Customer-obsessed science

Length control in Large Language Models (LLMs) is a crucial but under-addressed challenge, with applications ranging from voice interfaces requiring concise responses to research summaries needing comprehensive outputs. Current approaches to length control, including Regularized DPO, Length-Instruction Fine-Tuning, and tool-augmented methods, typically require expensive model retrain-ing or complex inference-time

With the advancement of robot manipulator technologies, developing custom-made low-cost manipulator arms has gained increasing popularity in the field of robotics. However, these low-cost systems often lack precise sensing and control capabilities, making reliable collision detection particularly critical to ensure safe operation. Popular methods of collision detection, which estimate external disturbances

Multilingual information retrieval has emerged as powerful tools for expanding knowledge sharing across languages. On the other hand, resources on high quality knowledge base are often scarce and in limited languages, therefore an effective embedding model to transform sentences from different languages into a feature vector space same as the knowledge base language becomes the key ingredient for cross

Online randomized controlled experiments (A/B tests) measure causal changes in industry. While these experiments use incremental changes to minimize disruption, they often yield statistically insignificant results due to low signal-to-noise ratios. Precision improvement (or reducing standard error) traditionally focuses on trigger observations - where treatment and control outputs differ. Though effective

In this paper, we present CACHE-ED, a novel framework for document entity extraction that combines the power of large language models (LLMs) with graph-based document representations, caching mechanisms, and an actor-critic multi-agent architecture. Our approach addresses the inefficiencies and inaccuracies that are common in extracting structured information from documents, particularly in templated formats