Lawrence Keunho Jang

Faster computer-use agents are bottlenecked by their speed, but existing benchmarks are irreproducible; this new standardized framework reveals counterintuitive speed-efficiency trade-offs and that current open-weight models lag behind.

Claude Opus 4.6 outperforms its peers, solving over half of the complex tasks in a personalized desktop environment, revealing critical gaps in current AI capabilities.

iOSWorld reveals that even state-of-the-art models falter in multi-app reasoning, achieving only 37% accuracy, underscoring the complexity of personal context in AI interactions.

Today's best web agents are shockingly inefficient, achieving only 1.15% trajectory efficiency on realistic long-horizon tasks, revealing a critical need to move beyond simple success rates.