Millimeter-scale particles of nuclear-reactor fuel are encased in four layers of different materials that act as a “miniature containment system”.
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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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2026Vision language models (VLMs) often generate hallucination, i.e., content that cannot be substantiated by either textual or visual inputs. Prior work primarily attributes this to over-reliance on linguistic prior knowledge rather than visual inputs.Some methods attempt to mitigate hallucination by amplifying visual token attention proportion-ally to their attention scores. However, these methods overlook
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AAAI 2026 Workshop on Trustworthy Agentic AI2026Large Language Models (LLMs) have shown remarkable capabilities in tool calling and tool usage, but suffer from hallucinations where they choose incorrect tools, provide malformed parameters and exhibit 'tool bypass' behavior by performing simulations and generating outputs instead of invoking specialized tools or external systems. This undermines the reliability of LLM based agents in production systems
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WSDM 2026 Generative AI for Streaming Media (GenAI4SM)2026Collaborative filtering is a foundational component of music recommender systems, powering a variety of recommendation tasks from retrieving the most relevant tracks, albums, artists, and podcasts for a given user to more nuanced objectives such as content discovery, familiar listening, and new release recommendation. To enable scalable, low-latency inference, content-retrieval models compute latent user
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ICLR 2026 Workshop on Algorithmic Fairness Across Alignment Procedures and Agentic Systems2026When an AI assistant remembers that Sarah is a single mother working two jobs, does it interpret her stress differently than if she were a wealthy executive? As personalized AI systems increasingly incorporate long-term user memory, understanding how this memory shapes emotional reasoning is critical. We investigate how user memory affects emotional intelligence in large language models (LLMs) by evaluating
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ECML-PKDD 20262026Real-time bidding (RTB) in sponsored search advertising has been extensively studied, yet a critical gap remains: how should advertisers set optimal bids in Manual Targeting (MT) campaigns where bids must be specified upfront without real-time adjustment? Unlike Automated Targeting campaigns that dynamically modify bids based on auction context, MT campaigns, which account for nearly 30% of advertisers
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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.
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