
url: https://ai.daily.yangsir.net/daily/20261029-T0-16
issue_date: 2026-10-29
publish_date: 2026-10-28T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2610.36077
---

# The Trinity：多模态智能体的能力驱动递归自我改进框架

arXiv新论文提出The Trinity框架，把多模态智能看作世界与智能体在人类引导下的协同演化。规模化这一过程面临两个瓶颈：构建异构、可验证的环境成本高昂，人工Harness迭代速度也难以跟上。该框架尝试用能力驱动的递归自我改进来突破这两个限制。论文目前为预印本，具体实验数据需查阅原文。

## English Version

**The Trinity: Capability-Driven Recursive Self-Improvement for Multimodal Agents**

A new arXiv paper introduces The Trinity, a framework that frames multimodal intelligence as human-guided co-evolution between worlds and agents through interaction. Scaling this process hits two bottlenecks: building heterogeneous, verifiable environments is costly, and manual Harness iteration cannot keep pace. The Trinity proposes capability-driven recursive self-improvement to address both. The paper is a preprint; detailed experimental results are in the full text.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2610.36077)

**详情页**：https://ai.daily.yangsir.net/daily/20261029-T0-16

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