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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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SIGMOD/PODS 20262026We present Aurora Limitless Database, a cloud-native distributed database system that extends Amazon Aurora PostgreSQL with horizontal scaling capabilities while maintaining strong consistency guarantees. The system provides transparent scalability using a router layer for query distribution and a storage layer of PostgreSQL shards, which eliminates the need for application-level sharding. Our key technical
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ECML-PKDD 20262026Graph Neural Networks (GNNs) break down on zero-degree nodes, as message passing requires neighbors. Without interaction history, unseen entities are sub-optimally embedded, leaving them weakly anchored in the latent space, creating a cold-start bottleneck in retrieval. To address this, we propose GRAFT, a factorized architecture that unifies structural and feature transformations into a shared weight space
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ECML-PKDD 20262026Accurate demand forecasting is vital for retail supply chain efficiency, yet a persistent trust-capacity gap limits industrial production to low-capacity interpretable models that fail to capture complex market dynamics. We propose Anchored FLoE, a dual-model framework that bridges this gap by fusing high-capacity deep learning with rigorous business guardrails. The framework integrates: (1) FLoE, an ensemble
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2026Recent progress in multi-turn reinforcement learning (RL) has significantly improved reasoning LLMs' performances on complex interactive tasks. Despite advances in stabilization techniques such as fine-grained credit assignment and trajectory filtering, instability remains pervasive and often leads to training collapse. We argue that this instability stems from inefficient exploration in multi-turn settings
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KDD 20262026Vision language models face a fundamental geometry trade-off: Euclidean representations excel at instance-level discrimination, while hyperbolic representations naturally encode semantic hierarchies. Hybrid training is challenging because one geometry may dominate early, leaving the other under-trained failure mode we term geometry dominance. We introduce Adaptive Geometry Routing (AGR), a framework that
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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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