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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ICSE 20262026Large Language Models (LLMs) are increasingly integrated into software systems as automated decision-making components. These systems rely on instruction prompts written in natural language to encode complex workflows. However, debugging these prompts when LLMs produce undesired outputs remains challenging due to their black-box nature and the impracticality of manually inspecting large, complex inputs.
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CSER 20262026Construction management systems require realistic test data capturing complex stakeholder interactions and temporal dependencies, yet accessing real project data remains challenging due to privacy constraints and proprietary information protection. This research addresses a critical systems engineering challenge by introducing agentic simulacra patterns that leverage multi-agent coordination to generate
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2026Multi-Agent Debate (MAD) frameworks improve factual reliability in large language models (LLMs) by allowing agents to critique and refine one another's reasoning. Yet, existing MAD systems are computationally expensive and prone to degradation under prolonged debates due to redundant exchanges and unstable judging. We propose a lightweight, industry-deployable alternative that unifies Selective Debate Initiation
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ICLR 2026 Workshop on Lifelong Agents2026For large language models deployed through black-box APIs, recurring inference costs often dominate one-time training costs, motivating composed agentic systems that amortize expensive reasoning into reusable intermediate representations. We study a broad class of such systems, termed Guide–Core Policies (GCOP), in which a guide model generates a structured strategy that is executed by a black-box core
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2026Video Large Language Models (VideoLLMs) excel at video understanding tasks where outputs are textual, such as Video Question Answering and Video Captioning. However, they underperform specialized embedding-based models in Retrieval tasks, such as Text-to-Video Retrieval and Moment Retrieval. We introduce ViLL-E (Video-LLM-Embed), a unified VideoLLM architecture endowed with a novel embedding generation
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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.
