6 papers
From Where Things Are to What They Are For: Benchmarking Spatial-Functional Intelligence in Multimodal LLMs
Le Zhang, Jihan Yang, Soundarya Krishnan +11
Human-level agentic intelligence extends beyond low-level geometric perception, evolving from recognizing where things are to understanding what they are for. While existing benchm…
Cambrian-P: Pose-Grounded Video Understanding
Jihan Yang, Zifan Zhao, Xichen Pan +6
Camera pose matters. The position and orientation of each viewpoint define a shared spatial coordinate frame that relates observations across video frames. Yet this signal is large…
Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders
Shengbang Tong, Boyang Zheng, Ziteng Wang +7
Representation Autoencoders (RAEs) have shown distinct advantages in diffusion modeling on ImageNet by training in high-dimensional semantic latent spaces. In this work, we investi…
Cambrian-S: Towards Spatial Supersensing in Video
Shusheng Yang, Jihan Yang, Pinzhi Huang +12
We argue that progress in true multimodal intelligence calls for a shift from reactive, task-driven systems and brute-force long context towards a broader paradigm of supersensing.…
Benchmark Designers Should "Train on the Test Set" to Expose Exploitable Non-Visual Shortcuts
Ellis Brown, Jihan Yang, Shusheng Yang +2
Multimodal LLMs can answer many questions in vision-centric benchmarks without looking at the image, using linguistic priors and skewed answer distributions. Blind evaluation shows…
Traveling Across Languages: Benchmarking Cross-Lingual Consistency in Multimodal LLMs
Hao Wang, Pinzhi Huang, Jihan Yang +2
The rapid evolution of multimodal large language models (MLLMs) has significantly enhanced their real-world applications. However, achieving consistent performance across languages…