TIDE: Trajectory-based Diagnostic Evaluation of Test-Time Improvement in LLM Agents

Recent advances in autonomous LLM agents demonstrate their ability to improve performance through iterative interaction with the environment. We define this paradigm as Test-Time Improvement (TTI). However, the mechanisms under how and why TTI succeed or fail remain poorly understood, and existing evaluation metrics fail to capture their task optimization efficiency, behavior adaptation after erroneous actions, and the specific utility of working memory. To address these gaps, we propose Test-time Improvement Diagnostic Evaluation (TIDE), a lightweight, agent-agnostic and environment-agnostic framework that decomposes TTI into three comprehensive and interconnected dimensions. The framework measures (1) the overall temporal dynamics of task completion and (2) identifies whether performance is constrained by recursive looping behaviors or (3) by burdensome accumulated memory. Through extensive experiments, \ours~highlights that improving agent performance calls for explicitly optimizing the interaction dynamics between the agent and the environment.