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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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2025Condition-based monitoring (CBM) is essential for maintaining high machine uptime in industrial settings. While existing CBM solutions effectively use time-series data (e.g., thermal, vibration, amperage, etc.), these can be enhanced with LLMs to integrate domain knowledge and generate interpretable summaries. However, LLMs often incur higher latency and cost than traditional methods. We thus propose LEAD
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NeurIPS 2025 Workshop on Mathematical Reasoning and AI2025We present an approach for training language models to interactively prove theorems using the Lean proof assistant. Our approach enables models to propose partial proofs, receive verification feedback, and iteratively refine their proofs. We develop a synthetic data generation pipeline that converts static proof datasets into multi-turn interactive sequences, complete with incremental verification feedback
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NeurIPS 2025 AI4NextG2025This paper presents a pilot study toward a foundation model for wireless sensing using FMCW radar. We propose a transformer-based architecture trained on data from mmWave sensor with self-supervised objectives designed to capture temporal–spatial signal characteristics without labels. Our framework introduces strategies for handling sparse channel representations and provides a unified normalization across
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2025Unlearning has been proposed to remove copyrighted and privacy-sensitive data from Large Language Models (LLMs). Existing approaches primarily rely on fine-tuning-based methods, which can be categorized into gradient ascent-based (GA-based) and suppression-based methods. However, they often degrade model utility (the ability to respond to normal prompts). In this work, we aim to develop a general framework
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ICCV 2025 Workshop on Computer Vision in Advertising and Marketing2025Financial institutions continue to process millions of handwritten forms despite digital transformation efforts, creating a significant operational bottleneck. This research addresses the persistent challenge of automating handwritten data extraction from financial documents by introducing a four-stage processing pipeline that significantly out-performs existing solutions. Our approach sequentially combines
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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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