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Jintao Huang

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#artificial intelligence Preprint Oct 2026

PersonaScienceBench: Evaluating Scientific Agents with Diverse Simulated Researchers

PersonalityScienceBench evaluates how agents collaborate with diverse researchers alongside whether they complete the task, and across eighteen modelconfigurations, task performance, fact recovery, and self-repetition reveal different strengths.

Si-Yang Wu, Wei-Zhi Zhang, Chen Yang et al. · 0 citations
#artificial intelligence Review Oct 2026

AgentPersonaBench: Benchmarking Persona-Driven User Simulation

We introduce AgentPersonaBench (APB), a benchmark evaluating whether persona conditioning faithfully steers downstream agent behavior. While language models are increasingly deployed for persona-driven user simulation, existing benchmarks primarily evaluate conversational styling or self-reports rather than authentic b...

Jin-Tao Huang, Yi-Fan Wang, Hong-Yuan Shen et al. · 0 citations
Open access Sep 2026

Efficient Acidic H2O2 Electrosynthesis over Co Atoms Anchored on Nitrogen-Doped Hierarchical Porous Carbon

The two-electron oxygen reduction reaction (2e− ORR) presents a promising route for the on-site production of hydrogen peroxide (H2O2), offering a green alternative to energy-consuming anthraquinone process. However, the high selectivity toward the competing 4e− ORR over the desired 2e− pathway leads to low Faradaic ef...

Xiao Huang, Zi-Hao Zhan, Hao Niu et al. · 0 citations
Review Sep 2026

A six-dimensional framework for water splitting electrocatalysts: from intrinsic activity to practical performance.

Electrocatalytic water splitting using renewable electricity is widely regarded as a promising route to sustainable hydrogen production. However, its practical application is hindered by the sluggish kinetics of both the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER). A critical challenge rem...

Yu'an Li, Hui-Ying Li, Jin-Tao Huang et al. · 0 citations
Review Aug 2026

MatrAIx: Simulating the World with 8.3 Billion Persona Agents

MatrAIx is introduced, a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users and provides an end-to-end infrastructure for evaluating AI systems and digital products with diverse simulated human users.

Xiaomin Li, Yuexing Hao, Jian Hou et al. · 1 citation

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