3 papers
cs.CV2026
Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence
Diankun Wu, Fangfu Liu, Yi-Hsin Hung +1
Recent advancements in Multimodal Large Language Models (MLLMs) have significantly enhanced performance on 2D visual tasks. However, improving their spatial intelligence remains a…
cs.CV2026
Spatial-TTT: Streaming Visual-based Spatial Intelligence with Test-Time Training
Fangfu Liu, Diankun Wu, Jiawei Chi +7
Humans perceive and understand real-world spaces through a stream of visual observations. Therefore, the ability to streamingly maintain and update spatial evidence from potentiall…
cs.LG2025
xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations
Kaiyuan Chen, Yixin Ren, Yang Liu +30
We introduce xbench, a dynamic, profession-aligned evaluation suite designed to bridge the gap between AI agent capabilities and real-world productivity. While existing benchmarks…