In 2017, IEEE Internet Computing identified a single paper from its publication history that had best withstood the test of time: a 2003 paper called “Amazon.com Recommendations: Item-to-Item Collaborative Filtering”
F4D Studios
How Amazon’s scientists developed a first-of-its-kind multi-echelon system for inventory buying and placement.
Top eight university teams move on to head-to-head finals focused on AI security for code generation.
We present Amazon Nova Premier, our most capable multimodal foundation model and teacher for model distillation.
A new multimodal foundation model that unifies speech and text processing in a single architecture, delivering frontier voice intelligence and industry-leading price performance.
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Research areas
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June 25, 2025With large datasets, directly generating data ID codes from query embeddings is much more efficient than performing pairwise comparisons between queries and candidate responses.
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Top eight university teams move on to head-to-head finals focused on AI security for code generation.
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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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2025Recent advancements in 3D Large Multi-modal Models (3D-LMMs) have driven significant progress in 3D question answering. However, recent multi-frame VisionLanguage Models (VLMs) demonstrate superior performance compared to 3D-LMMs on 3D question answering tasks, largely due to the greater scale and diversity of available 2D image data in contrast to the more limited 3D data. Multi-frame VLMs, although achieving
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ICMLT 20252025Applications of reinforcement learning (RL) in real-world scenarios are often limited by its generalizability across multiple different environments. Contextual RL offers a principled solution to this issue by capturing environmental heterogeneity through observable contextual variables. However, directly applying Contextual RL may not achieve optimal results when contexts exhibit high randomness and variance
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International Journal of Research in Marketing2025In 2020, Amazon launched the Climate Pledge Friendly (CPF) program to make it easy for customers to discover and shop for products with sustainability certifications. In this paper, we measure the causal impact of products qualifying for CPF on consumer purchase behavior. Using a dataset of about 45,000 products spanning three categories, and a Differencein-Differences identification strategy, we show that
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QECC-Synth: A layout synthesizer for quantum error correction codes on sparse hardware architecturesASPLOS 20252025Quantum Error Correction (QEC) codes are essential for achieving fault-tolerant quantum computing (FTQC). However, their implementation faces significant challenges due to disparity between required dense qubit connectivity and sparse hardware architectures. Current approaches often either underutilize QEC circuit features or focus on manual designs tailored to specific codes and architectures, limiting
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2025Hybrid models that combine the language modeling capabilities of Attention layers with the efficiency of Recurrent layers (e.g., State Space Models) have gained traction in practically supporting long contexts in Large Language Model serving. Yet, the unique properties of these models complicate the usage of complementary efficiency optimizations such as prefix caching that skip redundant computations across
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.
