AI

EvolvingBrain: An Adaptive Skill System for Multi-modal Agents Self-Evolution in Open Worlds

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 16/10/2026, 04:03:25 UTC.

Briefings

EvolvingBrain proposes an adaptive skill system to enable multi-modal agents to self-evolve in open worlds. The system extracts procedural and problematic patterns from trajectories, summarizes experience into skills, and evolves through interactions. Experiments on VAB-Minecraft and DiscoveryWorld show improved success rates and generalization capabilities compared to supervised fine-tuning and outcome-based reinforcement learning.

Key points

• EvolvingBrain uses an adaptive skill system composed of two components: Stratified Skill Self-Evolution and State-Aware Adaptive Execution.
• Stratified Skill Self-Evolution extracts procedural and problematic patterns from model-specific trajectories to form a multi-granularity knowledge repository.

Context

Multi-modal agents have made significant progress in open worlds but self-evolution remains limited. Supervised fine-tuning enables imitation but not active experience organization, while outcome-based reinforcement learning promotes exploration yet underuses intermediate trajectory information. EvolvingBrain addresses these gaps by using an adaptive skill system as the brain for agent self-evolution.

Limitations

Evidence is from a preprint (arXiv:2610.20786v1) and is subject to review. Experiments were conducted on VAB-Minecraft and DiscoveryWorld, and results are limited to these specific environments. The code is not publicly released yet, and the camera-ready version is not available for verification.

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