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Policy gradient algorithms have driven many recent advancements in language model reasoning. An appealing property is their ability to learn from exploration on their own trajectories, a process crucial for fostering diverse and creative solutions. As we show in this paper, many policy gradient algorithms naturally reduce the entropy—and thus the diversity of explored trajectories—as …
This post is cowritten with David Kim, and Premjit Singh from Ring. Scaling self-service support globally presents challenges beyond translation. In this post, we show you how Ring, Amazon’s home security subsidiary, built a production-ready, multi-locale Retrieval-Augmented Generation (RAG)-based support chatbot using Amazon Bedrock Knowledge Bases. By eliminating per-Region infrastructure deployments, Ring reduced the cost …
Read more “How Ring scales global customer support with Amazon Bedrock Knowledge Bases”
Existing feed-forward 3D Gaussian Splatting methods predict pixel-aligned primitives, leading to a quadratic growth in primitive count as resolution increases. This fundamentally limits their scalability, making high-resolution synthesis such as 4K intractable. We introduce LGTM (Less Gaussians, Texture More), a feed-forward framework that overcomes this resolution scaling barrier. By predicting compact Gaussian primitives coupled with …
Read more “Less Gaussians, Texture More: 4K Feed-Forward Textured Splatting”
State Space Models (SSMs) have become the leading alternative to Transformers for sequence modeling. Their primary advantage is efficiency in long-context and long-form generation, enabled by fixed-size memory and linear scaling of computational complexity. We begin this work by showing a simple theoretical result stating that SSMs cannot accurately solve any “truly long-form” generation problem …
Read more “To Infinity and Beyond: Tool-Use Unlocks Length Generalization in State Space Models”
As developers build AI agents with more sophisticated reasoning systems, they require higher-quality fuel–in the form of enterprise data and specialized tools–to drive real business value. To get the most out of that octane-rich mix, we offer Google-managed model context protocol (MCP) servers: an engine purpose-built for AI agents to interact securely with Google and …
Read more “How to build production-ready AI agents with Google-managed MCP servers”
Despite their output being ultimately consumed by human viewers, 3D Gaussian Splatting (3DGS) methods often rely on ad-hoc combinations of pixel-level losses, resulting in blurry renderings. To address this, we systematically explore perceptual optimization strategies for 3DGS by searching over a diverse set of distortion losses. We conduct the first-of-its-kind large-scale human subjective study on …
Read more “Drop-In Perceptual Optimization for 3D Gaussian Splatting”
How we built lightweight, real-time map collaboration for teams operating at the edge. About This Series Frontend engineering at Palantir goes far beyond building standard web apps. Our engineers design interfaces for mission-critical decision-making, build operational applications that translate insight to action, and create systems that handle massive datasets — thinking not just about what the user needs, but …
Read more “Frontend Engineering at Palantir: Redefining Real-Time Map Collaboration”
Kia ora! Customers in New Zealand have been asking for access to foundation models (FMs) on Amazon Bedrock from their local AWS Region. Today, we’re excited to announce that Amazon Bedrock is now available in the Asia Pacific (New Zealand) Region (ap-southeast-6). Customers in New Zealand can now access Anthropic Claude models (Claude Opus 4.5, …
Read more “Run Generative AI inference with Amazon Bedrock in Asia Pacific (New Zealand)”
AI has made it easier than ever for student developers to work efficiently, tackle harder problems, and pursue ambitious projects. But for students earning technical degrees, these new capabilities also create genuine tensions around learning. How much should I use AI? What should I use it for? As 90% of technology professionals now use AI …
Read more “The new AI literacy: Insights from student developers”
We introduce exclusive self attention (XSA), a simple modification of self attention (SA) that improves Transformer’s sequence modeling performance. The key idea is to constrain attention to capture only information orthogonal to the token’s own value vector (thus excluding information of self position), encouraging better context modeling. Evaluated on the standard language modeling task, XSA …