Low-rank adaptation, data augmentation, and chain-of-thought reasoning are among the techniques enabling accent-free polyglot outputs, improved expressiveness, and reliable synthesis.
By learning the idiosyncrasies of accumulated layers of legacy systems, AI agents can preserve institutional knowledge and provide a unified interface to a range of services.
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.
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April 7, 202613 min readHow automated reasoning reconciles the demands of security, performance, and maintainability. -
March 20, 202615 min read -
March 19, 202611 min read
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February 25, 202611 min read
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February 17, 20263 min read
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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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ARR 20252025Language models have demonstrated remarkable capabilities in reasoning tasks through test-time scaling techniques like best-of-N sampling and tree search. However, these approaches often demand substantial computational resources, creating a critical trade-off between performance and efficiency. We introduce STAND (STochastic Adaptive N-gram Drafting), a novel model-free speculative decoding approach that
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2025Task-Oriented Dialogue (TOD) systems have become increasingly important for real-world applications, yet existing frameworks face significant challenges in handling unstructured information, providing multilingual support, and engaging proactively. We propose SMART (Scalable Multilingual Approach for a Robust TOD System), a novel TOD framework that effectively addresses these limitations. SMART combines
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CIKM 20252025Search query understanding (QU) is an important building block of the modern e-commerce search engines. QU extracts multiple intents from customer queries, including intended color, brand, etc. One of the most important tasks in QU is predicting which product category the user is interested in. In our work we are tapping into query product type classification (Q2PT) task. Compared to classification of full-fledged
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2025Data perspectivism goes beyond majority vote label aggregation by recognizing various perspectives as legitimate ground truths. However, current evaluation practices remain fragmented, making it difficult to compare perspectivist approaches and analyze their impact on differ-ent users and demographic subgroups. To ad-dress this gap, we introduce PersEval, the first unified framework for evaluating perspectivist
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2025Existing outfit recommendation frameworks focus on outfit compatibility prediction and complementary item retrieval. We present a text-driven outfit generation framework, Text2Outfit, which generates outfits controlled by text prompts. Our framework supports two forms of outfit recommendation: 1) Text-to-outfit generation, where the prompt includes the specification for each outfit item (e.g., product features
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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
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