Using ensembles of agents to generate and refine interactions annotated with chains of thought improves performance on a battery of benchmarks by an average of 29%.
Language models inspired by specialized processing regions in the brain offer significant time and cost savings.
An ensemble of models, weighted according to their reluctance to flag anomalies, outperforms its predecessors.
Unique end-of-arm tools with three-dimensional force sensors and innovative control algorithms enable robotic arms to “pick” items from and “stow” items in fabric storage pods.
We present Amazon Nova Premier, our most capable multimodal foundation model and teacher for model distillation.
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Research areas
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August 4, 2025Translating from natural to structured language, defining truth, and definitive reasoning remain topics of central concern in automated reasoning, but Amazon Web Services’ new Automated Reasoning checks help address all of them.
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Created for students keen to build their career in machine learning, the fifth edition of the program is now open for all eligible students from recognized institutes in India.
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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2024As the scale of data and models for video understanding rapidly expand, handling long-form video input in transformer-based models presents a practical challenge. Rather than resorting to input sampling or token dropping, which may result in information loss, token merging shows promising results when used in collaboration with transformers. However, the application of token merging for long-form video
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2024 Conference on Digital Experimentation @ MIT (CODE@MIT)2024Online sites typically evaluate the impact of new product features on customer behavior using online controlled experiments (or A/B tests). For many business applications, it is important to detect heterogeneity in these experiments [1], as new features often have a differential impact by customer segment, product group, and other variables. Understanding heterogeneity can provide key insights into causal
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2024Code generation models are not robust to small perturbations, which often lead to incorrect generations and significantly degrade the performance of these models. Although improving the robustness of code generation models is crucial to enhancing user experience in real-world applications, existing research efforts do not address this issue. To fill this gap, we propose CodeFort, a framework to improve
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In-context learning (ICL) is a powerful paradigm where large language models (LLMs) benefit from task demonstrations added to the prompt. Yet, selecting optimal demonstrations is not trivial, especially for complex or multi-modal tasks where input and output distributions differ. We hypothesize that forming taskspecific representations of the input is key. In this paper, we propose a method to align representations
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2024Hierarchical Text Classification (HTC) is a sub-class of multi-label classification. It is challenging because the hierarchy typically has a large number of diverse topics. Existing methods for HTC fall within two categories, local methods (a classifier for each level, node, or parent) or global methods (a single classifier for everything). Local methods are computationally expensive, whereas global methods
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.
