Microsoft has fixed a “remote code execution” vulnerability in Windows 11 Notepad that allowed attackers to execute local or remote programs by tricking users into clicking specially crafted Markdown links, without displaying any Windows security warnings. […] Read More
Apple has released security updates to fix a zero-day vulnerability that was exploited in an “extremely sophisticated attack” targeting specific individuals. […] Read More
NVIDIA Nemotron 3 Nano 30B MoE model is now available in Amazon SageMaker JumpStartArtificial Intelligence Today we’re excited to announce that the NVIDIA Nemotron 3 Nano 30B model with 3B active parameters is now generally available in the Amazon SageMaker JumpStart model catalog. You can accelerate innovation and deliver tangible business value with Nemotron 3 Nano on Amazon Web Services (AWS) without having to manage model deployment complexities. You can power your generative AI applications with Nemotron capabilities using the managed deployment capabilities offered by SageMaker JumpStart.
Today we’re excited to announce that the NVIDIA Nemotron 3 Nano 30B model with 3B active parameters is now generally available in the Amazon SageMaker JumpStart model catalog. You can accelerate innovation and deliver tangible business value with Nemotron 3 Nano on Amazon Web Services (AWS) without having to manage model deployment complexities. You can power your generative AI applications with Nemotron capabilities using the managed deployment capabilities offered by SageMaker JumpStart. Read More
Introduction AI coding tools aren’t new. Autocomplete suggestions and inline code generation have been part of the developer toolkit for a few years now. But the latest generation of tools is doing something different. They’re not just finishing your sentences. They’re reading your entire codebase, planning changes across dozens of files, and executing tasks autonomously. […]
Indian defense sector and government-aligned organizations have been targeted by multiple campaigns that are designed to compromise Windows and Linux environments with remote access trojans capable of stealing sensitive data and ensuring continued access to infected machines. The campaigns are characterized by the use of malware families like Geta RAT, Ares RAT, and DeskRAT, which […]
MACD: Model-Aware Contrastive Decoding via Counterfactual Datacs.AI updates on arXiv.org arXiv:2602.01740v2 Announce Type: replace
Abstract: Video language models (Video-LLMs) are prone to hallucinations, often generating plausible but ungrounded content when visual evidence is weak, ambiguous, or biased. Existing decoding methods, such as contrastive decoding (CD), rely on random perturbations to construct contrastive data for mitigating hallucination patterns. However, such a way is hard to control the visual cues that drive hallucination or well align with model weaknesses. We propose Model-aware Counterfactual Data based Contrastive Decoding (MACD), a new inference strategy that combines model-guided counterfactual construction with decoding. Our approach uses the Video-LLM’s own feedback to identify object regions most responsible for hallucination, generating targeted counterfactual inputs at the object level rather than arbitrary frame or temporal modifications. These model-aware counterfactual data is then integrated into CD to enforce evidence-grounded token selection during decoding. Experiments on EventHallusion, MVBench, Perception-test and Video-MME show that MACD consistently reduces hallucination while maintaining or improving task accuracy across diverse Video-LLMs, including Qwen and InternVL families. The method is especially effective in challenging scenarios involving small, occluded, or co-occurring objects. Our code and data will be publicly released.
arXiv:2602.01740v2 Announce Type: replace
Abstract: Video language models (Video-LLMs) are prone to hallucinations, often generating plausible but ungrounded content when visual evidence is weak, ambiguous, or biased. Existing decoding methods, such as contrastive decoding (CD), rely on random perturbations to construct contrastive data for mitigating hallucination patterns. However, such a way is hard to control the visual cues that drive hallucination or well align with model weaknesses. We propose Model-aware Counterfactual Data based Contrastive Decoding (MACD), a new inference strategy that combines model-guided counterfactual construction with decoding. Our approach uses the Video-LLM’s own feedback to identify object regions most responsible for hallucination, generating targeted counterfactual inputs at the object level rather than arbitrary frame or temporal modifications. These model-aware counterfactual data is then integrated into CD to enforce evidence-grounded token selection during decoding. Experiments on EventHallusion, MVBench, Perception-test and Video-MME show that MACD consistently reduces hallucination while maintaining or improving task accuracy across diverse Video-LLMs, including Qwen and InternVL families. The method is especially effective in challenging scenarios involving small, occluded, or co-occurring objects. Our code and data will be publicly released. Read More
Red Hat unifies AI and tactical edge deployment for UK MODAI News The UK Ministry of Defence (MOD) has selected Red Hat to architect a unified AI and hybrid cloud backbone across its entire estate. Announced today, the agreement is designed to break down data silos and accelerate the deployment of AI models from the data centre to the tactical edge. For CIOs, it’s part of a
The post Red Hat unifies AI and tactical edge deployment for UK MOD appeared first on AI News.
The UK Ministry of Defence (MOD) has selected Red Hat to architect a unified AI and hybrid cloud backbone across its entire estate. Announced today, the agreement is designed to break down data silos and accelerate the deployment of AI models from the data centre to the tactical edge. For CIOs, it’s part of a
The post Red Hat unifies AI and tactical edge deployment for UK MOD appeared first on AI News. Read More
How insurance leaders use agentic AI to cut operational costsAI News Agentic AI offers insurance leaders a path to scalable efficiency as the sector confronts a tough digital transformation. Insurers hold deep data reserves and employ a workforce skilled in analytic decision-making. Despite these advantages, the industry has largely failed to advance beyond pilot programmes. Research suggests only seven percent of insurers have scaled these initiatives
The post How insurance leaders use agentic AI to cut operational costs appeared first on AI News.
Agentic AI offers insurance leaders a path to scalable efficiency as the sector confronts a tough digital transformation. Insurers hold deep data reserves and employ a workforce skilled in analytic decision-making. Despite these advantages, the industry has largely failed to advance beyond pilot programmes. Research suggests only seven percent of insurers have scaled these initiatives
The post How insurance leaders use agentic AI to cut operational costs appeared first on AI News. Read More
Barclays bets on AI to cut costs and boost returnsAI News Barclays recorded a 12 % jump in annual profit for 2025, reporting £9.1 billion in earnings before tax, up from £8.1 billion a year earlier. The bank also raised its performance targets out through 2028, aiming for a return on tangible equity (RoTE) of more than 14 %, up from a previous goal of above
The post Barclays bets on AI to cut costs and boost returns appeared first on AI News.
Barclays recorded a 12 % jump in annual profit for 2025, reporting £9.1 billion in earnings before tax, up from £8.1 billion a year earlier. The bank also raised its performance targets out through 2028, aiming for a return on tangible equity (RoTE) of more than 14 %, up from a previous goal of above
The post Barclays bets on AI to cut costs and boost returns appeared first on AI News. Read More
Harness engineering: leveraging Codex in an agent-first worldOpenAI News By Ryan Lopopolo, Member of the Technical Staff
By Ryan Lopopolo, Member of the Technical Staff Read More
