Vision-Language Model Ensembles Achieve Human-Expert Accuracy for Galaxy Merger Classification

arXiv:2606.00415 · astro-ph.GA, astro-ph.IM · Submitted 2026-05-29 · Read on arXiv

astro-ph.GA, astro-ph.IM

Submitted: 2026-05-29

Updated: 2026-09-24

Terminology

• Accuracy — 56× in this paper · explained in 1 episode
• Calibration — 23× in this paper · explained in 7 episodes
• Core — 13× in this paper · explained in 2 episodes
• Bayesian Framework — 8× in this paper · explained in 3 episodes
• Prompt Engineering — 8× in this paper · explained in 4 episodes
• Degeneracy — 4× in this paper · explained in 3 episodes
• Perplexity — 2× in this paper · explained in 3 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• AGN — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Science Institute for the European Space Agency — 1× in this paper
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• Resummation of the C-Parameter Sudakov Shoulder Using Effective Field Theory
• DART: Leveraging Multi-Agent Disagreement for Tool Recruitment in Multimodal Reasoning
• Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report

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