AGI SVP Rohit Prasad on how to mix your data with Amazon's training data at every stage — deep customization without catastrophic forgetting.
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.
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.
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December 10, 20255 min readNew audio-processing technology is making entertainment more accessible for millions of viewers. -
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November 20, 20254 min read
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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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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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ICML 2025 Workshop on Efficient Systems for Foundation Models, ARR 20252025Recent large language models have shown promising capabilities in long-form reasoning, following structured chains of thought before arriving at a final answer. However, we observe that these reasoning paths tend to include substantial redundancy; analyzing attention patterns reveals that attention scores are widely scattered, particularly incorrect answers exhibit greater attention sparsity. In this paper
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KDD 2025 Workshop on LLM4ECommerce2025Designing intelligent assistants for e-commerce sellers presents significant challenges, primarily due to the abstract nature of seller queries and the complexity of orchestrating multiple internal tools. In-context planning (ICP) has emerged as a promising adaptive problem-solving approach for this setting. However, selecting effective exemplars for ICP remains a difficult problem, largely because of the
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KDD 2025 Workshop on Prompt Optimization2025Prompt engineering represents a critical bottleneck to harness the full potential of Large Language Models (LLMs) for solving complex tasks, as it requires specialized expertise, significant trial-and-error, and manual intervention. This challenge is particularly pronounced for tasks involving subjective quality assessment, where defining explicit optimization objectives becomes fundamentally problematic
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ICML 2025 Workshop on Multi-Agent Systems, KDD 2025 Workshop on Machine Learning in Finance (MLF)2025Enterprise accounting data is complex, ambiguous, and shaped by evolving systems and regulations. The institutional knowledge needed to reason over the data is sparse, scattered and rarely structurally documented—posing major challenges for LLM agents. We introduce a multi-agent financial research framework that mimics a junior analyst’s onboarding and growth. The Analyst Agent learns proactively from repeated
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2025In this paper, we investigate the problem of quantifying fairness in Retrieval-Augmented Generation (RAG) systems, particularly for complex cognitive tasks that go beyond factual question-answering. While RAG systems have demonstrated effectiveness in information extraction tasks, their fairness implications for cognitively complex tasks - including ideation, content creation, and analytical reasoning —
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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.
