AgentAudit is an open, extensible framework that evaluates the full lifecycle of AI agents, assessing planning, tool selection, execution, memory, and reasoning across ten dimensions such as instruction integrity, security, and alignment. Unlike existing benchmarks that focus on single aspects, AgentAudit analyzes the entire execution trace to attribute failures to specific stages. The framework was tested on five large language models, revealing significant differences in trustworthiness even among models with similar task‑completion performance.
By Shrey Nag, Sachita, Abhishek Kumar Singh, Lipi Goel, Rajeshwar Singh Janwar
arXiv:2609.35889v1 Announce Type: cross
Abstract: Tool-using language-model agents select and execute third-party artifacts. Different implementations can return the requested output while producing...
By Xiaoyu Xu, Zi Liang, Minxin Du, Qipeng Xie, Qingqing Ye, Yuyuan Li, Haibo Hu
AutoTuneBench introduces a trustworthy measurement protocol for evaluating how large language model agents auto‑tune GPU kernels and serving engines. The benchmark addresses four failure modes—strawman baselines, machine‑dependent timing, saturated tasks, and infrastructure defects—by enforcing code‑frozen protocols, database validation, anti‑cheat checks, pre‑registered comparisons, and external result anchoring. Using this protocol, the authors demonstrate that previously reported speedups are inflated, revealing more modest improvements across different engines and machines.
By Li Chen
The paper introduces an action‑class diagnostic framework for multi‑turn tool‑calling in large language model agents, breaking failures into action‑class miscalibration and action‑execution failure across a four‑class action space (TOOL_CALL, ASK, REFUSE, CONFIRM). It defines a self‑revealing upper bound (Acc GAR) to expose state‑grader masking of miscalibration and shows that miscalibration is a significant, previously hidden failure mode, especially for heavily tool‑trained families. The study demonstrates that calibration can be reshaped by context‑only perturbations, but the effects vary widely across models and perturbation mechanisms, underscoring the need for diagnostics beyond aggregate accuracy.
By Kangjia Zhao, Jiajun Li, Haozhan Shen, Wei Chow, Linfeng Li, Hang Song, Lingdong Kong, Chen Zhi, Tiancheng Zhao, Songhua Liu, Jianwei Yin
arXiv:2609.00052v1 Announce Type: cross
Abstract: Commercial LLM APIs advertise a specific foundation model, but the served backbone may be silently substituted, quantized, or wrapped, for example to...
By Xun Wang, Bihe Zhao, Michael Backes, Franziska Boenisch, Adam Dziedzic
arXiv:2609.37315v1 Announce Type: cross
Abstract: Tool-using agents are entering settings where a wrong action carries real cost, and the benchmarks certifying them grade what each simulated tool cal...
By Rohith Reddy Bellibatlu, Zichong Wang, Wenbin Zhang