New research indicates that AI agents learn compressible models of data, which don’t have enough space to enable memorization.
How the Verus "program verifier", which automatically checks code against a mathematical specification of its functionality, helps increase security assurance in software projects.
Ten years after we founded the Automated Reasoning Group, mathematical logic has moved from academic research into production services that secure millions of customer workloads — demonstrating that systems can be provably correct, not just probably correct.
PatientAgentBench generates a synthetic patient health record, a realistic clinical vignette, and a patient agent that converses with the AI system under evaluation, to capture what a patient-facing agent actually has to do.
A new chiplet architecture, custom die-to-die connectivity, and support for DDR5-8800 memory and the latest PCIe gen6 interconnects improve performance by 25% for general-purpose and agentic AI workloads.
Customer-obsessed science
Research areas
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September 21, 202611 min readThree new papers from Amazon Bio Discovery address bottlenecks in AI-driven antibody engineering, from benchmarking binding predictors to experimentally validating de novo design. -
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August 21, 20269 min read
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July 30, 20268 min read
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The Build on Trainium program is a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI.
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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COLM 2026 Workshop on Lifelong Agents2026Multi-agent architectures today are hand-designed, generated one-shot, or evolved through unconstrained code search—none of which treats the number of agents as a searchable parameter, and none of which can discover that a single agent is optimal. We argue agent count and coordination topology should be optimized with the same discipline applied to neural architectures: bounded edits, held-out validation
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COLM 2026 Workshop on Efficient Reasoning2026The recent advancements in Vision Language Models (VLMs) have demonstrated progress toward true intelligence requiring robust reasoning capabilities. Beyond pattern recognition, linguistic reasoning must integrate with visual comprehension, particularly for Chart Question Answering (CQA) tasks involving complex data visualizations. Current VLMs face significant limitations in CQA, including imprecise numerical
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COLM 2026 Workshop on Agent Behavior2026Coordination interventions for multi-agent LLM systems are regime-dependent: identical guidance yields opposite effects at different decoding temperatures, improving accuracy in the deterministic regime but degrading it in the stochastic regime. The reversal traces to over-regularization: exploration-promoting interventions add explicit entropy to policies already receiving implicit entropy from stochastic
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2026Bee provides a personal AI assistant that uses frontier AI models over user-controlled real-world context. Privacy is a core system requirement: the architecture is designed to prevent Bee and other Amazon operators, non-attested internal services, and supporting infrastructure from accessing plaintext user data outside the user’s cryptographically authorized processing path. The Bee Private Compute architecture
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EMNLP 20262026Production tool calling for agentic systems must satisfy four operational requirements: lower per-invocation cost, low latency, adaptability to evolving tool catalogs, and the ability to catch errors before execution. The ReAct paradigm keeps the large language model (LLM) in the loop, but at production scale, reprocessing intermediate results inflates cost and latency and degrades answer quality. Programmatic
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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.
