arXiv AI By Pau Arnal, Khaled Denfir, Danylo Smahliuk, Amrut Avhad, Marcus A. Castro

EuroExec: Frontier Language Models Fall Short of Expert Judgment on European Executive Decision Tasks

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arXiv:2608. 04549v1 Announce Type: cross Abstract: Frontier LLMs are increasingly put to use on open-ended complex questions, different in nature from the ones they are typically evaluated on.

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arXiv AI
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SAGE: Scalable AI Governance & Evaluation

arXiv:2602. 07840v3 Announce Type: replace-cross Abstract: Evaluating relevance in large-scale search systems is fundamentally constrained by the governance gap between nuanced, resource-constrained human oversight and the high-throughput requirements of production systems.

By Benjamin Le, Xueying Lu, Nick Stern, Wenqiong Liu, Igor Lapchuk, Xiang Li, Baofen Zheng, Kevin Rosenberg, Jiewen Huang, Zhe Zhang, Abraham Cabangbang, Satej Milind Wagle, Jianqiang Shen, Raghavan Muthuregunathan, Abhinav Gupta, Mathew Teoh, Andrew Kirk, Thomas Kwan, Jingwei Wu, Wenjing Zhang
arXiv Computation and Language
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IDEAlign: Comparing Ideas of Large Language Models to Domain Expert

IDEAlign introduces a new protocol for evaluating the similarity of large language model (LLM) annotations to expert judgments. It uses pick‑the‑odd‑one‑out tasks to capture expert similarity and benchmarks various similarity methods—including text embeddings, topic models, and LLM-as-a-judge—against these human ratings. Applied to educational datasets, the study finds that most metrics miss nuanced expert dimensions, with LLM-as-a-judge performing best yet still insufficient for full expert alignment.

By Hyunji Nam, Lucia Langlois, James Malamut, Mei Tan, Dorottya Demszky
arXiv AI
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WuYuEval: A Multi-Level Benchmark for Large Language Models in Solid Waste Management

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By Yi Zhang, Hongyang Wang, Zheng Hao Leong, Zihao Wu, Kaijun Lin, Zhixing Pan, Qixun Huangfu, Wei Ren, Wenyan Wu, Fangyun Wang, Wenting Yu, Hengyu Lin, Muling Yang, Zongguo Wen