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July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. -
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July 9, 202610 min read
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Hosted by Danielle Perszyk, cognitive scientist at Amazon, 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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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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UAI 20252025In LLM alignment and many other ML applications, one often faces the Multi-Objective Fine-Tuning (MOFT) problem, i.e., fine-tuning an existing model with datasets labeled w.r.t. different objectives simultaneously. To address the challenge, we propose a Conditioned One-Shot fine-tuning framework (COS-DPO) that extends the Direct Preference Optimization technique, originally developed for efficient LLM alignment
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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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Nature's Nutrition & Diabetes Journal2025Background/Objectives: We present the first receiver operating characteristic (ROC) diagnostic analysis of skin-excreted acetone as a biomarker of ketosis. Participants/Methods: In a pilot study involving 16 healthy participants, we investigated the ability of skin-excreted acetone to differentiate between ketosis and non-ketosis states. Non-ketosis conditions were established under a normal diet and energy
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2025Retrieval-Augmented Generation (RAG) grounds large language models in external evidence, yet it still falters when answers must be pieced together across semantically distant documents. We close this gap with the Hierarchical Lexical Graph (HLG), a three-tier index that (i) traces every atomic proposition to its source, (ii) clusters propositions into latent topics, and (iii) links entities and relations
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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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