Models, methods, and the machinery of intelligence.
arXiv
Verdict: RoutineLong-horizon agents have drawn growing attention for their ability to work autonomously -- from hours to days -- on economically valuable tasks, a capability essential to automated discovery and recursive…
Scaling interactive environments is important for training long-horizon agents through reinforcement learning.
Generating information-rich visual artifacts such as slides, posters, and dashboards as code is appealing because the outputs are structured, editable, and verifiable.
Visual agents must turn video observations into timely decisions to track progress and choose what to do next.
City-scale 3D grounding is commonly formulated as candidate ranking, which requires precomputed instance proposals and becomes inefficient in large outdoor scenes.
Recent advances in vision-language models (VLMs) have substantially improved the capabilities of GUI agents, while memory augmentation has emerged as a promising approach for reusing ....
Large language models have evolved from single-turn chatbots into interactive agents capable of long-horizon reasoning.
Chest X-ray (CXR) interpretation often requires integrating visual findings with task-specific evidence rather than relying on direct visual recognition alone.
A robot needs a world model that predicts how its actions will change the scene and a policy that chooses those actions.