Скачать или смотреть SmartSnap: Agents That Prove GUI Success

In this AI Research Roundup episode, Alex discusses the paper: 'SmartSnap: Proactive Evidence Seeking for Self-Verifying Agents' SmartSnap tackles a key problem in agentic reinforcement learning for GUI tasks: expensive and unreliable post-hoc verification of whether a task is truly complete. Instead of sending long, noisy interaction trajectories to a verifier, it trains a self-verifying agent to proactively capture and submit minimal, decisive evidence while acting. The method formalizes evidence as atomic exhibits (action, next observation) and guides selection with the 3C Principles: Completeness, Conciseness, and Creativity. Verification is handled by an LLM-as-a-Judge with strict rules like no assumptions and success only with unequivocal proof, and training uses structured reward shaping to encourage valid and concise evidence. Paper URL: https://arxiv.org/abs/2512.22322 #AI #MachineLearning #DeepLearning #ReinforcementLearning #GUIAgents #LLMJudge #SelfVerification #AgenticRL

Resources:
Hugging Face model: https://huggingface.co/yolay/SmartSna...
Hugging Face model 2: https://huggingface.co/yolay/SmartSna...
Hugging Face model 3: https://huggingface.co/yolay/SmartSna...