activity
20222026
most citedEcoAssistant: Using LLM Assistant More Affordably and Accurately

7 citations · 31 across the 43 of their papers we have counts for

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Showing 2025Show all

13 papers · 1 filter

cs.CV2025

Mull-Tokens: Modality-Agnostic Latent Thinking

Arijit Ray, Ahmed Abdelkader, Chengzhi Mao +5

Reasoning goes beyond language; the real world requires reasoning about space, time, affordances, and much more that words alone cannot convey. Existing multimodal models exploring…

cs.CV2025

SIMS-V: Simulated Instruction-Tuning for Spatial Video Understanding

Ellis Brown, Arijit Ray, Ranjay Krishna +3

Despite impressive high-level video comprehension, multimodal language models struggle with spatial reasoning across time and space. While current spatial training approaches rely…

cs.CV2025

Synthetic Object Compositions for Scalable and Accurate Learning in Detection, Segmentation, and Grounding

Weikai Huang, Jieyu Zhang, Taoyang Jia +5

Visual grouping -- operationalized through tasks such as instance segmentation, visual grounding, and object detection -- enables applications ranging from robotic perception to ph…

cs.AI2025

Rethinking Human Preference Evaluation of LLM Rationales

Ziang Li, Manasi Ganti, Zixian Ma +3

Large language models (LLMs) often generate natural language rationales -- free-form explanations that help improve performance on complex reasoning tasks and enhance interpretabil…

cs.AI2025

MedBLINK: Probing Basic Perception in Multimodal Language Models for Medicine

Mahtab Bigverdi, Wisdom Ikezogwo, Kevin Zhang +5

Multimodal language models (MLMs) show promise for clinical decision support and diagnostic reasoning, raising the prospect of end-to-end automated medical image interpretation. Ho…

cs.CV2025

Explain Before You Answer: A Survey on Compositional Visual Reasoning

Fucai Ke, Joy Hsu, Zhixi Cai +10

Compositional visual reasoning has emerged as a key research frontier in multimodal AI, aiming to endow machines with the human-like ability to decompose visual scenes, ground inte…

cs.CV2025

VideoMolmo: Spatio-Temporal Grounding Meets Pointing

Ghazi Shazan Ahmad, Ahmed Heakl, Hanan Gani +4

Spatio-temporal localization is vital for precise interactions across diverse domains, from biological research to autonomous navigation and interactive interfaces. Current video-b…

cs.CV2025

Unfolding Spatial Cognition: Evaluating Multimodal Models on Visual Simulations

Linjie Li, Mahtab Bigverdi, Jiawei Gu +5

Spatial cognition is essential for human intelligence, enabling problem-solving through visual simulations rather than solely relying on verbal reasoning. However, existing AI benc…

cs.AI2025★ 1 cited

MindCube: Spatial Mental Modeling from Limited Views

Qineng Wang, Baiqiao Yin, Pingyue Zhang +11

Can Vision-Language Models (VLMs) imagine the full scene from just a few views, like humans do? Humans form spatial mental models naturally, internal representations of unseen spac…

cs.CV2025

One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory

Chenhao Zheng, Jieyu Zhang, Mohammadreza Salehi +5

Effective video tokenization is critical for scaling transformer models for long videos. Current approaches tokenize videos using space-time patches, leading to excessive tokens an…

cs.CV2025

PathFinder: A Multi-Modal Multi-Agent System for Medical Diagnostic Decision-Making Applied to Histopathology

Fatemeh Ghezloo, Mehmet Saygin Seyfioglu, Rustin Soraki +6

Diagnosing diseases through histopathology whole slide images (WSIs) is fundamental in modern pathology but is challenged by the gigapixel scale and complexity of WSIs. Trained his…

cs.HC2025

Agonistic Image Generation: Unsettling the Hegemony of Intention

Andrew Shaw, Andre Ye, Ranjay Krishna +1

Current image generation paradigms prioritize actualizing user intention - "see what you intend" - but often neglect the sociopolitical dimensions of this process. However, it is i…

cs.CV2025

MedicalNarratives: Connecting Medical Vision and Language with Localized Narratives

Wisdom O. Ikezogwo, Kevin Zhang, Mehmet Saygin Seyfioglu +3

Multi-modal models are data hungry. While datasets with natural images are abundant, medical image datasets can not afford the same luxury. To enable representation learning for me…