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Latent reasoning can now leverage outcome-reward reinforcement learning, achieving better performance with less computational overhead than traditional methods.
PalmClaw achieves a staggering 94.9% reduction in task completion time by transforming how mobile agents interact with device capabilities.
MLLMs are failing to recognize and effectively utilize physical tools, with top models achieving only 21% task completion in real-world scenarios.
Images can serve as a powerful standalone medium for reasoning, achieving nearly double the token efficiency of traditional text methods.
Multi-modification image retrieval is now possible: TEMA handles complex, real-world instructions that go beyond simple changes, outperforming existing methods on new datasets M-FashionIQ and M-CIRR.
