Trains a spatial vision-language model to transfer across categorical, metric, and temporal reasoning tasks by replaying heterogeneous supervision chronologically. It also evaluates adaptive frame selection as a separate inference-time improvement.
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.
Rewinds world-model trajectories to test alternative robot actions and turn outcome comparisons into action-level training signals. Real-robot data continually corrects the model; across three physical tasks, success rates beat the strongest baseline by 10.67–11.85 percentage points.
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.
Conditions robot action generation on predicted visual and motion features, capturing physical dynamics without generating future video during control. Reports results across four simulation benchmarks.