Models, methods, and the machinery of intelligence.
arXiv
Verdict: NotableSpatial intelligence lets an agent perceive and act in the physical world, and many of its judgments must be made at interaction rates: which spatial relation holds, how far an object is, or where to place it.
Online reinforcement learning (RL) with verifiable rewards has driven rapid progress in mathematics, coding, and tool use, but scaling this success to GUI agents remains challenging due to the high cost of interactive…
Existing agent benchmarks typically evaluate agents on well-specified tasks defined by issue reports, specifications, or task tickets.
Looped Mixture-of-Experts (MoE) models combine parameter reuse with sparse computation to improve performance under matched budgets.
With the rapid development of multimodal large language models (MLLMs), evaluating their video understanding capabilities has become increasingly important.
Data agents use code to process heterogeneous files and derive analytical insights.
How can we build an agent that thinks beyond human language, yet still acts through it in the external environment?
A few well-annotated datasets for land-cover semantic segmentation have recently been introduced to advance the field of earth observation technologies.
Vision-language models (VLMs) are increasingly deployed in multi-agent systems to automate complex computer-use workflows, yet existing benchmarks mostly assume consistent, cooperative ....