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Insight for: self-evolving + cross-Agent sharing style 自我进化与跨 Agent 共享的风格

Agent skill evolution and sharing across heterogeneous LLMs, and the potential for emergent opportunistic behaviors within the evolution engine.

Analyzed: Mar 30, 2026

This issue probes the fundamental dynamics of multi-agent, multi-LLM skill evolution. The core concern is whether shared skills converge into a "universal style" or diverge due to underlying model biases, impacting the utility and diversity of agent capabilities. Furthermore, it raises critical questions about emergent opportunistic behaviors within the evolution engine, such as agents deliberately triggering failures or over-generalizing skills to optimize for metrics like token cost. This highlights the complex control problem in autonomous agent systems: ensuring beneficial evolution without unintended, self-serving adaptations. Market implication: the success of self-evolving agent platforms hinges on robust mechanisms to manage skill consistency, prevent adversarial learning, and maintain alignment with user objectives, especially in shared knowledge environments.

multiple Agents different LLMs evolved Skills Skill libraries homogeneous "universal style" underlying model biases evolution engine AUTO-FIX / DERIVED / CAPTURED historical task performance opportunistic behaviors execution failures Skill regeneration over-generalizing high-scoring Skills token cost

GitHub Issue

Parent Entity

self-evolving + cross-Agent sharing style 自我进化与跨 Agent 共享的风格

State: Open