A self-learning scientific agent for X-ray diffraction
A central challenge for scientific agents is to turn analytical experience into reusable expertise grounded in physic...
每天自动聚合 AI 领域最新动态
A central challenge for scientific agents is to turn analytical experience into reusable expertise grounded in physic...
Flow policies capture rich and diverse action distributions, and fine-tuning them with off-policy RL to improve beyon...
Partnered human-humanoid interaction couples locomotion with continuous physical contact. A humanoid needs to coordin...
Dual-process agents pair a fast policy with a slow deliberative model. In real-time settings the slow model usually r...
Text-conditioned full-body human-object interaction (HOI) generation requires synthesizing human motion and object tr...
Multi-teacher on-policy distillation (MOPD) is used in two settings. In common-domain composition, several teachers s...
Enterprise AI agents that share a memory store face two unaddressed risks: sensitive data can leak through legitimate...
Pixel-space diffusion models avoid the lossy VAE of latent models, which suggests an advantage on downstream tasks wh...
Training and evaluating interactive language agents typically requires rich user interactions, yet collecting human f...
Generating corner-case scenarios with appropriate adversity in a simulation environment is critical for testing an au...
Methods that recover individual latent variables of the world, from nonlinear ICA to dictionary learning and causal r...
Language-model systems can separate contextual computation, persistent storage, and exact execution instead of updati...