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RHO transforms how AI agents can autonomously refine their skill sets without requiring labeled data, achieving a remarkable 19% increase in performance in just one optimization round.
VLMs struggle more with *seeing* than *thinking*, and targeted pre-training on visual perception alone unlocks surprisingly large gains in downstream reasoning.
Memory-augmented LLMs get a strategic upgrade: MemMA uses multi-agent reasoning to proactively guide memory construction and repair, leading to significant performance gains.
