Implementing Statistical Guardrails for Non-Deterministic Agents
Non-deterministic agents are those where the same input can lead to distinct outputs across multiple runs.
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, …
‘I Actually Thought He Was Going to Hit Me,’ OpenAI’s Greg Brockman Says of Elon Musk
OpenAI’s president wrapped his testimony on Tuesday by revealing a fiery meeting with Musk and subsequent efforts to remove several board members.
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 …
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A new open weights image model appears in ArtificialAnalysis. Outperforming Flux.2 Pro and Z Image Turbo.
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Agentic RAG Explained in 3 Levels of Difficulty
Traditional
PORTool: Importance-Aware Policy Optimization with Rewarded Tree for Multi-Tool-Integrated Reasoning
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’ …