Amazon scientists and policy experts discuss how the company’s responsible-AI pipeline embeds safety and values throughout the AI development lifecycle.
Agentic mechanism enables Amazon and vendors to optimize supply chain management without disclosing private information.
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.
Isabelle/HOL's balance of expressiveness, automation, and scalability enabled the world's first formally verified cloud hypervisor.
Customer-obsessed science
Research areas
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April 27, 20264 min readA new framework provides a statistical method for estimating the likelihood of catastrophic failures in large language models in adversarial conversations. -
April 15, 20268 min read -
April 7, 202613 min read
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April 1, 20265 min read
Featured news
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.
Now open across seven research areas, including Agentic AI and Robotics. Applicants receive unrestricted funds, AWS promotional credits, and training resources. Submission deadline is now May 13.
Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features conversations with leading AI researchers about the breakthroughs needed to achieve general intelligence.
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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CLeaR 20262026In this paper we show how to exploit interventional data to acquire the joint conditional distribution of all the variables using the Maximum Entropy principle. To this end, we extend the Causal Maximum Entropy method to make use of data arising from identifiable interventional distributions in addition to data from the observational distribution. Using Lagrange duality, we prove that the solution to the
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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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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.
