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3,363 papers
The UniQueR model learns a compact set of 3D anchor points that act as explicit geometric queries, enabling the network to infer scene structure, including geometry in occluded regions--in a single forward pass.
FILT3R, a training-free latent filtering layer that casts recurrent state updates as stochastic state estimation in token space, is proposed, and it is demonstrated that gains shrink in stable regimes as uncertainty contracts with accumulated evidence, and rise when genuine scene change increases process uncertainty.
This work proposes AtomicVLA, a unified planning-and-execution framework that jointly generates task-level plans, atomic skill abstractions, and fine-grained actions, and introduces a flexible routing encoder that automatically assigns dedicated atomic experts to new skills, enabling continual learning.
This work proposes US-JEPA, a self-supervised framework that adopts the Static-teacher Asymmetric Latent Training (SALT) objective and demonstrates that masked latent prediction provides a stable and efficient path toward robust ultrasound representations.
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Attention-Guided Dynamic Watermarking (AGMark), a novel framework that embeds detectable signals while largely preserving visual-semantic fidelity, is proposed, taking a significant step toward reliability-preserving multimodal watermarking.
TimeBlind is presented, a diagnostic benchmark for compositional spatio-temporal understanding that demonstrates that even frontier models rely heavily on static visual shortcuts rather than genuine temporal logic, positioning TimeBlind as a vital diagnostic tool for next-generation video understanding.
This work designs and studies a simple yet natural operator named 1S-DAug, which comprises geometric perturbations with controlled noise injection and image-conditioned denoising, and obtains positive results on well-established image-classification benchmarks across four datasets and multiple models.
A central problem in self-supervised learning is preventing representation collapse. Most methods avoid it through global mechanisms, such as contrastive expansion, variance constraints, decorrelating dimensions, or enforcing certain output distributions. In this work, we study a different design: short-range repulsion...
TangramPuzzle is introduced, a benchmark comprising 668 validated configurations and 1,336 instances for evaluating compositional spatial reasoning under geometric constraints, and the Tangram Construction Expression is formulated to encode tangram configurations with machine-verifiable geometric specifications.
This work introduces an Entity-Attention Kullback-Leibler (KL) regularizer that aligns the detector's cross-attention with scientific entities annotated for each panel, providing a stable conditioning signal that also improves localization and applies Group Relative Policy Optimization (GRPO) with a panel-level Entity-...
This work highlights MedGround as a scalable, data-driven approach to anchor medical language to verifiable visual evidence and presents MedGround-35K, a novel multimodal medical dataset.
This study shows that combining DL with LLMs can produce useful clinical narratives, but current LLMs remain unreliable for high-stakes medical decisions and provides a clearer view of their limits and a path for building safer reasoning systems.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
A Blog post by H company on Hugging Face
A Blog post by Liquid AI on Hugging Face
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
Human-Computer Interaction and Visualization
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