Low-rank adaptation, data augmentation, and chain-of-thought reasoning are among the techniques enabling accent-free polyglot outputs, improved expressiveness, and reliable synthesis.
By learning the idiosyncrasies of accumulated layers of legacy systems, AI agents can preserve institutional knowledge and provide a unified interface to a range of services.
As AI agents become more autonomous, the key challenge isn't what they can do; it's how to design the human side of the equation.
What machine learning theorists learned using AI agents to generate proofs — and what comes next.
Customer-obsessed science
Research areas
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March 20, 202615 min readSimplifying and clarifying the assembly code for core operations enabled automated optimization and verification. -
March 19, 202611 min read -
February 25, 202611 min read
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February 17, 20263 min read
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Featured news
Now open across seven research areas, including Agentic AI and Robotics. Applicants receive unrestricted funds, AWS promotional credits, and training resources. Submission deadline is May 6
Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features conversations with leading AI researchers about the breakthroughs needed to achieve general intelligence.
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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2025The increasing complexity and fragmentation of financial systems in large organizations have created significant challenges for financial teams, particularly in performing real-time, end-to-end validation, as existing validation methods relying on static rules or batch processing are often inadequate for today's dynamic financial environments. This paper introduces a novel approach using Large Language
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KDD 2025 Workshop on LLM4ECommerce2025We present an autonomous framework that leverages Large Language Models (LLMs) to automate end-to-end business analysis and market report generation. At its core, the system employs specialized agents - Researcher, Reviewer, Writer, and Retriever - that collaborate to analyze data and produce comprehensive reports. These agents learn from real professional consultants' presentation materials at Amazon through
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IROS 20252025We present and tackle the problem of Embodied Question Answering (EQA) with Situational Queries (S-EQA) in a household environment. Unlike prior EQA work tackling simple queries that directly reference target objects and properties ('What is the color of the car?'), situational queries (such as 'Is the house ready for sleeptime?') are challenging as they require the agent to correctly identify multiple
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2025Text style transfer in enterprise environments presents unique challenges that extend beyond traditional style transfer approaches, particularly when dealing with complex technical documentation and strict organizational guidelines. This paper introduces Onoma, a novel enterprise-scale style transfer system that addresses the fundamental challenges of maintaining consistent brand voice while preserving
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IEEE 2025 Workshop on Machine Learning for Signal Processing (MLSP)2025In this paper we investigate cross-lingual Text-To-Speech (TTS) synthesis through the lens of adapters, in the context of lightweight TTS systems. In particular, we compare the tasks of unseen speaker and language adaptation with the goal of synthesising a target voice in a target language, in which the target voice has no recordings therein. Results from objective evaluations demonstrate the effectiveness
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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
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
