AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,462 papers
PXDepth is a discriminative monocular depth model that separates global context modeling from pixel-level depth prediction, and combines faithful local geometry with competitive global depth accuracy while remaining efficient at inference.
Augmentations that model the physical causes of domain shift improve cross-source transfer at no cost to in-domain accuracy, and the improvement survives a selection-disjoint, contamination-free evaluation.
This work introduces a new setting, denoted as Criterion-Conditional In-Context Learning (CC-ICL), where models must infer the latent criterion from context and adjust predictions accordingly under fixed task semantics, and proposes two complementary metrics, Criterion Invariance and Criterion Sensitivity, to evaluate...
Predicting distant metastasis from the digital H&E slides of the primary tumor is a critical yet challenging task in computational pathology. Multiple Instance Learning (MIL) approaches can attend to subdomains in whole slide images (WSIs) that harbor features of pre-metastatic cancer regions. However, conventional MIL...
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APT-Tune is proposed, a parameter-efficient recipe that teaches VLMs to use causal transitions without forgetting how to answer video questions, and substantially improves APT recall while also improving event-level video transfer.
From YAML-first specifications that decouple contents, placement, behavior, and agent exposure, MagicSim constructs diverse executable worlds spanning task families, interaction regimes, physics, layouts, sensors, avatars, and robot embodiments in one reset-and-step loop.
This work introduces MMLongEmbed, the first comprehensive benchmark for evaluating MEMs in long-context scenarios, and finds that current architectures rely heavily on superficial feature matching and struggle to capture deep semantic and structural dependencies.
The Prefix-Adaptive Block Diffusion Model (PA-BDM) is proposed, which replaces intra-block bidirectional denoising with causal denoising from prefix to suffix and treats the block size as a maximum candidate range rather than a fixed commitment unit.
This paper presents Star-Fusion, a multi-modal architecture that reformulates orientation estimation as a discrete topological classification task, and leverages spherical K-Means clustering to partition the celestial sphere into K topologically consistent regions, effectively mitigating coordinate wrapping artifacts.
This work proposes CF-VLA, a coarse-to-fine two-stage formulation that restructures action generation into a coarse initialization step that constructs an action-aware starting point, followed by a single-step local refinement that corrects residual errors.
SketchVLM is presented, a training-free, model-agnostic framework that enables VLMs to produce non-destructive, editable SVG overlays on the input image to visually explain their answers, and finds that single-turn generation already achieves strong accuracy and annotation quality, and multi-turn generation opens up fu...
Diffusion models can be challenged in the low signal-to-noise regime, where they have to make pixel-level predictions despite the presence of high noise. The geometric intuition is akin to using the finest stroke for oil painting throughout, which may be ineffective. We therefore study \emph{stroke-size control} as a c...
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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