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

Hang Yan, Xinyu Che, Fangzhi Xu, Qiushi, Zichen Ding, Kanzhi Cheng, Jian Zhang, Tao Qin, Jun Liu, Lin

TIDE: Trajectory-based Diagnostic Evaluation of Test-Time Improvement in LLM Agents: 35 upvotes on Hugging Face Daily Papers, #9 of 53 papers on 2026-02-05. Day-by-day upvote history.

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 for task completion. To address these gaps, we propose Test-time Improvement Diagnostic Evaluation (TIDE), an 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 primarily constrained by recursive looping behaviors or (3) by burdensome accumulated memory. Through extensive experiments across diverse agents and environments, TIDE highlights that improving agent performance requires more than scaling internal reasoning, calling for explicitly optimizing the interaction dynamics between the agent and the environment.

Paper page on Hugging Face · arXiv

Data: hysts-bot-data/daily-papers-stats and the Daily Papers API. Open data: tardellirs/paper-pulse-data. Sister project: Model Pulse, the download history of every model on the Hub.