Efficiently serve dozens of fine-tuned models with vLLM on Amazon SageMaker AI and Amazon Bedrock
AWS demonstrates how to efficiently serve multiple fine-tuned models using vLLM on Amazon SageMaker and Amazon Bedrock. The implementation includes kernel-level optimizations for Mixture of Experts models.
Why this matters: This enables enterprises to deploy and manage multiple specialized AI models cost-effectively while maintaining performance.
Building intelligent event agents using Amazon Bedrock AgentCore and Amazon Bedrock Knowledge Bases
AWS shows how to build intelligent event agents using Amazon Bedrock AgentCore and Knowledge Bases. The system maintains attendee preferences and provides personalized experiences.
Why this matters: This provides a template for organizations to create scalable, personalized AI assistants without extensive custom infrastructure development.
Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets
Researchers present an automated framework for translating AI benchmarks and datasets while preserving quality. The method addresses semantic drift and context loss in existing translations.
Why this matters: Accurate multilingual benchmarks are essential for properly evaluating AI models across different languages and regions.
SumTablets: A Transliteration Dataset of Sumerian Tablets
Researchers released SumTablets, a dataset pairing 91,606 Sumerian cuneiform tablet glyphs with their transliterations. This addresses a gap that previously hindered NLP applications to Sumerian texts.
Why this matters: Enables computational analysis of ancient Sumerian, potentially accelerating historical and linguistic research.
Train CodeFu-7B with veRL and Ray on Amazon SageMaker Training jobs
AWS published a technical guide for training the CodeFu-7B model using specific reinforcement learning methods. The process utilizes distributed computing on SageMaker.
Why this matters: It provides a replicable framework for organizations to train specialized, large-scale AI models efficiently.
Generate structured output from LLMs with Dottxt Outlines in AWS
An AWS blog post details a method for generating structured outputs from large language models. The approach uses the Dottxt Outlines framework within SageMaker.
Why this matters: This enables more reliable integration of LLMs into applications that require consistent data formats.
Scaling data annotation using vision-language models to power physical AI systems
Bedrock Robotics uses vision-language models to analyze construction footage and generate labeled datasets for autonomous equipment training. This collaboration with AWS aims to scale data annotation.
Why this matters: Automates labor-intensive data preparation for physical AI systems in industrial settings.
A Very Big Video Reasoning Suite
Researchers introduced a large-scale dataset and benchmark for evaluating video reasoning in AI models. The suite aims to systematically study capabilities like understanding continuity and causality in videos.
Why this matters: Provides tools to measure and improve AI's ability to reason about dynamic visual scenes.
Accelerating AI model production at Hexagon with Amazon SageMaker HyperPod
Hexagon scaled AI model production by pretraining segmentation models using Amazon SageMaker HyperPod infrastructure. This collaboration with AWS accelerated their model development pipeline.
Why this matters: Reduces infrastructure management overhead for enterprise AI model training.
Why we no longer evaluate SWE-bench Verified
OpenAI discontinued evaluation of SWE-bench Verified due to contamination issues and flawed measurements of coding progress.
Why this matters: Shows the importance of reliable benchmarks for accurately assessing AI coding capabilities.
Amazon SageMaker AI in 2025, a year in review part 1: Flexible Training Plans and improvements to price performance for inference workloads
Amazon SageMaker AI introduced Flexible Training Plans and improved price performance for inference workloads in 2025. These were part of broader infrastructure enhancements.
Why this matters: These improvements help organizations manage AI training costs and optimize deployment efficiency.
Amazon SageMaker AI in 2025, a year in review part 2: Improved observability and enhanced features for SageMaker AI model customization and hosting
Amazon SageMaker AI enhanced observability, model customization, and hosting capabilities in 2025. These updates followed earlier infrastructure improvements.
Why this matters: Better observability and customization tools enable more sophisticated AI deployment and monitoring.
GGML and llama.cpp join HF to ensure the long-term progress of Local AI
GGML and llama.cpp have partnered with Hugging Face to promote the development of Local AI technologies.
Why this matters: This collaboration aims to enhance the accessibility and effectiveness of AI solutions in local environments.
Sink-Aware Pruning for Diffusion Language Models
Researchers proposed sink-aware pruning for diffusion language models, showing attention sinks are less stable than in autoregressive models.
Why this matters: Could reduce computational costs for diffusion models without sacrificing quality, making them more practical to deploy.
CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts
The CLEF HIPE-2026 evaluation lab focuses on extracting person-place relationships from multilingual historical texts. It assesses systems on accuracy, efficiency, and generalization.
Why this matters: This research enables more accurate construction of historical knowledge graphs for digital humanities.
MARS: Margin-Aware Reward-Modeling with Self-Refinement
MARS is a new method that improves AI reward models by focusing data augmentation on the most ambiguous training examples. It provides theoretical and empirical improvements over uniform augmentation.
Why this matters: This makes AI alignment training more data-efficient and robust, reducing reliance on costly human feedback.
Amazon Quick now supports key pair authentication to Snowflake data source
Amazon Quick Sight now supports key pair authentication for connecting to Snowflake data sources.
Why this matters: Enhances security for business intelligence tools accessing sensitive data in cloud data warehouses.
Gemini 3.1 Pro: A smarter model for your most complex tasks
Google DeepMind released Gemini 3.1 Pro, an AI model designed for complex tasks requiring more than simple answers.
Why this matters: Enables more sophisticated AI applications that can handle nuanced, multi-step problems.
Advancing independent research on AI alignment
OpenAI is committing $7.5 million to The Alignment Project to fund independent AI alignment research. The funding supports work on AGI safety and security.
Why this matters: This investment could accelerate research into making advanced AI systems safer and more reliable.
Introducing OpenAI for India
OpenAI is expanding AI access in India through local infrastructure development and enterprise support. The initiative aims to advance workforce skills across the country.
Why this matters: This could accelerate AI adoption in one of the world's largest markets and create localized AI solutions.