@inproceedings{yan-etal-2026-tide,
title = "{TIDE}: Trajectory-based Diagnostic Evaluation of Test-Time Improvement in {LLM} Agents",
author = "Yan, Hang and
Che, Xinyu and
Xu, Fangzhi and
Sun, Qiushi and
Ding, Zichen and
Cheng, Kanzhi and
Zhang, Jian and
Qin, Tao and
Liu, Jun and
Lin, Qika",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.639/",
pages = "14705--14729",
ISBN = "979-8-89176-459-0",
abstract = "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, TIDE highlights that improving agent performance calls for explicitly optimizing the interaction dynamics between the agent and the environment."
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<abstract>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, TIDE highlights that improving agent performance calls for explicitly optimizing the interaction dynamics between the agent and the environment.</abstract>
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%0 Conference Proceedings
%T TIDE: Trajectory-based Diagnostic Evaluation of Test-Time Improvement in LLM Agents
%A Yan, Hang
%A Che, Xinyu
%A Xu, Fangzhi
%A Sun, Qiushi
%A Ding, Zichen
%A Cheng, Kanzhi
%A Zhang, Jian
%A Qin, Tao
%A Liu, Jun
%A Lin, Qika
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yan-etal-2026-tide
%X 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, TIDE highlights that improving agent performance calls for explicitly optimizing the interaction dynamics between the agent and the environment.
%U https://aclanthology.org/2026.emnlp-main.639/
%P 14705-14729
Markdown (Informal)
[TIDE: Trajectory-based Diagnostic Evaluation of Test-Time Improvement in LLM Agents](https://aclanthology.org/2026.emnlp-main.639/) (Yan et al., EMNLP 2026)
ACL
- Hang Yan, Xinyu Che, Fangzhi Xu, Qiushi Sun, Zichen Ding, Kanzhi Cheng, Jian Zhang, Tao Qin, Jun Liu, and Qika Lin. 2026. TIDE: Trajectory-based Diagnostic Evaluation of Test-Time Improvement in LLM Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 14705–14729, Budapest, Hungary. Association for Computational Linguistics.