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Adversarial attacks can be effectively mitigated in LVLMs by jointly optimizing visual and semantic supervision, leading to enhanced robustness across diverse tasks.
Learning from real-world environments follows a precise log-sigmoid scaling law, with agent performance and learning speed improving dramatically over time.
LLM agent memory architectures matter, and a systematic comparison reveals a new SOTA method by combining the best of existing approaches.
