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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 26, 20265 min read
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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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2025Large Language Models (LLMs) have demonstrated exceptional performance in natural language processing tasks, yet their massive size makes serving them inefficient and costly. Semistructured pruning has emerged as an effective method for model acceleration, but existing approaches are suboptimal because they focus on local, layer-wise optimizations using heuristic rules, failing to leverage global feedback
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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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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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2025Since the seminal work of TabPFN, research on tabular foundation models (TFMs) based on in-context learning (ICL) has challenged long-standing paradigms in machine learning. Without seeing any real-world data, models pretrained on purely synthetic datasets generalize remarkably well across diverse datasets, often using only a moderate number of in-context examples. This shifts the focus in tabular machine
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NeurIPS 2025 Workshop on Multimodal Algorithmic Reasoning2025Large Language Models (LLMs) perform well on short-horizon tasks but struggle with long-horizon, multimodal scenarios that require multi-step reasoning, perception, and adaptive planning. We identify two key challenges in these settings: the difficulty of long-term coordination between planning and execution within single-agent architectures and the inefficiency of indiscriminate visual grounding. To address
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Gretchen Ertl
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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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