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Vision-language-action (VLA) models achieve strong performance across embodied manipulation tasks by unifying visual ...
Spatial intelligence requires agents to actively acquire visual evidence and integrate observations into a coherent r...
Modern language models are increasingly asked to handle several tasks within a single prompt. Doing so requires solvi...
Despite the success of video world models, extending them to multiple agents is difficult because it requires generat...
Geometric Relative Positional Encoding has become the primary mechanism for injecting spatial inductive biases into m...
Diffusion-based video matting has recently emerged as a leading approach, leveraging video diffusion priors from inte...
As a prototypical latent world model, Joint-Embedding Predictive Architectures (JEPAs) provide strong semantic repres...
Training LLM agents to interact with users over multiple turns is challenging because the learning signal depends not...
LLM agents are commonly trained with RLVR, but the sparse nature of typical RLVR rewards makes credit assignment chal...
Large language models often answer factual questions incorrectly in closed-book settings, and such errors are usually...
Real-world observations are noisy and often contain excessive task-irrelevant information. Effective world modeling a...
Recent research-level physics-reasoning benchmarks for LLMs reveal a failure mode that lies not in knowledge or execu...