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Tencent releases Hy-Embodied-RxBrain-1.0, a unified multimodal foundation model for embodied cognition that combines language reasoning with visual imagination for understanding, world state prediction, and subgoal planning.
RxBrain is an embodied cognition foundation model that jointly reasons with language and visual imagination to represent embodied plans, using a unified multimodal Mixture-of-Transformers architecture. It achieves promising real-robot performance without large-scale action data.
A preprint arguing that AI can map known biology but cannot perform 'kind formation'—the minting of new variables and constraints—which requires the embodied engagement of human scientists. The authors propose a curriculum to train scientists as 'detectors of the unparameterized' to complement AI in biological discovery.
This paper advocates for incorporating enactive approaches to perception and cognition into AI, highlighting four key concepts: experience, action-perception inseparability, autonomy, and embodiment. It finds resonance with reinforcement learning but suggests broader integration of enactive ideas.
This paper proposes a minimal architecture for body-grounded perspective formation in artificial agents, extending prior work with an interoceptive viability signal and conative alignment mechanism to operationalize machine subjectivity from a phenomenological perspective.