A family of four foundation models designed to meet diverse enterprise needs across reasoning, multimodal processing, and real-time conversational AI.
How agentic systems work under the hood — and how AWS’s new AgentCore framework implements their essential components.
Reasoning models can generate seven to 10 times as many tokens as necessary on simple tasks, creating unsustainable costs at scale. Amazon's vision for metacognitive AI could fundamentally shift how models allocate computational resources.
Amazon's competitive-agent architecture creates a continuous improvement cycle that develops security protections at machine speed, reducing what typically takes weeks down to hours.
A new approach to reducing carbon emissions reveals previously hidden emission “hotspots” within value chains, helping organizations make more detailed and dynamic decisions about their future carbon footprints.
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December 1, 20258 min read“Network language models” will coordinate complex interactions among intelligent components, computational infrastructure, access points, data centers, and more. -
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November 20, 20254 min read
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Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
The collaboration will advance research in generative AI, robotics, natural language processing and cloud computing while fostering innovation in foundational and emerging technologies.
University teams battle to harden and hack AI coding assistants in head-to-head tournament
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Led by David Luan and Pieter Abbeel, the lab will focus on developing new foundational capabilities for enabling useful AI agents.
The company's new state-of-the-art foundation models deliver frontier intelligence and industry-leading price performance.
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NeurIPS 2025 Workshop on Efficient Reasoning2025We introduce PHLoRA2 (Post-hoc LoRA), a simple yet powerful method to extract low-rank adaptation adapters from full-rank fine-tuned models without requiring access to training data or gradients. By computing the low-rank decomposition of weight differences between a base model and its fine-tuned counterpart, our method reconstructs adapter modules that can be merged or dynamically routed at inference time
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NeurIPS 2025 Workshop on ResponsibleFM2025Given the constant flux in the world of geopolitics, staying up to date and compliant with international trade issues is challenging. But exploring if LLMs can aid this task is a frontier hither to unexplored in the LLM evaluation literature - primarily due to the lack of a dataset set for benchmarking the capabilities of LLMs on questions regarding international trade subjects. To address this gap, we
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Transactions of Machine Learning Research2025Despite fast progress, efficiently training large language models (LLMs) in extremely long contexts remains challenging. Existing methods fall back to training LLMs with short contexts (up to a few thousand tokens) and use inference time techniques when evaluating on very long contexts (above 1M tokens). Training on very long contexts is limited by GPU memory availability and the prohibitively long training
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2025Large Language Model (LLM)-powered agents have emerged as a new paradigm for complex, multi-turn human-AI interactions, yet most existing systems adopt a one-size-fits-all approach, neglecting the evolving preferences and goals of individual users. This limitation hinders their ability to maintain alignment and coherence over extended multi-turn interactions and dynamic tasks. To address this gap, we propose
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NeurIPS 2025 Workshop on Aligning Reinforcement Learning Experimentalists and Theorists2025In recommendation systems, diversity and novelty are essential for capturing varied user preferences and encouraging exploration, yet many systems prioritize click relevance. While reinforcement learning (RL) has been explored to improve diversity, it often depends on random exploration that may not align with user interests. We propose LAAC (LLM-guided Adversarial Actor Critic), a novel method that leverages
Collaborations
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
