EP343: How AI agents escape infinite loops

Title: No Time Like the Present: Agentic Test-Time Training for LLM AgentsSource: http://arxiv.org/abs/2607.03441v1Summary:This paper introduces Agentic Test-Time Training (aTTT), a novel framework that continuously adapts model weights during multi-turn agent episodes to prevent performance degradation over long trajectories. By utilizing a token-level reweighting method to mitigate self-training drift on repeated states, it provides a highly foundational mechanism for sustaining agent reasoning and competence in long-horizon environments.