Venture capitalists are deploying tranched, multi-step funding rounds to secure stakes in elite AI startups, pushing seed valuations past $4 billion for...
Nvidia introduced the DSX POD, a factory-built, modular data center approach designed to accelerate AI infrastructure deployment. Nvidia also announced its OpenShell Reference Architecture, combining Vera Rubin compute, Vera CPUs, BlueField-4 data processing units (DPUs) and security software to build, monitor and constrain AI agents.
Mass “Black October” panic is stoking fears of a looming internet lockdown, as attackers race to exploit a critical flaw in much of the web and banks scramble to rein in AI agents inside customer accounts.Some major U.S. banks are finding unvetted third-party AI agents inside customer accounts, incl...
Reinforcement learning (RL) is an effective way to strengthen the long-context reasoning of large language models (LLMs), but it needs training data that are long, diverse, challenging, and verifiable. Human annotation of such data is expensive, naturally occurring data are too scarce to sustain RL, and existing synthesis methods depend on external corpora or stronger teacher models, which become harder to obtain as the trained models get stronger. Recently, RL from zero data, in which a model learns from tasks it proposes itself, has achieved excellent performance, but existing methods target short-context tasks. Therefore, we propose $\textbf{LongZero}$, a method that lets an LLM improve its own long-context reasoning without any external data. Instead of writing questions and answers over existing documents, LongZero first has the model synthesize the question and answer with its supporting evidence in a compact structured form, and only then separately expands the evidence into a long multi-document context. LongZero combines these self-written multi-hop reasoning tasks with self-written multi-round retrieval tasks and trains the model with GRPO. We evaluate LongZero on two…
DeepSeek-V4.1-Flash-1017 is a minor update to DeepSeek-V4.1-Flash, delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks. Alongside these gains, the model reduces task execution time and output length, enabling users to complete…