AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,462 papers
Modern generative models, such as GANs, diffusion architectures, and autoregressive systems, now produce facial images that are nearly indistinguishable from authentic photographs. This capability makes detecting forged images increasingly difficult, raising serious concerns about identity theft, fraud, and misinformat...
This work introduces NeoMME, a family of 260M and 800M-parameter Multimodal and Multilingual bidirectional Encoders that process multilingual text and raw image patches in a single bidirectional Transformer encoder.
This work introduces DocHop, a benchmark for integrated chart--context reasoning in document-style images and constructs DocHop via a stochastic logic-first generation pipeline with controllable reasoning depth and visual density, to enable systematic evaluation.
Predictive world models enable robots to plan by imagining the outcomes of their actions, but their value for control hinges on generating many rollouts quickly. This creates a bottleneck for diffusion-based world models: multistep sampling makes each rollout expensive, limiting large-scale action search at inference t...
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Graphic designers often blend visual concepts to communicate multiple ideas within a single image, leveraging positive and negative space to create balance, emphasis, and aesthetic appeal. While computational methods have begun to support automatic concept blending, they largely overlook the role of spatial composition...
This work introduces GapFill, a tool grounded in professional practices that reduces the effort of gap detection, zooming, and color selection and suggests appropriate fill colors by referencing surrounding regions, leveraging the flat-color nature of anime-style images.
This paper investigates the influence of task-irrelevant context by formulating it as a controlled intervention within a binary visual judgment framework and reveals a robust geometric regularity: contextconditioned margins follow a consistent affine transformation of their context-free counterparts.
This work introduces IntroConformal, a training-free Conformal Risk Control (CRC) framework that provides finite-sample, distribution-free factuality guarantees and proposes verification probability, a stronger score capturing the model's self-administered judgment on claim factuality.
dLVLMs reverse the yes-bias of AR models in binary visual queries and collapse to near-zero accuracy on underrepresented racial groups with opposite-polarity gender bias, suggesting reliability is shaped by the generative paradigm together with training data.
This work investigates the fundamental design principles required for robust visual text representation learning and trains Pixel Linguist II, a native-resolution vision encoder trained with on-the-fly rendering, unified contrastive grounding, and a multilingual curriculum over 280M training examples.
This paper introduces InSight, a benchmark for agentic claim verification over interactive visualizations and evaluates state-of-the-art models, revealing that interactive verification remains a non-trivial challenge.
This work provides a systematic explanation of why modality gap reduction does not consistently improve CLIP zero-shot accuracy from the perspective of downstream decision structure and suggests that gap correction should be evaluated not only by average alignment, but also by its impact on downstream prediction struct...
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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