6 papers
Same or Not? Enhancing Visual Perception in Vision-Language Models
Damiano Marsili, Aditya Mehta, Ryan Y. Lin +1
Vision-language models (VLMs) excel at broad visual understanding but remain coarse-grained, exhibit visual biases, and miss subtle visual details. Existing training corpora reinfo…
No Labels, No Problem: Training Visual Reasoners with Multimodal Verifiers
Damiano Marsili, Georgia Gkioxari
Visual reasoning is challenging, requiring both precise object grounding and understanding complex spatial relationships. Existing methods fall into two camps: language-only chain-…
NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models
Mouadh Yagoubi, Yasser Dahou, Billel Mokeddem +12
Existing benchmarks have proven effective for assessing the performance of fully trained large language models. However, we find striking differences in the early training stages o…
Visual Agentic AI for Spatial Reasoning with a Dynamic API
Damiano Marsili, Rohun Agrawal, Yisong Yue +1
Visual reasoning -- the ability to interpret the visual world -- is crucial for embodied agents that operate within three-dimensional scenes. Progress in AI has led to vision and l…
Find Any Part in 3D
Ziqi Ma, Yisong Yue, Georgia Gkioxari
Why don't we have foundation models in 3D yet? A key limitation is data scarcity. For 3D object part segmentation, existing datasets are small in size and lack diversity. We show t…
TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis
Sabera Talukder, Yisong Yue, Georgia Gkioxari
This work studies the problem of time series analysis with generalist (or foundation) models, which are models trained across many data domains. Drawing inspiration from the widesp…