Models, methods, and the machinery of intelligence.
arXiv
Verdict: NotableWorld models enable embodied agents to anticipate action consequences, yet jointly modeling locomotion and manipulation across diverse embodiments remains challenging due to heterogeneous action spaces and limited…
Multimodal large language models (MLLMs) have made rapid progress on visual reasoning, yet many tasks require evidence that is difficult to obtain through internal reasoning alone: our controlled study on 22 benchmarks…
Game engines and coding agents can specify the layout, motion, and camera of a scene, yet turning such coarse structure into realistic video requires a renderer that follows the structure and completes the appearance…
Decision models support routing, guardrails, and output verification in LLM systems.
Retrieval Augmented Generation (RAG) grounds large language models in external knowledge.
As LLMs expand their language coverage, evaluating how well they understand and represent diverse cultures becomes increasingly important.
Agent harnesses have become a central component of modern agentic systems, shaping how models use tools, manage context, and coordinate long-horizon interaction.
Using an LLM to generate training data for another is among the most general ways to train one, and it remains common practice.
Frontier coding agents are increasingly framed as end-to-end automated researchers, and ML for science offers a demanding test of that promise: improving a scientific model often requires ....