A new hybrid optimization approach allows edge devices to fine-tune vision-language models using only forward passes, achieving up to 7% higher accuracy than existing techniques.
"Reinforcement learning gyms" train agents on the many low-level tasks that they must chain together to execute customer requests.
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.
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January 13, 20267 min readLeveraging existing environment simulators and reward functions based on verifiable ground truth boosts task success rate, even with small models and small training datasets. -
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Hosted by Dr. 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.
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.
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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 LLM4ECommerce2025Building and maintaining a rich and high-quality product schema helps customers of an e-commerce service find products based on the characteristics they desire. As the quantity of products sold on the service increases, so does the complexity of maintaining the schema. Expanding it requires finding gaps, designing new product attributes, and ensuring that they do not already exist in the schema. In this
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KDD 2025 Workshop on LLM4ECommerce2025Customer service in e-commerce often relies on human agents to handle inquiries related to orders, returns, and product information. While this approach is effective, it can be expensive and difficult to scale during periods of high demand. Recent advances in intelligent chatbots, particularly those based on Retrieval Augmented Generation (RAG) models, have significantly improved customer service efficiency
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KDD 2025 Workshop on LLM4ECommerce, EMNLP 20252025Accurate intent classification is critical for efficient routing in customer service, ensuring customers are connected with the most suitable agents while reducing handling times and operational costs. However, as companies expand their product lines, intent classification faces scalability challenges due to the increasing number of intents and variations in taxonomy across different verticals. In this
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KDD 2025 Workshop on Structured Knowledge for Large Language Models2025Despite advances in large language model (LLM)-based natural language interfaces for databases, scaling to enterprise-level data catalogs remains an under-explored challenge. Prior works addressing this challenge rely on domain-specific fine-tuning—complicating deployment—and fail to leverage important semantic context contained within database metadata. To address these limitations, we introduce a component-based
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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.
