Topic: SmartSnap

We highlight the introduction of SmartSnap, a novel reinforcement learning training method that transforms GUI agents from passive executors into proactive self-verifiers. Instead of relying on complex external supervision or lengthy trajectory reviews, this framework enables agents to curate an evidence snapshot set following the 3C principles of completeness, conciseness, and creativity. Our analysis shows that this approach significantly reduces verification overhead, requiring an average of only 1.5 screenshots per task to confirm completion. Experimental results on AndroidLab demonstrate performance gains of up to 26.08%, remarkably allowing mid-sized models like Qwen3-32B to match the capabilities of massive models such as DeepSeek-V3 and Qwen3-235B. This shift towards proactive evidence seeking simplifies RL training for dynamic environments like mobile operating systems where state feedback is often transient or difficult to capture, marking a transition from brute-force execution to cognitive synergy.