By learning the idiosyncrasies of accumulated layers of legacy systems, AI agents can preserve institutional knowledge and provide a unified interface to a range of services.
As AI agents become more autonomous, the key challenge isn't what they can do; it's how to design the human side of the equation.
What machine learning theorists learned using AI agents to generate proofs — and what comes next.
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.
Customer-obsessed science
Research areas
-
March 20, 202615 min readSimplifying and clarifying the assembly code for core operations enabled automated optimization and verification. -
March 19, 202611 min read -
February 17, 20263 min read
-
-
January 13, 20267 min read
Featured news
Now open across seven research areas, including Agentic AI and Robotics. Applicants receive unrestricted funds, AWS promotional credits, and training resources. Submission deadline is May 6
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.
-
2025Behavioral therapy notes are important for both legal compliance and patient care. Unlike progress notes in physical health, quality standards for behavioral therapy notes remain underdeveloped. To address this gap, we collaborated with licensed therapists to design a comprehensive rubric for evaluating therapy notes across key dimensions: completeness, conciseness, and faithfulness. Further, we extend
-
2025We present a novel large-scale dataset for defect detection in a logistics setting. Recent work on industrial anomaly detection has primarily focused on manufacturing scenarios with highly controlled poses and a limited number of object categories. Existing benchmarks like MVTec-AD [6] and VisA [33] have reached saturation, with state-of-the-art methods achieving up to 99.9% AUROC scores. In contrast to
-
ACL 2025 Workshop on Research on Agent Language Models2025Developing language model-based dialogue agents requires effective data to train models that can follow specific task logic. However, most existing data simulation methods focus on increasing diversity in language, topics, or dialogue acts at the utterance level, largely neglecting a critical aspect of task logic diversity at the dialogue level. This paper proposes a novel data simulation method designed
-
KDD 2025 Workshop on LLM4ECommerce2025Evaluating long-form AI-generated content remains challenging due to the lack of standardized methodologies that robustly align with human judgment across formats such as articles, blogs, and essays. We introduce HALF-Eval, a scalable framework that combines structured, checklist-based evaluation with machine learning aggregation to assess key quality dimensions, including creativity, impact, coherence
-
KDD 2025 Workshop on Evaluation and Trustworthiness of Agentic and Generative AI Models2025Humor is a complex yet essential aspect of human communication. It can be defined as a communicative expression establishing surprising, incongruent relationships or meanings to amuse. This paper presents empirical evidence demonstrating the successful application of computational methods to humor recognition in AI generated textual data, specifically jokes. Through experiments on synthetic and open-source
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
View all
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.
