Customer-obsessed science

Large language model (LLM) agents deployed in healthcare and life sciences (HCLS) routinely receive queries that are semantically ambiguous—the same terms carry different meanings across clinical, regulatory, pharmacovigilance, data-standards, and research domains. Existing approaches address ambiguity post-hoc through output filtering or retrieval augmentation, but do not quantify it before the model responds

Continual learning methods for vision-language models are developed on benchmarks where each new task introduces entirely new domain knowledge. Real-world task sequences are more natural: they routinely share visual concepts, language patterns, and even training samples across stages. However, existing mixture-of-expert methods that assign one expert per task with fixed routing can split similar inputs

Time reasoning is a make-or-break capability for Large Language Models (LLMs) aspiring to act as reliable personal and enterprise assistants. This paper introduces the Temporal Reasoning Dataset (TRD), a programmatically generated multilingual benchmark designed to evaluate temporal reasoning operational capabilities in LLMs across ten languages, with particular focus on basic operations relevant to conversational

We 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

Graph 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