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 ...

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 ...

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

AI agents are a risky business. Even when stuck inside the chatbox window, LLMs will make mistakes and behave badly. Once ...

This desktop app for hosting and running LLMs locally is rough in a few spots, but still useful right out of the box.

Print Join the Discussion View in the ACM Digital Library The mathematical reasoning performed by LLMs is fundamentally different from the rule-based symbolic methods in traditional formal reasoning.

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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.

According to the 2025 Stack Overflow Developer Survey, the single greatest frustration for developers is dealing with AI solutions that look correct but are slightly wrong. Nearly half of developers ...

Overview AI-generated code moves fast, but it lives in production for a long time, which makes strong monitoring essential ...