Search papers, labs, and topics across Lattice.
7
0
10
CAP is proposed, a single-stage humanoid locomotion policy that recovers this signal with a perceptive world-model encoder trained as a learned denoiser to reconstruct clean depth from a corrupted input, together with a co-active proprioceptive variational encoder that supplies depth-free body-state information.
Leading open-source MLLMs achieve an alarming 61.9% average accuracy on fine-grained fire and smoke reasoning, but adapting vision encoders on just 7% targeted domain data more than triples classification performance.
Routing-enabled federated learning can now leverage hidden subpopulations within clients, leading to substantial gains in prediction accuracy and routing efficiency.
Users can now intuitively grasp a robot's inferred goals through its motion, reducing control effort and enhancing collaboration.
Fed-CausalDiff enables federated learning to perform causal inference with unprecedented accuracy by decoupling global and local data influences.
daVinci-kernel outperforms the best existing RL-trained model in GPU kernel optimization by effectively co-evolving skill selection and execution strategies.
MiniMax-M2 proves that massive parameter counts don't always translate to better agentic performance; strategic activation of a smaller subset can unlock frontier-level intelligence.
