Using the Neuron Kernel Interface, a Reactor–AWS collaboration tackled the dynamic shapes, memory access patterns, and cache management that make real-time autoregressive diffusion hard—building techniques that generalize across models.
Three new papers from Amazon Bio Discovery address bottlenecks in AI-driven antibody engineering, from benchmarking binding predictors to experimentally validating de novo design.
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.
Customer-obsessed science
Research areas
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October 1, 202610 min readAugmenting a network graph with agentic AI produces a “digital twin” that can help isolate network failures. -
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August 21, 20269 min read
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July 30, 20268 min read
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July 29, 20266 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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EACL 2026 Industry Track2026Personalized shopping agents must adapt their decisions to different user personas, balancing efficiency, preference alignment, and goal success. Building upon the WebShop dataset and τ2-Bench environment, ShopperBench introduces a persona-guided benchmark for evaluating such adaptive behaviors. ShopperBench augments shopping trajectories with persona-conditioned goals, reasoning rationales, and preference
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2026We introduce SWAN (Semantic Watermarking with Abstract Meaning Representation)1 , a novel framework that embeds watermark signatures into the semantic structure of a sentence using Abstract Meaning Representation (AMR). In contrast to existing watermarking methods, which typically encode signatures by adjusting token selection preferences during text generation, SWAN embeds the signature directly in the
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2026Individual treatment effect (ITE) estimation from observational data becomes unreliable when three challenges co-occur: extreme class imbalance (0.4% treatment rate), outcome sparsity (97.6% zeros), and pervasive cold-start (99.2% incomplete profiles). These conditions violate identifying assumptions—propensity scores collapse toward boundary values, and outcome predictions degrade for subjects with sparse
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2026Structured texts refer to texts containing structured elements beyond plain texts, such as code snippets and placeholders. Such structured texts increasingly require segmentation into semantically meaningful components, which cannot be effectively handled by conventional sentence-level segmentation methods. To address this, we propose BoundRL, a novel approach that jointly performs efficient token-level
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ICML 2026 Workshop on Scalable Learning and Optimization for Efficient Multimodal AI Agents (SCALE)2026Enterprise environments differ fundamentally from the clean settings assumed in LLM research: knowledge is distributed across heterogeneous sources, often incomplete or inconsistent, and key procedural logic is implicitly encoded in artifacts rather than explicitly documented. In such settings, retrieval-based approaches are insufficient, as no single source contains the full workflow. We propose a replication-driven
Collaborations
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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.
