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MacArena: Benchmarking Computer Use Agents on an Online macOS Environment

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arXiv:2606. 06560v1 Announce Type: new Abstract: Computer-use agents (CUAs) operate graphical user interfaces (GUIs) through vision and control primitives, and their capabilities have advanced rapidly, driven in part by standardized online evaluation benchmarks such as OSWorld, which serve both as evaluation tools and as training environments for reinforcement learning.

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Authors: Victor Muryn, Maksym Shamrai, Sofiia Mazepa, Yehor Khodysko
Released: 2026-06-08 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because MacArena: Benchmarking Computer Use Agents on an Online macOS Environment overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, under, eval, source, line, rate, control, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.06560

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