As AI agents become more autonomous, the key challenge isn't what they can do; it's how to design the human side of the equation.
What machine learning theorists learned using AI agents to generate proofs — and what comes next.
As AI models grow larger, they become less insightful, not more. To ensure that they continue to learn, we need to reduce their inference time.
How do you match AI warnings about future events with what actually happens? A KDD test-of-time award recognizes a 2014 approach that modern systems still need.
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February 2, 202610 min readEvery NFL game generates millions of tracking data points from 22 RFID-equipped players. Seventy-five machine learning models running on AWS process that data in under a second, transforming football into a sport where every movement is measured, modeled, and instantly analyzed. -
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January 8, 20264 min read
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December 29, 20256 min read
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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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2026Agentic vision–language models are increasingly trained to 'think with images' by calling image operations. However, we show that high final-answer accuracy often hides unfaithful visual reasoning: models may invoke tools on irrelevant regions or ignore tool outputs entirely, yet still guess the correct answer. In this work, we first propose a faithfulness evaluation protocol that measures whether intermediate
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2026Generating long, coherent text remains a challenge for large language models (LLMs), as they lack hierarchical planning and structured organization in discourse generation. We introduce Structural Alignment, a novel method that aligns LLMs with human-like discourse structures to enhance long-form text generation. By integrating linguistically grounded discourse frameworks into reinforcement learning, our
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EACL 2026 Industry Track2026Conversational agents have become ubiquitous across application domains, such as, shopping assistants, medical diagnosis, autonomous task planning etc. Users interacting with these agents often fail to understand how to start a conversation or what to ask next to obtain the desired information. To enable seamless and hassle-free user-agent interactions, we introduce Next Question Suggestions (NQS), which
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ICASSP 20262026Recent advances in generative retrieval allow large language models (LLMs) to recommend items by generating their identifiers token by token. This requires each item to be represented by a compact, semantically meaningful sequence of tokens that an LLM can understand. We introduce a method to generate multimodal music token (3MToken) that transforms rich metadata from a music database—including audio, credits
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2026Reinforcement learning with verifiable rewards has significantly advanced reasoning with large language models (LLMs) in domains such as mathematics and logic. However, verifiable signals provide only coarse-grained or binary correctness feedback. This limitation results in inefficiencies like overly verbose or repetitive reasoning. Existing length-based solutions (e.g., length penalty) compromise accuracy
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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.
