From reimagining storage to serverless computing, Aurora continues to push the boundaries of what's possible in database technology.
Amazon VP and distinguished scientist Byron Cook explains how AWS's new Automated Reasoning checks address key challenges in automated reasoning: translating natural to structured language, defining truth, and definitive reasoning.
Trained on millions of hours of data from Amazon fulfillment centers and sortation centers, Amazon’s new DeepFleet models predict future traffic patterns for fleets of mobile robots.
A new philosophy for developing LLM architectures reduces energy requirements, speeds up runtime, and preserves pretrained-model performance.
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August 26, 2025With a novel parallel-computing architecture, a CAD-to-USD pipeline, and the use of OpenUSD as ground truth, a new simulator can explore hundreds of sensor configurations in the time it takes to test just a few physical setups.
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University teams battle to harden and hack AI coding assistants in head-to-head tournament
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Led by David Luan and Pieter Abbeel, the lab will focus on developing new foundational capabilities for enabling useful AI agents.
The company's new state-of-the-art foundation models deliver frontier intelligence and industry-leading price performance.
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Finite-time convergence and sample complexity of actor-critic multi-objective reinforcement learning2024Reinforcement learning with multiple, potentially conflicting objectives is pervasive in real-world applications, while this problem remains theoretically under-explored. This paper tackles the multi-objective reinforcement learning (MORL) problem and introduces an innovative actor-critic algorithm named MOAC which finds a policy by iteratively making trade-offs among conflicting reward signals. Notably
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2024Existing Large Language Models (LLMs) usually remain static after deployment, which might make it hard to inject new knowledge into the model. We aim to build models containing a considerable portion of self-updatable parameters, enabling the model to integrate new knowledge effectively and efficiently. To this end, we introduce MemoryLLM, a model that comprises a transformer and a fixed-size memory pool
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2024This paper proposes the use of “multicalibration” to yield interpretable and reliable confidence scores for outputs generated by large language models (LLMs). Multicalibration asks for calibration not just marginally, but simultaneously across various intersecting groupings of the data. We show how to form groupings for prompt/completion pairs that are correlated with the probability of correctness via
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2024Adapting large language models (LLMs) to unseen tasks with in-context training samples without fine-tuning remains an important research problem. To learn a robust LLM that adapts well to unseen tasks, multiple meta-training approaches have been proposed such as MetaICL and MetaICT, which involve meta-training pre-trained LLMs on a wide variety of diverse tasks. These meta-training approaches essentially
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Tokenization matters: Navigating data-scarce tokenization for gender inclusive language technologies2024Gender-inclusive NLP research has documented the harmful limitations of gender binary-centric large language models (LLM), such as the inability to correctly use gender-diverse English neopronouns (e.g., xe, zir, fae). While data scarcity is a known culprit, the precise mechanisms through which scarcity affects this behavior remain under-explored. We discover LLM misgendering is significantly influenced
Academia
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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.
