· ai
Synthetic Data Is Not About Replacing Reality. It Is About Questioning It.
A Quiet Moment in Data & Machine Learning The model performs well. The metrics look reassuring. The pipeline feels complete. And yet, something does not sit ri...
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- · ai
[Paper] Re-Depth Anything: Test-Time Depth Refinement via Self-Supervised Re-lighting
Monocular depth estimation remains challenging as recent foundation models, such as Depth Anything V2 (DA-V2), struggle with real-world images that are far from...
- · ai
[Paper] Adversarial Robustness of Vision in Open Foundation Models
With the increase in deep learning, it becomes increasingly difficult to understand the model in which AI systems can identify objects. Thus, an adversary could...
- · ai
[Paper] When Reasoning Meets Its Laws
Despite the superior performance of Large Reasoning Models (LRMs), their reasoning behaviors are often counterintuitive, leading to suboptimal reasoning capabil...
- · ai
[Paper] Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomy
Imitation learning (IL) enables autonomous behavior by learning from expert demonstrations. While more sample-efficient than comparative alternatives like reinf...
- · ai
[Paper] Humanlike AI Design Increases Anthropomorphism but Yields Divergent Outcomes on Engagement and Trust Globally
Over a billion users across the globe interact with AI systems engineered with increasing sophistication to mimic human traits. This shift has triggered urgent ...
- · ai
[Paper] RadarGen: Automotive Radar Point Cloud Generation from Cameras
We present RadarGen, a diffusion model for synthesizing realistic automotive radar point clouds from multi-view camera imagery. RadarGen adapts efficient image-...
- · ai
[Paper] Exploring the Effect of Basis Rotation on NQS Performance
Neural Quantum States (NQS) use neural networks to represent wavefunctions of quantum many-body systems, but their performance depends on the choice of basis, y...
- · ai
[Paper] Regularized Random Fourier Features and Finite Element Reconstruction for Operator Learning in Sobolev Space
Operator learning is a data-driven approximation of mappings between infinite-dimensional function spaces, such as the solution operators of partial differentia...
- · ai
[Paper] Weighted Stochastic Differential Equation to Implement Wasserstein-Fisher-Rao Gradient Flow
Score-based diffusion models currently constitute the state of the art in continuous generative modeling. These methods are typically formulated via overdamped ...
- · ai
[Paper] Learning vertical coordinates via automatic differentiation of a dynamical core
Terrain-following coordinates in atmospheric models often imprint their grid structure onto the solution, particularly over steep topography, where distorted co...
- · ai
[Paper] Visually Prompted Benchmarks Are Surprisingly Fragile
A key challenge in evaluating VLMs is testing models' ability to analyze visual content independently from their textual priors. Recent benchmarks such as BLINK...