Unifies detection, localization, counting, and OCR with segmentation and matting in one vision-language model, using structured tokens for coordinates and pixel-level outputs.
Connects scene understanding, long-horizon visual planning, and robot control by alternating language reasoning with video prediction. An agentic variant learns when generated visual evidence is worth using.
Simulates closed-loop interactions between people and wearable AI assistants to test whether multimodal models can guide real-world tasks. Its 120 multi-turn benchmark tasks are scored against verifiable simulator states.
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 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.