arXiv:2608.30685v1 Announce Type: new
Abstract: Large language model (LLM) agents are increasingly deployed in user-facing services that require iterative tool use under dynamic business conditions....
By Wei Chen, Peilun Zhou, Zhaoyu Hu, Jiajun Chai, Zhongni Hou, Yufei Zhang, Derong Xu, Guojun Yin, Wei Lin, Zhi Zheng, Tong Xu
LiteTrajEval is a lightweight architecture designed for budget‑bounded trajectory evaluation of large‑language‑model agents. It creates compact domain‑specific rule profiles offline, preprocesses trajectories online to flag heuristic failure signals, and then uses a single rubric‑guided LLM judge to generate structured diagnostic reports. On public Magnetic‑One‑style and τ‑bench‑style datasets, LiteTrajEval improves failure‑localization alignment with human annotations by 20–35 percentage points and reduces cost and evaluation time by 6× and 8× respectively, and has been deployed in an enterprise agentic platform.
By Linh-An Phan, MingXue Wang, Guangyu Wu, Feng Pan, Zhaoyu Pang, Yanbin Zhang
arXiv:2607. 05638v1 Announce Type: cross Abstract: Teams deploying large language models in business contexts need evaluation systems, yet most treat evaluation as static model selection: run benchmarks, rank models, deploy the winner.
By Kenneth Benavides, Josh Fleischer, Danti Chen
AuditKIT is a source‑available evaluation library that unifies benchmark accuracy, judge‑based assessment, runtime performance, tool‑use behavior, and retrieval quality into a single result representation. It offers direction‑aware metric contracts, configuration fingerprints, and extensible metrics and backends, while providing turn‑aware tool‑call matching and distinguishing verified task outcomes from trajectory‑level diagnostics. Seven case studies demonstrate how answer extraction, runtime behavior, and metric selection can alter evaluation outcomes across model comparison, serving runtimes, retrieval, tool calling, and interoperability.
By Shreeyans Arora, Utsav Avaiya, Zera Lyngkhoi, Hem Gosalia, Ram Mohan Rao Kadiyala, Vinay Kumar Sankarapu, Pratinav Seth
arXiv:2606. 29193v1 Announce Type: cross Abstract: LLM-based agents are reshaping microservice operations into AgentOps, where benchmarks are key to evaluating failure diagnosis over multimodal observability data.
By Yuanhong Cai, Xiaohui Nie, Kanglin Yin, Changhua Pei, Yongqian Sun, Shenglin Zhang, Haibin Liu, Guiyang Liu, Xidao Wen, Fang Situ, Dan Pei
arXiv:2607. 04686v1 Announce Type: cross Abstract: Tool calling is central to modern language model agents, but aggregate benchmark scores often hide where tool use fails.
By Harsh Soni