Using ensembles of agents to generate and refine interactions annotated with chains of thought improves performance on a battery of benchmarks by an average of 29%.
Language models inspired by specialized processing regions in the brain offer significant time and cost savings.
An ensemble of models, weighted according to their reluctance to flag anomalies, outperforms its predecessors.
Unique end-of-arm tools with three-dimensional force sensors and innovative control algorithms enable robotic arms to “pick” items from and “stow” items in fabric storage pods.
We present Amazon Nova Premier, our most capable multimodal foundation model and teacher for model distillation.
Customer-obsessed science
Research areas
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July 29, 2025New cost-to-serve-software metric that accounts for the full software development lifecycle helps determine which software development innovations provide quantifiable value.
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Created for students keen to build their career in machine learning, the fifth edition of the program is now open for all eligible students from recognized institutes in India.
University teams battle to harden and hack AI coding assistants in head-to-head tournament
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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.
The company's new state-of-the-art foundation models deliver frontier intelligence and industry-leading price performance.
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Enterprise 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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Speculative decoding has emerged as a promising approach to accelerating large language model (LLM) generation using a fast drafter while maintaining alignment with the target model’s distribution. However, existing approaches face a tradeoff: external drafters offer flexibility but can suffer from slower drafting, while self-speculation methods use drafters tailored to the target model but require re-training
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As large language models increasingly gain popularity in real-world applications, processing extremely long contexts, often exceeding the model’s pre-trained context limits, has emerged as a critical challenge. While existing approaches to efficient long-context processing show promise, recurrent compression-based methods struggle with information preservation, whereas random access approaches require substantial
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2025Diffusion Policies are effective at learning closed-loop manipulation policies from human demonstrations but generalize poorly to novel arrangements of objects in 3D space, hurting real-world performance. To address this issue, we propose Spherical Diffusion Policy (SDP), an SE(3) equivariant diffusion policy that adapts trajectories according to 3D transformations of the scene. Such equivariance is achieved
Academia
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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.
