Customer-obsessed science
Research areas
-
July 10, 20265 min readHydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world. -
July 9, 202610 min read -
-
Featured news
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.
-
ECML-PKDD 20262026When fusing heterogeneous modalities for classification, a central challenge is cardinality heterogeneity: modalities often produce token sequences of vastly different lengths, yet standard symmetric fusion wastes attention capacity under this asymmetry. We present CRAFT, a modality-agnostic fusion framework that selects a high-density attention backbone using token cardinality and standalone task relevance
-
arXiv2026Large language model (LLM) agents deployed in healthcare and life sciences (HCLS) routinely receive queries that are semantically ambiguous—the same terms carry different meanings across clinical, regulatory, pharmacovigilance, data-standards, and research domains. Existing approaches address ambiguity post-hoc through output filtering or retrieval augmentation, but do not quantify it before the model responds
-
2026Continual learning methods for vision-language models are developed on benchmarks where each new task introduces entirely new domain knowledge. Real-world task sequences are more natural: they routinely share visual concepts, language patterns, and even training samples across stages. However, existing mixture-of-expert methods that assign one expert per task with fixed routing can split similar inputs
-
2026Time reasoning is a make-or-break capability for Large Language Models (LLMs) aspiring to act as reliable personal and enterprise assistants. This paper introduces the Temporal Reasoning Dataset (TRD), a programmatically generated multilingual benchmark designed to evaluate temporal reasoning operational capabilities in LLMs across ten languages, with particular focus on basic operations relevant to conversational
-
SIGMOD/PODS 20262026We present Aurora Limitless Database, a cloud-native distributed database system that extends Amazon Aurora PostgreSQL with horizontal scaling capabilities while maintaining strong consistency guarantees. The system provides transparent scalability using a router layer for query distribution and a storage layer of PostgreSQL shards, which eliminates the need for application-level sharding. Our key technical
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.
