arXiv AI August 11, 2026 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

WuYuEval: A Multi-Level Benchmark for Large Language Models in Solid Waste Management

Read the original on arXiv AI →

arXiv:2608. 07529v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as technical assistants, but their competence in solid waste management (SWM) remains difficult to assess because existing benchmarks emphasize general knowledge rather than professional decisions under engineering, environmental, and policy constraints.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 10

InvestPhilBench: A Multi-Layer Benchmark for Evaluating Large Language Model Procedural Reasoning in Expert Investment Philosophy

arXiv:2606. 25984v2 Announce Type: replace Abstract: Large language models are increasingly deployed as investment research assistants, yet no benchmark tests whether they can accurately reconstruct and apply the specific procedural decision frameworks of expert investors.

By Mingguang Chen, Bo Qu

llms benchmarks

arXiv Machine Learning

Jun 8

Elmes*: Automated Construction of Fine-Grained Evaluation Rubrics for Large Language Models in Long-Tail Educational Scenarios

arXiv:2606. 06546v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know.

By Tao Liu, Ye Lu, Ruohua Zhang, Siyu Song, Wentao Liu, Aimin Zhou, Hao Hao

llms agents benchmarks safety

arXiv AI

Jul 17

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation

arXiv:2607. 14109v1 Announce Type: cross Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language understanding.

By Inder Preet, Shuxin Lin, Dhaval Patel

llms nlp benchmarks

arXiv Machine Learning

Jun 25

InvestPhilBench: A Multi-Layer Dynamic Benchmark for Evaluating Large Language Model Procedural Reasoning in Expert Investment Philosophy

arXiv:2606. 25984v1 Announce Type: cross Abstract: Large language models are increasingly deployed as investment research assistants, yet no benchmark tests whether they can accurately reconstruct and apply the specific procedural decision frameworks of expert investors.

By Mingguang Chen, Bo Qu

llms benchmarks

arXiv AI

5d ago

TsuGO: Probing Search Efficiency in LLM Reasoning via Go Life-and-Death Problems

arXiv:2608. 13221v1 Announce Type: new Abstract: The evaluation of LLM reasoning is moving from final-answer accuracy to process-level assessment, yet existing methods still fail to capture how models plan reasoning paths and allocate reasoning resources--that is, how they organize search.

By Shunwen Bai, Ziping Ma, Chaoyang Zhang, Yarong Wang, Jiale Liu, Zhen Qin, Qingpei Guo

llms agents benchmarks safety

arXiv AI

Jun 26

NuclearQAv2: A Structured Benchmark for Evaluating Domain-Science Competence in Large Language Models

arXiv:2606. 27047v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong performance across a wide range of tasks, but ensuring their reliability in highly technical domains remains a significant challenge.

By Henry Shaowu Yuchi, Michal Kucer, Benjamin H. Sims, Selma Peterson, Emily Taylor

llms benchmarks

Related stories

InvestPhilBench: A Multi-Layer Benchmark for Evaluating Large Language Model Procedural Reasoning in Expert Investment Philosophy

Elmes*: Automated Construction of Fine-Grained Evaluation Rubrics for Large Language Models in Long-Tail Educational Scenarios

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation

InvestPhilBench: A Multi-Layer Dynamic Benchmark for Evaluating Large Language Model Procedural Reasoning in Expert Investment Philosophy

TsuGO: Probing Search Efficiency in LLM Reasoning via Go Life-and-Death Problems

NuclearQAv2: A Structured Benchmark for Evaluating Domain-Science Competence in Large Language Models