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S. Dillmann

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#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
Review Sep 2026

Lens Modeling and Cosmological Inference from an Impure Sample of Galaxy-Galaxy Strong Lenses

The start of the Legacy Survey of Space and Time marks a new era for strong lensing science, where the number of strong lenses identified is expected to increase to $\mathcal{O}(10^5)$. In this paper we use a neural network to determine the precision with which lens parameters can be determined, using realistic simulat...

Philip Holloway, Aprajita Verma, Philip J. Marshall et al. · 0 citations

The Platonic Universe: Do Foundation Models See the Same Sky?

The results support the ARH over the strict PRH, demonstrate astronomy's value as an experimental framework for neural representation learning, and suggest that astro-foundation models can build on general-purpose pre-trained architectures, capitalizing on the broader open machine learning community's already-spent com...

UniverseTBD Trinidad Borrell, S. Dillmann, Kshitij Duraphe et al. · 6 citations

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