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LIRA is introduced, a local cross-layer action-conditioning mechanism that formulates VLM-to-action conditioning as depth-aware information routing and improves the principal aggregate metrics over the VLA-Adapter baseline under the same 0.5B-parameter configuration.
This work presents GigaBrain-0.7, an embodied foundation model with substantially improved generalization across diverse robot embodiments, and introduces one-stage alignment training that jointly optimizes vision-language understanding and multi-embodiment action generation.
GigaWorld-Policy-0.5 preserves the training benefits of future visual dynamics while improving inference efficiency for robot control, and introduces a Mixture-of-Transformers architecture that separates visual dynamics modeling and action generation into specialized experts.
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