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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Research areas
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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. -
December 29, 20256 min read -
December 29, 20259 min read
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December 8, 20258 min read
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December 5, 20256 min read
Featured news
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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NeurIPS 2025 Workshop on Machine Learning and the Physical Sciences2025Long-horizon motion forecasting for multiple autonomous robots is challenging due to non-linear agent interactions, compounding prediction errors, and continuous-time evolution of dynamics. Learnt dynamics of such a system can be useful in various applications such as travel time prediction, prediction-guided planning and surrogate simulation. In this work, we aim to develop an efficient trajectory forecasting
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2025We present CEDA, a novel multimodal framework for detecting hallucinations in large language model outputs through a multi-agent debate approach. While existing methods for black-box LLMs often rely on response sampling and self-consistency checking, our framework leverages a three-fold approach: a multi-agent debate setting to critically examine and debate the authenticity of generated content, a lightweight
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NeurIPS 2025 Workshop on Evaluating the Evolving LLM Lifecycle2025Building infrastructure-as-code (IaC) in cloud computing is a critical task, underpinning the reliability, scalability, and security of modern software systems. Despite the remarkable progress of large language models (LLMs) in software engineering – demonstrated across many dedicated benchmarks – their capabilities in developing IaC remain underexplored. Unlike existing IaC benchmarks that predominantly
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2025Understanding causal relationships among the variables of a system is paramount to explain and control its behavior. For many real-world systems, however, the true causal graph is not readily available and one must resort to predictions made by algorithms or domain experts. Therefore, metrics that quantitatively assess the goodness of a causal graph provide helpful checks before using it in downstream tasks
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NeurIPS 2025 Workshop on New Perspectives in Graph Machine Learning2025Graph Neural Networks (GNNs) have proven to be highly effective for link and edge prediction across domains ranging from social networks to drug discovery. However, processing extremely large graphs with millions of densely connected nodes poses significant challenges in terms of computational efficiency, learning speed, and memory management. Thus making Graph Foundational Model very computationally expensive
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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.
