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HPE declares Juniper deal a 'home run' as AI and networking fuel record quarter
HPE just posted its biggest earnings beat since 2018, and CEO Antonio Neri wasted little time taking a victory lap for the company's $14 billion Juniper acquisition. The infrastructure giant posted record quarterly revenue of $10.7 billion on Monday, up 40 percent year over year, while networking revenue surged to $2.7 billion and AI systems orders reached $1.8 billion. Investors liked what they saw, sending shares sharply higher after the results landed.
Marvell enters the AI network fray with 102.4 Tbps switch silicon
Marvell enjoyed a fillip from Nvidia chief Jensen Huang at Computex, who praised the firm as it unveiled the latest 102.4 Tbps switch silicon it has purpose-built for AI infrastructure. The fabless semiconductor biz announced upcoming availability of its Teralynx T100 chip to coincide with the Taiwanese trade show, claiming that it needs 25 percent lower power than competitive solutions with lower latency for AI training and inference workloads. But the firm is late to this party, as other...
Event Detection for Parameter-to-KPI Dependency Learning for AI-RAN
arXiv:2606.06459v1 Announce Type: new Abstract: Next-generation wireless networks are expected to rely on multiple concurrent AI-driven control functions that optimize different network objectives simultaneously, particularly in AI-integrated and open radio access network architectures such as AI Radio Access Network (AI-RAN) and Open Radio Access Network (O-RAN). When these functions interact, they can interfere with one another in ways that are difficult to detect from raw network data...
Autonomous Incident Resolution at Hyperscale: An Agentic AI Architecture for Network Operations
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Comparing Sentiment Contagion in AI-Agent and Human Social Networks: Evidence from MOLTBOOK
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Palo Alto Networks headquarters in Santa Clara, California.
ARIADNE: AI-RAN Informed Link Adaptation in Digital Twin Network Environments
Announce Type: replace Abstract: Artificial Intelligence (AI)-powered Radio Access Network (RAN) networks have attracted significant attention from both industry and academia. Meanwhile, Digital Twins offer a safe playground for experimenting with AI/Machine Learning (ML)-based solutions for advanced AI-RAN research.
Toward Trustworthy Digital Twins in AI Agent-based Wireless Network Optimization: Challenges, Solutions, and Opportunities
arXiv:2511.19961v2 Announce Type: replace Abstract: Optimizing modern wireless networks is exceptionally challenging due to their high dynamism and complexity. While the AI agent powered by reinforcement learning (RL) offers a promising solution, its practical application is limited by prohibitive exploration costs and potential risks in the real world. The emerging digital twin (DT) technology provides a safe and controlled virtual environment for agent training, but its effectiveness...