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Stop hard-coding reasoning strategies for your LLM agent: a learned router that dynamically picks the best paradigm for each task boosts performance by up to 5.5%, beating even the best fixed strategy.
Embodied agents can now collaboratively reason about space and manipulate objects in the real world, thanks to a new reinforcement learning approach that fuses their egocentric viewpoints into a world-centric understanding.
By explicitly bridging the gap between on-body appearances and flat layouts, BridgeDiff achieves state-of-the-art virtual try-off results, generating more realistic and structurally sound flat-garment representations.
