Models, methods, and the machinery of intelligence.
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Hugging Face
Verdict: RoutineLarge language model (LLM) routing distributes inference work across different models, advancing the cost-quality Pareto frontier of LLM serving.
General-purpose service robots need navigation systems that can handle diverse human requests in unfamiliar environments, combining task generality with scene generality.
Interactive virtual worlds allow agents to learn through exploration and interaction.
Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action.
Assistants from 2011 answered and waited, and agents from 2023 did a task and stopped.
Autoregressive video generation requires denoising the current frames while writing their key-value representations as context for future predictions.
Reinforcement learning (RL) is the central training paradigm for advancing large foundation models towards self-improvement.
General-purpose agents increasingly write code, use tools, and complete complex digital tasks, raising the question of how far these capabilities carry into the physical world.