As AI deployments scale and start to include packs of agents autonomously working in concert, organizations face a naturally amplified attack surface.

Large language models (LLMs) can suggest hypotheses, write code and draft papers, and AI agents are automating parts of the research process. Although this can accelerate science, it also makes it ...

The convergence of cloud computing and generative AI marks a defining turning point for enterprise security. Global spending ...

Nvidia researchers developed dynamic memory sparsification (DMS), a technique that compresses the KV cache in large language models by up to 8x while maintaining reasoning accuracy — and it can be ...

In the SDLC, there should be no shortcuts. Developers must view AI as a collaborator to be monitored, rather than an autonomous entity to be unleashed.

AI agents are powerful, but without a strong control plane and hard guardrails, they’re just one bad decision away from chaos.

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Anthropic’s Claude Opus 4.6 identified 500+ unknown high-severity flaws in open-source projects, advancing AI-driven vulnerability detection.

Discover Claude Opus 4.6 from Anthropic. We analyze the new agentic capabilities, the 1M token context window, and how it outperforms GPT-5.2 while addressing critical trade-offs in cost and latency.

Connected and autonomous vehicles have struggled to move beyond pilot projects as high infrastructure costs and coordination barriers slow real-world deployment. New research published in the journal ...

On the Humanity’s Last Exam (HLE) benchmark, Kimi K2.5 scored 50.2% (with tools), surpassing OpenAI’s GPT-5.2 (xhigh) and ...

Ragas async llm_factory uses max_tokens instead of max_completion_tokens model arg for open ai gpt 5.2. Im using ragas to evalute answers of our chatbot based on answer faithfulness and relevancy.