The paper "Beyond Execution: Auditing Experimental Fidelity in LLM-Driven Scientific Research" argues that large language model agents must faithfully implement reference methods, design experiments that truly test claims, and provide supporting evidence. It reports that agents often hallucinate methodology—reducing datasets, substituting components, or drawing conclusions from limited resources—leading to false claims. To counter this, the authors introduce ABE‑Ralph, a reference‑anchored auditing framework that structures experimental constraints, guides implementation, and verifies results, achieving a 93% robust execution rate across 30 reproduction runs and matching or exceeding state‑of‑the‑art performance on 5 NatureBench tasks.
"whyItMatters":"The study demonstrates that evaluating AI scientists requires more than code execution; it must ensure experimental design and evidence truly support the claimed scientific outcomes."
By Lezhi Yu, Xiaogang Xu, Yuhua Zhou, Shuibing He, Aimin Pan
arXiv:2608. 07346v1 Announce Type: new Abstract: With the rapid advancement of large language models (LLMs), harnesses have become essential infrastructure for deploying agents across a wide range of domains.
By Haoning Wang, Mingxun Zhang, Chenyue Yu, Yingjun Shang, Xia Hu, Guanchu Wang, Na Zou
arXiv:2608. 07346v2 Announce Type: replace Abstract: With the rapid advancement of large language models (LLMs), harnesses have become essential infrastructure for deploying agents across a wide range of domains.
By Haoning Wang, Mingxun Zhang, Chenyue Yu, Yingjun Shang, Xia Hu, Guanchu Wang, Na Zou
arXiv:2604. 16706v2 Announce Type: replace Abstract: Automated evaluation of tool-using large language model (LLM) agents is widely assumed to be reliable, yet this assumption is rarely validated against human annotation.
By Bhaskar Gurram
arXiv:2602. 02905v2 Announce Type: replace Abstract: Autonomous agents powered by large language models (LLMs) promise to accelerate scientific discovery end-to-end, but rigorously evaluating their capacity for verifiable discovery remains a central challenge.
By Zhen Wang, Fan Bai, Zhongyan Luo, Jinyan Su, Kaiser Sun, Xinle Yu, Jieyuan Liu, Kun Zhou, Claire Cardie, Mark Dredze, Zhiting Hu, Eric P. Xing
arXiv:2606. 18237v1 Announce Type: cross Abstract: Reproducing research results from papers and released code is central to scientific progress.
By Shanda Li, Qiuhong Anna Wei, Jingwu Tang, Valerie Chen, Nihar B Shah, Tim Dettmers, Yiming Yang, Ameet Talwalkar
arXiv:2609.08149v1 Announce Type: new
Abstract: SWE-Bench Pro has emerged as a standard benchmark for evaluating software engineering agents on challenging repository-level tasks. However, our analys...
By Pujun Zheng, Zixin Shang, Shufan Jiang, Wenhui Tian, Dongsheng Zhu, Zerun Ma, Dingbo Yuan, Qi Zhang
arXiv:2605. 06177v2 Announce Type: replace Abstract: Reproducing and comparing deep research agents today is hard: the same backbone evaluated on the same benchmark can report different accuracies across papers because the harness and tool registry differ, and integrating a new model into a comparable evaluation surface costs weeks of model-specific engineering.
By Jinge Wu, Hongjian Zhou, Mingde Zeng, Jiayuan Zhu, Junde Wu, Jiazhen Pan, Ayush Noori, Sean Wu, Honghan Wu, Fenglin Liu, David A. Clifton
Harbor Adapters is a unified evaluation infrastructure that ports over 80 agentic benchmarks, enabling arbitrary agents to be tested across complex environments. The authors performed a large‑scale evaluation of 8 models on 54 benchmarks, using Terminus‑2 and three native harnesses, revealing detailed agent capabilities and failure modes. They also created Harbor‑Index, a curated set of 82 challenging tasks from 29 benchmarks, designed to be affordable yet comprehensive, with the best model achieving a 28.0% pass rate.
By Lin Shi (Audrey), Haowei Lin (Audrey), Zixuan Zhu (Audrey), Xiaoyue Zhou (Audrey), Xiang Li (Audrey), Xiangning Lin (Audrey), Yaxuan Deng (Audrey), Han Xu (Audrey), Yuangang Li (Audrey), Shanda Li (Audrey), Zizhao Chen (Audrey), Hanwen Xing (Audrey), Harsh Raj (Audrey), Bo Chen (Audrey), Quan Shi (Audrey), Steven Dillmann (Audrey), Yipeng Gao (Audrey), Puneesh Khanna (Audrey), Ruofan Lu (Audrey), Chao Beyond Zhou (Audrey), Michael Yang (Audrey), Robert Zhang (Audrey), Siyuan Chai (Audrey), Jiayu Chang (Audrey), Yizhao Chen (Audrey), Xiaokun Chen (Audrey), Yiwei Dai (Audrey), Wenting Yang (Audrey), Hange Liu (Audrey), Minghao Liu (Audrey), Zihan Wang (Audrey), Adnan El Assadi (Audrey), Benedikt Stroebl (Audrey), E. Kelly Buchanan (Audrey), Han Meng (Audrey), Junwei He (Audrey), Longxuan Yu (Audrey), Radin Shayanfar (Audrey), Yukyung Lee (Audrey), Zhikang Dong (Audrey), Allen G Hart (Audrey), Anjiang Wei (Audrey), Anurag Kashyap (Audrey), Arpandeep Khatua (Audrey), Audrey Jixin Zheng (Audrey), Chengrui Ma (Audrey), David Heineman (Audrey), Dubing Chen (Audrey), Hai-Anh Trinh (Audrey), Haishuo Fang (Audrey), Hefan Zhang (Audrey), Hui Shen (Audrey), Issa Sugiura (Audrey), Jiankai Sun (Audrey), Jiechao Gao (Audrey), Junhong Lin (Audrey), Junnan Li (Audrey), Kai Yang (Audrey), Lei Hsiung (Audrey), Maoyu Wang (Audrey), Mengze Tang (Audrey), Nabil Omi (Audrey), Negin Raoof (Audrey), Nicholas Edwards (Audrey), Octavia Guo (Audrey), Orfeas Menis Mastromichalakis (Audrey), Pengliang Ji (Audrey), Przemys{\l}aw Hejman (Audrey), Qi Qi (Audrey), Qunshu Lin (Audrey), Richard Zhuang (Audrey), Rui Yang (Audrey), Ruichen Zheng (Audrey), Ryan Marten (Audrey), Shaghayegh Fazliani (Audrey), Shizheng Hou (Audrey), Sicong Jiang (Audrey), Sijie Li (Audrey), Song Bian (Audrey), Terry Yue Zhuo (Audrey), Tianqing Wu (Audrey), Tom Tang (Audrey), Wanjia Zhao (Audrey), Weihao Xuan (Audrey), Wenhua Liang (Audrey), Xian Liu (Audrey), Xin Lan (Audrey), Xuan Zhang (Audrey), Xuandong Zhao (Audrey), Yanchuan Tang (Audrey), Yifan Jiang (Audrey), Yijiang Li (Audrey), Yitong Guan (Audrey), Yizhi Li (Audrey), Yonghui Liu (Audrey), Yuheng Tang (Audrey), Yujun (Audrey), Mao, Yunfei Zhao, Yuxin Wang, Yuxuan Tang, Zhenheng Tang, Zhifei Li, Ziruo Wang, Ziyu She, Kaiyuan Liu, Iheb Chaabane, Yuxin Tang, Xiangyi Li, Andy Konwinski, Boxuan Li, Leon Liangyu Chen, Alex Dimakis, Nicholas Carlini, Soroush Vosoughi, Di He, Etash Guha, Benjamin Feuer, Mike Merrill, Ludwig Schmidt, Alex Shaw
arXiv:2606. 22678v2 Announce Type: replace-cross Abstract: Agentic coding harnesses - such as Agent-Skills, Superpowers, and Agent-Rigor - are increasingly deployed to augment underlying LLMs for real-world software engineering tasks.
By Meher Bhaskar Madiraju, Meher Sai Preetam Madiraju
arXiv:2606. 04990v1 Announce Type: cross Abstract: Large language model (LLM)-based agents increasingly solve complex tasks by interacting with external tools, retrieval systems, memory modules, environments, and other agents.
By Yiqi Wang, Jiaqi Zhang, Taotao Cai, Zirui Liu, Qingqiang Sun, Zequn Sun, Zhangkai Wu, Mingkai Zhang, Yanming Zhu
arXiv:2607. 25891v1 Announce Type: new Abstract: Evaluating AI agents in interactive environments is hindered by fragmented tasks, scaffolds, verifiers, and scoring rules.
By Stefan Krsteski, Charlotte Meyer, Guillaume Allegre, Tony O'Halloran, Alexandre Sallinen