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LLMs can reason better and generate more diverse outputs by projecting negative samples onto a positive subspace during reinforcement learning.
Safety-aligned GUI agents are surprisingly vulnerable to simple, model-agnostic visual distractions, repeatedly falling for the same overlaid UI elements even after successfully completing a task.
