AGI SVP Rohit Prasad on how to mix your data with Amazon's training data at every stage — deep customization without catastrophic forgetting.
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.
How agentic systems work under the hood — and how AWS’s new AgentCore framework implements their essential components.
Competitive-agent architecture develops security protections at machine speed, reducing weeks of work to hours.
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December 10, 20255 min readNew audio-processing technology is making entertainment more accessible for millions of viewers. -
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November 20, 20254 min read
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Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Meet the 63 Amazon Research Award (ARA) recipients, who represent 41 universities in 8 countries.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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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.
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NeurIPS 2025 Workshop on AI for Music2025Recent advances in generative retrieval allow large language models (LLMs) to recommend items by generating their identifiers token by token, rather than using nearest-neighbor search over embeddings. This approach requires each item, such as a music track, to be represented by a compact and semantically meaningful token sequence that LLMs can generate. We propose a multimodal music tokenizer (3MToken)
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NeurIPS 2025 Workshop on Multi-Turn Interactions in Large Language Models2025Agentic tool use has gained traction with the rise of agentic tool calling, yet most existing work overlooks the complexity of multi-turn tool interactions. We introduce OrchDAG, a synthetic data generation pipeline that models tool execution as directed acyclic graphs (DAGs) with controllable complexity. Using this dataset, we benchmark model performance and propose a graph-based reward to enhance RLVR
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2025Fine-tuning vision language models (VLMs) has achieved remarkable performance across various downstream tasks, yet, it requires access to model gradients through backpropagation (BP), making them unsuitable for memory-constrained, inference-only edge devices. To address this limitation, previous work has explored various BP-free fine-tuning methods. However, these approaches often rely on high-variance
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2025Text-to-SQL systems translate natural language (NL) questions into SQL queries, enabling non-technical users to interact with structured data. While large language models (LLMs) have shown promising results on the text-to-SQL task, they often produce semantically incorrect yet syntactically valid queries, with limited insight into their reliability. We propose SQLENS, an end-to-end framework for fine-grained
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2025As machine learning (ML) systems are increasingly deployed in high-stakes domains, the need for robust methods to assess fairness has become more critical. While statistical fairness metrics are widely used due to their simplicity, they are limited in their ability to explain why disparities occur, as they rely on associative relationships in the data. In contrast, causal fairness metrics aim to uncover
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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.
