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

• URL: http://arxiv.org/abs/2602.02196v2
• Date: Tue, 03 Feb 2026 04:28:15 GMT
• Title: TIDE: Trajectory-based Diagnostic Evaluation of Test-Time Improvement in LLM Agents
• Authors: Hang Yan, Xinyu Che, Fangzhi Xu, Qiushi Sun, Zichen Ding, Kanzhi Cheng, Jian Zhang, Tao Qin, Jun Liu, Qika Lin,
• Abstract summary: Recent advances in autonomous LLM agents demonstrate their ability to improve performance through iterative interaction with the environment.<n>We propose Test-time Improvement Diagnostic Evaluation (TIDE), an agent-agnostic and environment-agnostic framework that decomposes TTI into three comprehensive and interconnected dimensions.
• Score: 43.376952807616256
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

Related papers

• Act, Think or Abstain: Complexity-Aware Adaptive Inference for Vision-Language-Action Models [7.802379200026965]
  We propose an adaptive framework that dynamically routes VLA execution based on the complexity of the perceived state.<n>Our approach transforms the VLA's vision-language backbone into an active detection tool by projecting latent embeddings into an ensemble of parametric and non-parametric estimators.
  arXiv  Detail & Related papers  (2026-03-05T13:14:41Z)
• Rethinking the Role of Entropy in Optimizing Tool-Use Behaviors for Large Language Model Agents [54.18201810286764]
  Tool-using agents based on Large Language Models (LLMs) excel in tasks such as mathematical reasoning and multi-hop question answering.<n>In long trajectories, agents often trigger excessive and low-quality tool calls, increasing latency and degrading inference performance.<n>We propose using entropy reduction as a supervisory signal and design two reward strategies to address the differing needs of optimizing tool-use behavior.
  arXiv  Detail & Related papers  (2026-02-02T12:52:14Z)
• From Capabilities to Performance: Evaluating Key Functional Properties of LLM Architectures in Penetration Testing [5.7613138934999455]
  Large language models (LLMs) are increasingly used to automate or augment penetration testing, but their effectiveness and reliability across attack phases remain unclear.<n>We present a comprehensive evaluation of multiple LLM-based agents, from single-agent to modular designs, across realistic penetration testing scenarios.
  arXiv  Detail & Related papers  (2025-09-16T21:51:59Z)
• MEJO: MLLM-Engaged Surgical Triplet Recognition via Inter- and Intra-Task Joint Optimization [52.149337961205624]
  We propose a framework that empowers both inter- and intra-task optimization for surgical triplet recognition.<n>For inter-task optimization, we introduce the Shared-Specific-Disentangled (S$2$D) learning scheme that decomposes representations into task-shared and task-specific components.<n>For intra-task optimization conflicts, we develop a Coordinated Gradient Learning (CGL) strategy, which dissects and rebalances the positive-negative ambiguities.
  arXiv  Detail & Related papers  (2025-09-16T09:48:52Z)
• Active Test-time Vision-Language Navigation [60.69722522420299]
  ATENA is a test-time active learning framework that enables a practical human-robot interaction via episodic feedback on uncertain navigation outcomes.<n>In particular, ATENA learns to increase certainty in successful episodes and decrease it in failed ones, improving uncertainty calibration.<n>In addition, we propose a self-active learning strategy that enables an agent to evaluate its navigation outcomes based on confident predictions.
  arXiv  Detail & Related papers  (2025-06-07T02:24:44Z)
• PIPA: A Unified Evaluation Protocol for Diagnosing Interactive Planning Agents [12.052972947563424]
  Existing benchmarks predominantly evaluate agent performance based on task completion as a proxy for overall effectiveness.<n>We propose PIPA, a unified evaluation protocol that conceptualizes the behavioral process of interactive task planning agents.<n>Our analyses show that agents excel in different behavioral stages, with user satisfaction shaped by both outcomes and intermediate behaviors.
  arXiv  Detail & Related papers  (2025-05-02T21:27:10Z)
• On the Role of Feedback in Test-Time Scaling of Agentic AI Workflows [71.92083784393418]
  Agentic AI (systems that autonomously plan and act) are becoming widespread, yet their task success rate on complex tasks remains low.<n>Inference-time alignment relies on three components: sampling, evaluation, and feedback.<n>We introduce Iterative Agent Decoding (IAD), a procedure that repeatedly inserts feedback extracted from different forms of critiques.
  arXiv  Detail & Related papers  (2025-04-02T17:40:47Z)
• Interactive Agents to Overcome Ambiguity in Software Engineering [61.40183840499932]
  AI agents are increasingly being deployed to automate tasks, often based on ambiguous and underspecified user instructions.<n>Making unwarranted assumptions and failing to ask clarifying questions can lead to suboptimal outcomes.<n>We study the ability of LLM agents to handle ambiguous instructions in interactive code generation settings by evaluating proprietary and open-weight models on their performance.
  arXiv  Detail & Related papers  (2025-02-18T17:12:26Z)
• Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges [27.474011433615317]
  Continual learning (CL) enables the development of models and agents that learn from a sequence of tasks. We investigate the factors that contribute to the performance differences between task-agnostic CL and multi-task (MTL) agents.
  arXiv  Detail & Related papers  (2022-05-28T17:59:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.