2.4k citations · 2.9k across the 65 of their papers we have counts for
15 papers · 1 filter
CoSPlan: Corrective Sequential Planning via Scene Graph Incremental Updates
Shresth Grover, Priyank Pathak, Akash Kumar +1
Vision Language Models (VLMs) have shown promising planning capabilities, yet their success remains confined to the text domain, leaving visual decision-making relatively underexpl…
Fara-7B: An Efficient Agentic Model for Computer Use
Ahmed Awadallah, Yash Lara, Raghav Magazine +9
Progress in computer use agents (CUAs) has been constrained by the absence of large and high-quality datasets that capture how humans interact with a computer. While LLMs have thri…
Physics Knowledge in Frontier Models: A Diagnostic Study of Failure Modes
Ieva Bagdonaviciute, Vibhav Vineet
While recent Vision-Language Models (VLMs) have achieved impressive progress, it remains difficult to determine why they succeed or fail on complex reasoning tasks. Traditional ben…
Just Do It!? Computer-Use Agents Exhibit Blind Goal-Directedness
Erfan Shayegani, Keegan Hines, Yue Dong +6
Computer-Use Agents (CUAs) are an increasingly deployed class of agents that take actions on GUIs to accomplish user goals. In this paper, we show that CUAs consistently exhibit Bl…
What MLLMs Learn about When they Learn about Multimodal Reasoning
Jiwan Chung, Neel Joshi, Pratyusha Sharma +2
Evaluation of multimodal reasoning models is typically reduced to a single accuracy score, implicitly treating reasoning as a unitary capability. We introduce MathLens, a benchmark…
OmViD: Omni-supervised active learning for video action detection
Aayush Rana, Akash Kumar, Vibhav Vineet +1
Video action detection requires dense spatio-temporal annotations, which are both challenging and expensive to obtain. However, real-world videos often vary in difficulty and may n…
Test-time Prompt Refinement for Text-to-Image Models
Mohammad Abdul Hafeez Khan, Yash Jain, Siddhartha Bhattacharyya +1
Text-to-image (T2I) generation models have made significant strides but still struggle with prompt sensitivity: even minor changes in prompt wording can yield inconsistent or inacc…
Out of Sight, Not Out of Context? Egocentric Spatial Reasoning in VLMs Across Disjoint Frames
Sahithya Ravi, Gabriel Sarch, Vibhav Vineet +2
An embodied AI assistant operating on egocentric video must integrate spatial cues across time - for instance, determining where an object A, glimpsed a few moments ago lies relati…
TEMPURA: Temporal Event Masked Prediction and Understanding for Reasoning in Action
Jen-Hao Cheng, Vivian Wang, Huayu Wang +11
Understanding causal event relationships and achieving fine-grained temporal grounding in videos remain challenging for vision-language models (VLMs). We propose TEMPURA (Temporal…
Grounding Task Assistance with Multimodal Cues from a Single Demonstration
Gabriel Sarch, Balasaravanan Thoravi Kumaravel, Sahithya Ravi +2
A person's demonstration often serves as a key reference for others learning the same task. However, RGB video, the dominant medium for representing these demonstrations, often fai…
Phi-4-reasoning Technical Report
Marah Abdin, Sahaj Agarwal, Ahmed Awadallah +20
We introduce Phi-4-reasoning, a 14-billion parameter reasoning model that achieves strong performance on complex reasoning tasks. Trained via supervised fine-tuning of Phi-4 on car…
HierarQ: Task-Aware Hierarchical Q-Former for Enhanced Video Understanding
Shehreen Azad, Vibhav Vineet, Yogesh Singh Rawat
Despite advancements in multimodal large language models (MLLMs), current approaches struggle in medium-to-long video understanding due to frame and context length limitations. As…
A Large-Scale Analysis on Contextual Self-Supervised Video Representation Learning
Akash Kumar, Ashlesha Kumar, Vibhav Vineet +1
Self-supervised learning has emerged as a powerful paradigm for label-free model pretraining, particularly in the video domain, where manual annotation is costly and time-intensive…
Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead
Vidhisha Balachandran, Jingya Chen, Lingjiao Chen +8
Inference-time scaling can enhance the reasoning capabilities of large language models (LLMs) on complex problems that benefit from step-by-step problem solving. Although lengtheni…
MM-GEN: Enhancing Task Performance Through Targeted Multimodal Data Curation
Siddharth Joshi, Besmira Nushi, Vidhisha Balachandran +4
Vision-language models (VLMs) are highly effective but often underperform on specialized tasks; for example, Llava-1.5 struggles with chart and diagram understanding due to scarce…