Fair-MPO improves multi-turn agentic reasoning via multi-level preference optimization and a fairness objective that fixes long-horizon credit assignment and data imbalance, achieving state-of-the-art benchmark results.
OmniSpace improves autonomous vehicle MLLM spatial reasoning via camera pose injection, multi-view epipolar attention, and 3D geometric distillation without auxiliary 3D models, surpassing existing methods across planning, risk detection, and language benchmarks.
DriveSpatial benchmarks vision-language models' spatiotemporal autonomous driving intelligence, finding a 28.4-point human gap with cognitive scene construction as the key bottleneck.