How we reproduced three attacks that extract private training data from AI models and the cryptographic defenses that stop them.
Amazon engineers and scientists have created new tools to optimize delivery networks under uncertainty — and keep them adapting without missing a beat.
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.
As AI models grow larger, they become less insightful, not more. To ensure that they continue to learn, we need to reduce their inference time.
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May 26, 20265 min readHow to train language models to generate diverse, accurate reasoning paths using tokens that control distinct reasoning strategies. -
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May 14, 202616 min read
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Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features researchers tackling the hardest problems in agentic AI — from building reliable perception systems to designing training environments that mirror human learning.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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ICASSP 20262026Streaming automatic speech recognition (ASR) systems based on Large Language Models (LLMs) face a fundamental trade-off between accuracy and latency. Existing approaches typically employ fixed-size chunking to maintain low latency, which often compromises recognition accuracy. We propose SCALE, a streaming ASR framework that addresses this challenge through three key techniques: (a) dynamic chunk boundary
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AISTATS 20262026A/B tests in online experiments face statistical power challenges when testing multiple candidates simultaneously, while adaptive experimental designs (AED) alone fall short in inferring experiment statistics such as the average treatment effect, especially with many metrics (e.g., revenue, safety) and heterogeneous variances. This paper proposes a fixed-budget multi-metric AED framework with a two-phase
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2026Large Language Model (LLM)-based Multi-Agent Systems (MAS) enable complex problem-solving but introduce significant debugging challenges, characterized by long interaction traces, inter-agent dependencies, and delayed error manifestation. Existing diagnostic approaches often rely on expensive expert annotation or 'LLM-as-a-judge' paradigms, which struggle to pinpoint decisive error steps within extended
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2026In this paper, we investigate the problem of how to effectively master tool-use to solve complex visual reasoning tasks for Multimodal Large Language Models. To achieve that, we propose a novel Tool-supervised Reinforcement Learning (ToolsRL) framework, with direct tool supervision for more effective tool-use learning. We focus on a series of simple, native, and interpretable visual tools, including zoom-in
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CVPR 2026 Workshop on TRUE-V2026Vision Language Models (VLMs) are increasingly adopted for document understanding tasks, often replacing traditional OCR systems. However, VLMs exhibit a fundamental difference: they frequently correct or rewrite imperfect text rather than transcribe it literally, a behavior that remains largely underexplored. We present a systematic investigation through controlled experiments with intentionally perturbed
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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.
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
