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Sampling-Guided Policy Search (SGPS), which couples recurring action-target refinement by sampling-based model-predictive control with first-order policy optimization with first-order policy optimization, and shows that refinement improves policy learning beyond initialization and tracking alone.
The Decoupled Embodiment Model (DEM), which pairs a fine-tuned DINOv3 encoder and a frozen NeoBERT encoder with a MeanFlow head that generates each action chunk in a single forward pass, achieves observed success comparable to state-of-the-art VLM-backbone policies under the evaluation protocol.
Tracking text progress directly from latent speech tokens rather than rendered audio cuts alignment error by 88% while slashing compute overhead by 20x.
X2Streaming-ASR slashes commit latency to as low as 27 ms while achieving superior recognition accuracy, setting a new benchmark for streaming ASR systems.
Forget hand-coded adaptation rules: Meta-TTL learns policies that let language agents self-improve at test time, generalizing zero-shot to unseen environments.
