New audio-processing technology is making entertainment more accessible for millions of viewers.
Reasoning models can generate seven to 10 times as many tokens as necessary on simple tasks, creating unsustainable costs at scale. Amazon's vision for metacognitive AI could fundamentally shift how models allocate computational resources.
AGI SVP Rohit Prasad on how to mix your data with Amazon's training data at every stage — deep customization without catastrophic forgetting.
How agentic systems work under the hood — and how AWS’s new AgentCore framework implements their essential components.
Competitive-agent architecture develops security protections at machine speed, reducing weeks of work to hours.
Customer-obsessed science
Research areas
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December 5, 20256 min readA multiagent architecture separates data perception, tool knowledge, execution history, and code generation, enabling ML automation that works with messy, real-world inputs. -
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November 20, 20254 min read
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Featured news
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Meet the 63 Amazon Research Award (ARA) recipients, who represent 41 universities in 8 countries.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
David Chang/Getty Images/iStockphoto
Led by David Luan and Pieter Abbeel, the lab will focus on developing new foundational capabilities for enabling useful AI agents.
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ACM CCS 20252025Motivated by applications to efficient secure computation, we consider the following problem of encrypted matrix-vector product (EMVP). Let F be a finite field. In an offline phase, a client uploads an encryption of a matrix M ∈ F^(m×ℓ) to a server, keeping only a short secret key. The server stores the encrypted matrix M̂. In the online phase, the client may repeatedly send encryptions q̂_i of query vectors
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2025Quantifying uncertainty in black-box LLMs is vital for reliable responses and scalable oversight. Existing methods, which gauge a model's uncertainty through evaluating self-consistency in responses to the target query, can be misleading: an LLM may confidently provide an incorrect answer to a target query, yet give a confident and accurate answer to that same target query when answering a knowledge-preserving
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EMNLP 2025 Findings2025Large language models (LLMs) often fail to scale their performance on long-context tasks performance in line with the context lengths they support. This gap is commonly attributed to retrieval failures—the models' inability to identify relevant information in the long inputs. Accordingly, recent efforts often focus on evaluating and improving LLMs' retrieval performance: if retrieval is perfect, a model
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KDD 2025 Workshop on AI for Supply Chain2025Despite significant advancements in time series forecasting, accurate modeling of time series with strong heterogeneity in magnitude and/or sparsity patterns remains challenging for state of the art deep learning architectures. We identify several factors that lead existing models to systematically under-perform on low magnitude and sparse time series, including loss functions with implicit biases toward
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SIGDIAL 20252025Large Language Models (LLMs) are increasingly employed in multi-turn conversational tasks, yet their pre-training data predominantly consists of continuous prose, creating a potential mismatch between required capabilities and training paradigms. We introduce a novel approach to address this discrepancy by synthesizing conversational data from existing text corpora. We present a pipeline that transforms
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.
