AIBullisharXiv – CS AI · 8h ago7/10
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Scaling Self-Evolving Agents via Parametric Memory
Researchers introduce TMEM, a parametric memory framework that enables AI agents to learn and evolve within a single episode by updating LoRA weights online, rather than merely retrieving frozen memories. This approach combines explicit memory storage with fast adaptive weights, allowing agents to genuinely improve their policy during rollouts and demonstrates consistent performance gains across multiple benchmarks.