Stochastic KV Routing: Enabling Adaptive Depth-Wise Cache Sharing

Serving transformer language models with high throughput requires caching Key-Values (KVs) to avoid redundant computation during autoregressive generation. The memory footprint of KV caching is significant and heavily impacts serving costs. This work proposes to lessen these memory requirements. While recent work has largely addressed KV cache reduction via compression and eviction along the temporal …

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How Hapag-Lloyd uses Amazon Bedrock to transform customer feedback into actionable insights

Hapag-Lloyd stands as one of the world’s leading liner shipping companies, operating a modern fleet of 313 container ships with a total transport capacity of 2.5 million TEU (Twenty-foot Equivalent Unit—a standard unit of measurement for cargo capacity in container shipping). The company maintains a container capacity of 3.7 million TEU, which includes one of …

Five must-have guides to move agents into production with Gemini Enterprise Agent Platform

Building AI agents that work well in a demo is one thing, but running them in production requires serious infrastructure.  At Google Cloud Next ’26, we introduced Gemini Enterprise Agent Platform to help developers build, deploy, scale, govern, and optimize  autonomous AI agents. From managing long-running state and enforcing security with the Agent Governance Stack, …

AI lets chemists design molecules by simply describing them

Creating complex molecules usually requires years of experience and countless decisions, but a new AI system is changing that. Synthegy lets chemists guide synthesis and reaction planning using simple language, while powerful algorithms generate and evaluate possible solutions. The AI doesn’t just compute—it reasons, scoring pathways and explaining which ones make the most sense.

A simple physics-inspired model sheds light on how AI learns

Artificial intelligence systems based on neural networks—such as ChatGPT, Claude, DeepSeek or Gemini—are extraordinarily powerful, yet their internal workings remain largely a “black box.” To better understand how these systems produce their responses, a group of physicists at Harvard University has developed a simplified mathematical model of learning in neural networks that can be analyzed …