Deploying Multi-Turn RL Infrastructure for Amazon Nova on Amazon SageMaker HyperPod
In this post, you deploy a two-phase infrastructure for multi-turn RL using Amazon Nova Forge on Amazon SageMaker HyperPod. By the end, you have an event-driven…
Cloud & Big Tech 399 stories
Automatically redact PII in images with Amazon Nova
In this post, we present a multi-step pipeline directed by Amazon Nova, which uses its contextual vision reasoning to coordinate complementary tools, including Meta’s open-source Segment…
Streaming benchmark and recommendation results to MLflow with Amazon SageMaker AI
In this post, you learn how to use the new MLflow integration with Amazon SageMaker AI optimized inference recommendation jobs and Amazon SageMaker AI benchmark jobs…
OpenAI and Databricks at DAIS 2026: Making enterprise AI real
Data + AI Summit 2026 brought together over 32k+ in-person attendees across hundreds of sessions, product announcements...
Fortress: A Case Study in Stabilizing Search Recommendations via Temporal Data Augmentation and Feature Pruning
In search and recommendation systems, predictive models often suffer from temporal instability when certain input features introduce volatility in output scores. This instability can degrade model…
Understanding Annotator Safety Policy with Interpretability
Safety policies define what constitutes safe and unsafe AI outputs, guiding data annotation and model development. However, annotation disagreement is pervasive and can stem from multiple…
TopoPrimer: The Missing Topological Context in Forecasting Models
We introduce TopoPrimer, a framework that makes the global topological structure of the series population an explicit input to any forecasting model. TopoPrimer improves accuracy across…
Path-Constrained Mixture-of-Experts
Sparse Mixture-of-Experts (MoE) architectures route each token through a subset of experts at each layer independently. We propose viewing MoE computation through the lens of expert…
Revisiting ASR Error Correction with Specialized Models
Language models play a central role in automatic speech recognition (ASR), yet most methods rely on text-only models unaware of ASR error patterns. Recently, large language…
Scaling Properties of Continuous Diffusion Spoken Language Models
Speech-only spoken language models (SLMs) lag behind text and text-speech models in performance, with recent discrete autoregressive (AR) SLMs indicating significant computational and data demands to…
Segmental Attention Decoding with Long Form Acoustic Encodings
We address the fundamental incompatibility of attention-based encoder-decoder (AED) models with long-form acoustic encodings. AED models trained on segmented utterances learn to encode absolute frame positions…
How Amazon Bedrock catches AI-generated phishing
Social engineering through phishing remains one of the most common tactics for launching cyberattacks. AI-generated phishing email messages now pose a new challenge for security teams…
Best practices for multi-turn reinforcement learning in Amazon SageMaker AI
In this post, we share best practices for reliable multi-turn RL training. We cover how to build a training environment you can trust, set up an…
The 3 questions to answer to take AI from experimentation to impact
Companies are starting to see the potential of AI in their businesses. Today, 60%...
Inside the infrastructure strategies propelling AI leaders
AI adoption is starting to translate into real-world returns. But as efforts accelerate,...
Meet Brain: The AI system behind Azure reliability
Learn how Microsoft is building a digital twin of Azure Service Health and why it changes how hyperscale operates. The post Meet Brain: The AI system…
Anti-Causal Domain Generalization: Leveraging Unlabeled Data
The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments. Existing methods typically require…
Residual Context Diffusion Language Models
Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to purely autoregressive language models because they can decode multiple tokens in parallel. However, state-of-the-art…
Learning Structured Reasoning via Tractable Trajectory Control
Large language models can exhibit emergent reasoning behaviors, often manifested as recurring lexical patterns (e.g., “wait,” indicating verification). However, complex reasoning trajectories remain sparse in unconstrained…
Conformal Thinking: Risk Control for Reasoning on a Compute Budget
Reasoning Large Language Models (LLMs) enable test-time scaling, with dataset-level accuracy improving as the token budget increases, motivating adaptive reasoning—spending tokens when they improve reliability and…
MemoryLLM: Plug-n-Play Interpretable Feed-Forward Memory for Transformers
Understanding how transformer components operate in LLMs is important, as it is at the core of recent technological advances in artificial intelligence. In this work, we…
Learning Unmasking Policies for Diffusion Language Models
Diffusion (Large) Language Models (dLLMs) now match the downstream performance of their autoregressive counterparts on many tasks, while holding the promise of being more efficient during…
On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs
Reinforcement learning (RL) finetuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivating its extension to vision language models (VLMs).…
Amortizing Maximum Inner Product Search with Learned Support Functions
Maximum inner product search (MIPS) is a crucial subroutine in machine learning, requiring the identification of a vector taken within a database (the keys) that best…