A single, optimized LLM unifies what previously required multiple models and can serve as a reasoning partner for medical chemists.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Adding a pet to your flight sounds like a one-click task, but every click passes through layers of software dating back to the 1960s. Amazon's AGI Lab trains AI agents not to replace these brittle systems but to learn them deeply enough to finally make them work.
Low-rank adaptation, data augmentation, and chain-of-thought reasoning are among the techniques enabling accent-free polyglot outputs, improved expressiveness, and reliable synthesis.
What machine learning theorists learned using AI agents to generate proofs — and what comes next.
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April 17, 20266 min readIsabelle/HOL's balance of expressiveness, automation, and scalability enabled the world's first formally verified cloud hypervisor. -
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Now open across seven research areas, including Agentic AI and Robotics. Applicants receive unrestricted funds, AWS promotional credits, and training resources. Submission deadline is May 6
Hosted by 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.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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2025For music streaming services expanding into audiobooks, cold-start personalization presents a critical challenge: as audiobooks are a newly introduced content type, the vast majority of existing users have no audiobook listening history. This domain-level cold-start scenario differs from traditional item or user cold-start scenarios, since personalization must begin before any behavioral data exists in
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FAIM 20252025Conveyors play a crucial role in transporting packages and containers in manufacturing and production facilities. While computer vision has emerged as a promising technology for real-time monitoring of transportation systems, its application in conveyor operations remains in the early stages. This paper introduces an Industrial Internet of Things (IIoT) framework for real-time conveyor monitoring. We first
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AISTATS 2025, NeurIPS 2025 Workshop on Efficient Reasoning2025Speculative decoding is an effective technique for accelerating large language model (LLM) inference by drafting multiple tokens in parallel. However, its practical speedup is often limited by a rigid verification step, which strictly enforces that the accepted token distribution exactly matches that of the target model. This constraint leads to the rejection of many plausible tokens, reducing the acceptance
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2025Since the seminal work of TabPFN, research on tabular foundation models (TFMs) based on in-context learning (ICL) has challenged long-standing paradigms in machine learning. Without seeing any real-world data, models pretrained on purely synthetic datasets generalize remarkably well across diverse datasets, often using only a moderate number of in-context examples. This shifts the focus in tabular machine
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NeurIPS 2025 Workshop on Multimodal Algorithmic Reasoning2025Large Language Models (LLMs) perform well on short-horizon tasks but struggle with long-horizon, multimodal scenarios that require multi-step reasoning, perception, and adaptive planning. We identify two key challenges in these settings: the difficulty of long-term coordination between planning and execution within single-agent architectures and the inefficiency of indiscriminate visual grounding. To address
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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
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
