Recently, Multimodal Large Language Models (MLLMs) have been widely integrated into diffusion frameworks primarily as text encoders to tackle complex tasks such...
Existing video depth estimation faces a fundamental trade-off: generative models suffer from stochastic geometric hallucinations and scale drift, while discrimi...
Constructing scientific multimodal document reasoning datasets for foundation model training involves an inherent trade-off among scale, faithfulness, and reali...
Cross-entropy (CE) training provides dense and scalable supervision for language models, but it optimizes next-token prediction under teacher forcing rather tha...
Reasoning LLMs-as-Judges, which can benefit from inference-time scaling, provide a promising path for extending the success of reasoning models to non-verifiabl...
Intelligent systems across physics, language and perception often exhibit factorisable structure, yet are typically modelled by monolithic neural architectures ...
Acceleration methods for diffusion models (e.g., token merging or downsampling) typically optimize synthesis quality under reduced compute, yet often ignore dis...
We present STAMP (Selective Task-Aware Mechanism for Text Privacy), a new framework for task-aware text privatization that achieves an improved privacy-utility ...
Neural network verification is often used as a core component within larger analysis procedures, which generate sequences of closely related verification querie...
Learning good representations is essential for latent planning with world models. While pretrained visual encoders produce strong semantic visual features, they...
This article, a lightly adapted version of Perplexity's response to NIST/CAISI Request for Information 2025-0035, details our observations and recommendations c...
Pretraining produces a learned parameter vector that is typically treated as a starting point for further iterative adaptation. In this work, we instead view th...
Despite interdisciplinary research leading to larger and longer-term impact, most work remains confined to single-domain academic silos. Recent AI-based approac...
Computing power that used to be available only in supercomputers decades ago especially their parallelism is currently available in standard personal computer C...
Salient object detection (SOD) in remote sensing images faces significant challenges due to large variations in object sizes, the computational cost of self-att...
This work pursues automated planning and scheduling of distributed data pipelines, or workflows. We develop a general workflow and resource graph representation...
State space models (SSMs) like Mamba have gained significant traction as efficient alternatives to Transformers, achieving linear complexity while maintaining c...
Long-context agentic workflows have emerged as a defining use case for large language models, making attention efficiency critical for both inference speed and ...
Introduction Deep learning systems are powerful because they learn representations of data automatically. Instead of engineers manually designing features, neu...
While decoder-only Large Language Models (LLMs) have recently dominated the NLP landscape, encoder-only architectures remain a cost-effective and parameter-effi...
The Overlooked Pitfall of AI-Driven ‘Value Alignment’ in Algorithmic Decision-Making As AI systems increasingly shape our lives, the importance of AI ethics ca...
Multimodal agents offer a promising path to automating complex document-intensive workflows. Yet, a critical question remains: do these agents demonstrate genui...
Synthetic data has become essential for training code generation models, yet it introduces significant noise and hallucinations that are difficult to detect wit...
Computers ordering cappuccinos. A couple of weeks ago, Google and Samsung announced a big Gemini development coming to their newest devices: task automation. St...
Ukraine’s battlefield data sharing initiative Ukraine’s four‑year war with Russia has made it the world leader in battlefield drone technologyhttps://www.polit...
The rapid advancement of large language models (LLMs) has accelerated progress toward universal AI assistants. However, existing benchmarks for personalized ass...
Translating complex reinforcement learning (RL) environments into high-performance implementations has traditionally required months of specialized engineering....
Any-to-Any models are an emerging class of multimodal models that accept combinations of multimodal data (e.g., text, image, video, audio) as input and generate...
Grammarly pulls AI author-impersonation tool after backlash Writing tool Grammarly has disabled an AI feature which mimicked personas of prominent writers, inc...
State-of-the-art cloud-native applications require intelligent schedulers that can effectively balance system stability, resource utilisation, and associated co...
Editor’s note: This post is part of Into the Omniversehttps://www.nvidia.com/en-us/omniverse/news/, a series focused on how developers, 3D practitioners, and en...
Distributed AI and IoT applications increasingly execute across heterogeneous resources spanning end devices, edge/fog infrastructure, and cloud platforms, ofte...
Deep Operator Networks (DeepONets) provide a branch-trunk neural architecture for approximating nonlinear operators acting between function spaces. In the class...
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Spiking Neural Networks (SNNs) have gained significant attention in edge computing due to their low power consumption and computational efficiency. However, exi...
GeForce NOW at GDC 2024 GeForce NOWhttps://www.nvidia.com/en-us/geforce-now/ is showcasing its service at this week’s Game Developers Conference GDC in San Fra...
Large-scale sparse multi-objective optimization problems (LSMOPs) are prevalent in real-world applications, where optimal solutions typically contain only a few...
As AI agents are increasingly used in high-stakes domains like healthcare and law enforcement, aligning their behaviour with social, legal, ethical, empathetic,...
“Ask Maps,” rolling out today to Google Maps on mobile, lets you ask Gemini questions about locations and even to plan trips on your behalf....
Overview Perplexity wants to be more than just an answer engine. On Wednesday, it launched Personal Computer, a new AI agent tool that can turn a spare Mac int...
Although the temporal spike dynamics of spiking neural networks (SNNs) enable low-power temporal pattern capture capabilities, they also incur inherent inconsis...
This work presents a quantum mechanical framework for analyzing quantization-based optimization algorithms. The sampling process of the quantization-based searc...
Researchers posing as teens got popular AI assistants to help them map out shootings and bombings By Rebecca Ruizhttps://mashable.com/author/rebecca-ruiz !Rebec...
When the production company Particle6 debuted its AI‑generated “actor” Tilly Norwoodhttps://techcrunch.com/2025/10/01/hollywood-is-not-taking-kindly-to-the-ai-g...
Resolving issues on code repositories is an important part of software engineering. Various recent systems automatically resolve issues using large language mod...
Curiosity‑driven research has long sparked technological transformations. A century ago, curiosity about atoms led to quantum mechanics and eventually the trans...
AI usage is rising rapidly, but the impact on engineering productivity is more modest than many hype‑driven narratives suggest. Social media and vendor marketin...
Overview In February 2026, a team at Google DeepMind published Intelligent AI Delegation—a framework for how autonomous agents should safely decompose tasks, t...
