Stochastic KV Routing: Enabling Adaptive Depth-Wise Cache Sharing

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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

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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 …

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

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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, …

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AI lets chemists design molecules by simply describing them

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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

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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 …

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PORTool: Importance-Aware Policy Optimization with Rewarded Tree for Multi-Tool-Integrated Reasoning

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Multi-tool-integrated reasoning enables LLM-empowered tool-use agents to solve complex tasks by interleaving natural-language reasoning with calls to external tools. However, training such agents using outcome-only rewards suffers from credit-assignment ambiguity, obscuring which intermediate steps (or tool-use decisions) lead to success or failure. In this paper, we propose PORTool, an importance-aware policy-optimization algorithm that reinforces agents’ …

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Democratizing Machine Learning at Netflix: Building the Model Lifecycle Graph

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Saish Sali, Nipun Kumar, Sura Elamurugu Introduction As Netflix has grown, machine learning continues to support our ability to deliver value to members and drive excellence across multiple areas of our business. When Netflix began investing in machine learning over a decade ago, it was primarily focused on a single domain: personalization. Scala was the …

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