activity
20142026
most citedEverybody Dance Now

23 citations · 64 across the 18 of their papers we have counts for

collaborators

21 papers

cs.CV2026

KilometerVision: A New Frontier for Large-Scale Spatial Intelligence in VLMs

Aravindh Mahendran, Michael King, Matthew Koichi Grimes +12

We push the frontier of large-scale spatial intelligence in Vision-Language Models (VLMs) and introduce the first benchmark that probes geographical layout understanding from real-…

cs.CV2026

Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale

A. Sophia Koepke, Daniil Zverev, Shiry Ginosar +1

The Platonic Representation Hypothesis posits that neural networks trained on different modalities (e.g., text and images) converge toward a shared representation of reality. If tr…

cs.CV2026

Forecasting Motion in the Wild

Neerja Thakkar, Shiry Ginosar, Jacob Walker +3

Visual intelligence requires anticipating the future behavior of agents, yet vision systems lack a general representation for motion and behavior. We propose dense point trajectori…

cs.AI2025

Empowerment Gain and Causal Model Construction: Children and adults are sensitive to controllability and variability in their causal interventions

Eunice Yiu, Kelsey Allen, Shiry Ginosar +1

Learning about the causal structure of the world is a fundamental problem for human cognition. Causal models and especially causal learning have proved to be difficult for large pr…

cs.CV2025

Frozen Forecasting: A Unified Evaluation

Jacob C Walker, Pedro Vélez, Luisa Polania Cabrera +7

Forecasting future events is a fundamental capability for general-purpose systems that plan or act across different levels of abstraction. Yet, evaluating whether a forecast is "co…

cs.CV2025

Poly-Autoregressive Prediction for Modeling Interactions

Neerja Thakkar, Tara Sadjadpour, Jathushan Rajasegaran +2

We introduce a simple framework for predicting the behavior of an agent in multi-agent settings. In contrast to autoregressive (AR) tasks, such as language processing, our focus is…