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This paper examines the role of Novel View Synthesis (NVS) in geometric representation learning. In principle, NVS should reason about 3D scene structure, thereby enabling transferable multi-view geom

Keerthi Kaashyap, Dennis Anthony, Akshay Krishnan

We introduce 4DCodeBench, a benchmark for 4D inverse graphics through code generation, in which agents reconstruct dynamic scenes from video as executable graphics programs. To accomplish this, agents

Ruihong Shen, Žiga Kovačič, Peter Kulits

Continual learning treats degradation on previously seen data as evidence of failure, a convention inherited from settings with a stationary prediction target, where a correct label remains correct in

Nishit Anand, Ramani Duraiswami, Dinesh Manocha

Ribonucleic acid (RNA) functions through conformational changes that are not fully captured by static structures. However, large-scale standardized RNA dynamics data remain limited, and existing appro

Yiming Huang, Lennart Bastian, Hanqun Cao

Inspired by human vision, we introduce a framework using active gaze to enable fine-grained bimanual manipulation with only a single stereo camera. EyeRobot 2.0 physically attends to a 3D fixation poi

Kush Hari, Justin Kerr, Nidhya Shivakumar

We study the minimization of sums of smooth strongly convex functions over undirected graphs, with each function held by one agent and communication restricted to neighbors in the graph. Existing dece

Kuangyu Ding, Gesualdo Scutari

The choice of post-training data for large language models substantially affects downstream performance. Gradient-based data selection is a popular approach that ranks training data by how well their

Lyuxin David Zhang, Eric Wong, Surbhi Goel

Scarcity of labeled data limits development of deep learning biomarkers in oncology. We develop a two-stage AI model predicting pathological complete response (pCR) to neoadjuvant therapy in breast ca

Jungkyu Park, Dhruva Biswas, Joseph Cappadona

The dynamics of cells, organisms, and fluids are often modeled as probability distributions evolving over time. Reconstructing and extrapolating this evolution from unpaired snapshots requires assumpt

Fedor Sergeev, Markus Heinonen, Daniel Waxman

LLM-guided evolutionary methods, such as AlphaEvolve, have emerged as powerful approaches for challenging computational optimization problems, such as circle packing. However, prior work typically opt

Hui Chen, Xuan Qi, James Xu Zhao

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