AI Video Papers2026
Infers implicit user intent from continuous video and adapts both response timing and content as goals change. Its benchmark covers explicit, implicit, and dynamic intents, while hierarchical memory tracks state across sustained interactions.
AI Agent Papers2026
Coordinates world creation, trajectory exploration, and Harness evolution to improve multimodal agents. It organizes tasks by shared capabilities rather than surface categories, reporting mean gains of 34 points on training benchmarks and 26 on unseen benchmarks.
AI Video Papers2026
Generates continuing video segments from multi-turn natural-language instructions and camera control. Its segment-wise causal attention preserves scene context while confining action instructions to their intended time windows.