arXiv:2605.21951v1 Announce Type: new Abstract: Achieving self-evolution in intelligent agents requires the continual accumulation of new knowledge across changing task sequences without forgetting previously acquired abilities. Existing approaches either internalize knowledge by updating model parameters, which induces catastrophic forgetting, or rely on external memory, which fails to genuinely enhance the model's intrinsic capabilities. We propose MoLEM, a generative mixture of latent memory framework based on a dynamic mixture-of-experts (MoE). We treat multiple experts as independent carr
This research addresses a fundamental challenge in AI development—continual learning without catastrophic forgetting—which is critical for the next generation of intelligent agents.
Achieving genuine self-evolution in AI directly impacts the capabilities and autonomy of future AI systems, moving them closer to true intelligence and reducing reliance on constant human oversight.
The proposed MoLEM framework offers a new architectural paradigm for AI that could enable more robust, adaptive, and genuinely self-evolving agents, bypassing limitations of current memory and learning approaches.
- · AI research labs
- · Generative AI developers
- · Autonomous systems manufacturers
- · Robotics companies
- · AI models without continuous learning capabilities
- · Companies reliant on static AI deployments
Self-evolving agents become more capable and require less retraining.
Accelerated development of more complex and autonomous AI applications across various industries.
Enhanced AI systems could fundamentally alter human-computer interaction and automation paradigms.
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Read at arXiv cs.LG