activity
20242026
most citedReplicationBench: Can AI Agents Replicate Astrophysics Research Papers?

1 citations · 1 across the 3 of their papers we have counts for

collaborators

7 papers

astro-ph.IM2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…

cs.SE2026

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Mike A. Merrill, Alexander G. Shaw, Nicholas Carlini +82

AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not…

cs.CL20251 cited

ReplicationBench: Can AI Agents Replicate Astrophysics Research Papers?

Christine Ye, Sihan Yuan, Suchetha Cooray +10

Frontier AI agents show increasing promise as scientific research assistants, and may eventually be useful for extended, open-ended research workflows. However, in order to use age…

astro-ph.HE2025

The Advanced X-ray Imaging Satellite (AXIS) Community Science Book

Michael Koss, Nafisa Aftab, Steven W. Allen +395

The AXIS Community Science Book represents the collective effort of 592 scientists worldwide to define the transformative science enabled by the Advanced X-ray Imaging Satellite (A…

astro-ph.CO2025

Lens Model Accuracy in the Expected LSST Lensed AGN Sample

Padmavathi Venkatraman, Sydney Erickson, Phil Marshall +13

Strong gravitational lensing of active galactic nuclei (AGN) enables measurements of cosmological parameters through time-delay cosmography (TDC). With data from the upcoming LSST…

cs.LG2025

Learning Representations of Event Time Series with Sparse Autoencoders for Anomaly Detection, Similarity Search, and Unsupervised Classification

Steven Dillmann, Juan Rafael Martínez-Galarza

Event time series are sequences of discrete events occurring at irregular time intervals, each associated with a domain-specific observational modality. They are common in domains…