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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CIKM 20252025Relevance in e-commerce product search is critical to ensuring that results accurately reflect customer intent. While large language models (LLMs) have recently advanced natural language processing capabilities, their high inference latency and significant infrastructure demands make them less suitable for real-time e-commerce applications. Consequently, transformer-based encoder models are widely adopted
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NeurIPS 2025 Workshop on Evaluating the Evolving LLM Lifecycle2025Rigorous evaluation of Large Language Models (LLMs) is critical for their adoption in high-stakes applications, particularly in highly technical domains that require deep expertise and specialized training. The proliferation of LLMs from vari2025ous providers further underscores the need for comprehensive model performance benchmarking. Like many standardized tests and certification exams, several prominent
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NeurIPS 2025 Workshop on Efficient Reasoning2025We introduce PHLoRA2 (Post-hoc LoRA), a simple yet powerful method to extract low-rank adaptation adapters from full-rank fine-tuned models without requiring access to training data or gradients. By computing the low-rank decomposition of weight differences between a base model and its fine-tuned counterpart, our method reconstructs adapter modules that can be merged or dynamically routed at inference time
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NeurIPS 2025 Workshop on ResponsibleFM2025Given the constant flux in the world of geopolitics, staying up to date and compliant with international trade issues is challenging. But exploring if LLMs can aid this task is a frontier hither to unexplored in the LLM evaluation literature - primarily due to the lack of a dataset set for benchmarking the capabilities of LLMs on questions regarding international trade subjects. To address this gap, we
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Transactions of Machine Learning Research2025Despite fast progress, efficiently training large language models (LLMs) in extremely long contexts remains challenging. Existing methods fall back to training LLMs with short contexts (up to a few thousand tokens) and use inference time techniques when evaluating on very long contexts (above 1M tokens). Training on very long contexts is limited by GPU memory availability and the prohibitively long training
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Gretchen Ertl
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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
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