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June 8, 20267 min readFour approaches can dramatically improve the performance and trustworthiness of AI agents in operational environments. -
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May 27, 20264 min readMachine learning
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Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features researchers tackling the hardest problems in agentic AI — from building reliable perception systems to designing training environments that mirror human learning.
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.
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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ACL 2026 Workshop on Generation, Evaluation & Metrics (GEM)2026Recent studies have highlighted that Large Language Models (LLMs) often exhibit limited coherence, that is the ability to produce consistent responses to semantically equivalent questions. While most prior research has focused exclusively on English, limited investigation has been conducted on other languages. In this work, we study the coherence of LLMs on Question Answering tasks across six languages:
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EUSIPCO 20262026We present VCNAC, a variable-channel neural audio codec. Our approach features a single encoder and decoder parametrization that enables native inference for different channel setups, from mono speech to cinematic 5.1 channel surround audio. Channel compatibility objectives ensure that multichannel content maintains perceptual quality when decoded to fewer channels. The shared representation enables training
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2026Multimodal Large Language Models (MLLMs) have recently demonstrated promising capabilities in multimodal coding tasks such as chart-to-code generation. However, existing methods primarily rely on supervised fine-tuning (SFT), which requires the model to learn code patterns through chart-code pairs but does not expose the model to a code execution environment. Moreover, while self-correction through execution
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VLDB 20262026Accurate optimizer statistics are fundamental to query and ML-prediction performance in modern database systems, yet maintaining them poses a significant challenge for large-scale data warehouses. Traditional statistics collection relies on full table scans, which become prohibitively expensive as tables grow to billions of rows and beyond. This creates a critical tension: statistics must be kept current
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CVPR 2026 Workshop on Personalization in Generative AI2026Makeup transfer models enable fun augmented reality (AR) experiences as well as virtual try-on (VTO) for online makeup shopping. While recent state-of-the-art diffusion-based solutions such as Stable-Makeup [45] dramatically improve the accuracy and realism of makeup transfer, they still face limitations in identity and skin color preservation, making production-level VTO for makeup shopping unrealistic
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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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