arXiv cs.AI· Runxuan Liu, Jingqi Shi, Baoxin Wang, Honghong Zhao, Haoyuan Xu, Yueqing Sun, Jiafeng Liang, Tao He, Dayong Wu, Qi Gu, Shijin Wang, Ming Liu, Bing Qin·· 2 天前AI 评分26
EvolvingBrain：面向开放世界多模态智能体自进化的自适应技能系统
EvolvingBrain: An Adaptive Skill System for Multi-modal Agents Self-Evolution in Open Worlds
AI 导读
研究者提出 EvolvingBrain，用自适应技能系统驱动多模态智能体在开放世界中自进化，包含分层技能自进化与状态感知自适应执行两部分。前者从模型轨迹中提取过程模式与问题模式，生成技能并形成由 Domain、Plan、Action/Trap 技能组成的分层技能架构；后者将长程交互总结为结构化摘要记忆，指导状态感知技能融合策略。
正文 · 原文
Authors:Runxuan Liu, Jingqi Shi, Baoxin Wang, Honghong Zhao, Haoyuan Xu, Yueqing Sun, Jiafeng Liang, Tao He, Dayong Wu, Qi Gu, Shijin Wang, Ming Liu, Bing Qin
Abstract:Multi-modal agents have made significant progress in interacting with open worlds. However, their ability to self-evolve remains limited. Supervised fine-tuning enables imitation but not active experience organization, while outcome-based reinforcement learning promotes exploration yet underuses intermediate trajectory information. Therefore, we propose EvolvingBrain, which uses an adaptive skill system as the brain for agent self-evolution, consisting of two following components. Stratified Skill Self-Evolution: extracts Procedural Patterns and Problematic Patterns from model-specific trajectories, summarizes experience by generating skills, and evolves through multiple interactions with the open world, ultimately forming a Stratified Skill Architecture consisting of Domain, Plan, and Action/Trap Skills, which provides the agent with a multi-granularity knowledge repository and a novel evolution approach. State-Aware Adaptive Execution: summarizes long-horizon interactions into Structured Abstractive Memory and uses it to guide the State-Aware Skill Fusion Strategy, enabling the agent to utilize knowledge according to its current state. Experiments on VAB-Minecraft and DiscoveryWorld demonstrate that EvolvingBrain improves success rates on open-world tasks and exhibits generalization capabilities. Our code is provided in the supplementary materials and will be publicly released in the camera-ready version.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.20786 [cs.AI] |
| (or arXiv:2610.20786v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20786 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Runxuan Liu [view email]
[v1]
Wed, 14 Oct 2026 18:30:07 UTC (5,223 KB)
来源：arXiv cs.AI · arxiv.org