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Trust in AI agent networks hinges on blockchain, which can redefine how agents interact across diverse platforms and stakeholders.
Real-world GUI agents can achieve over 72% success in complex mobile tasks by leveraging a unique hybrid training approach that integrates real-device execution and adaptive learning from failures.
Agents can now escape the Self-Confirmation Trap, leading to more reliable experience learning and improved self-evolution.
Dataset condensation, already vulnerable to backdoor attacks, now faces a far stealthier threat: InkDrop leverages decision boundary uncertainty to hide malicious triggers, making detection significantly harder.
By explicitly modeling tooth relationships, TCATSeg achieves state-of-the-art accuracy in 3D dental model segmentation, even in challenging pre-orthodontic cases.
RAIN reconciles privacy, robustness, and verifiability in federated learning under Shuffle-DP, achieving up to 90x lower communication cost and 10x faster aggregation while defending against poisoning attacks.
