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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December 29, 20259 min read
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December 5, 20256 min read
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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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2026Large Language Models (LLMs) have demonstrated exceptional capabilities but face two critical deployment challenges: high computational costs and scarcity of personalized domain training data. We address these dual challenges through a comprehensive framework that combines synthetic data generation with inference optimization techniques. Our approach employs LLMs for zero-shot and few-shot synthetic dataset
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2026Neural codec language models have revolutionized speech synthesis but face significant challenges when adapted to music generation, particularly in achieving precise timbre control while preserving melodic content. We introduce Neural Code Language Model for Controllable Timbre Transfer (NCLMCTT), a novel architecture that enables zero-shot instrument cloning through direct audio conditioning without explicit
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EurIPS 20252025Current large language model (LLM) evaluations primarily focus on single-answer tasks, whereas many real-world applications require identifying multiple correct answers. This capability remains under-explored due to the lack of dedicated evaluation frameworks. We introduce SATA-BENCH, a benchmark for evaluating LLMs on Select All That Apply (SATA) questions spanning six domains, including read-ing comprehension
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ACML 20252025Continuous time-event sequence (CTES) forecasting is essential across diverse domains, from healthcare to finance, requiring accurate prediction of both future event types and their timestamps. Traditionally, CTES forecasting has been driven by Temporal Point Processes (TPPs), which rely on intensity function-based priors. However, these methods often fail to generalize effectively to real-world scenarios
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NeurIPS 2025 Workshop on Uncovering Causality in Science2025Switchback experiments assign units to treatment and control over time, yielding more precise causal estimates than fixed designs but risking bias from carryover effects, where past treatments influence future outcomes. Existing estimators require specifying an influence period, i.e. an upper bound on carryover duration, often guessed from intuition. We propose a statistical test that detects when this
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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.
