Customer-obsessed science

E-commerce product titles often include redundant information that negatively impacts the user experience. Removing repeated words through restructuring and paraphrasing can make titles more concise and improve readability. While large language models can optimize titles, their computational cost makes them impractical for large-scale applications. In this paper, we first analyze the sources of repetition

Mapping cyber threat intelligence (CTI) text to MITRE ATT&CK techniques is essential for structured threat analysis, yet manual annotation is costly and does not scale. The ATT&CK taxonomy comprises several hundred attack techniques, and a single CTI passage may describe multiple techniques, making accurate and complete extraction challenging. Existing automated approaches fall short in different ways:

Quantization of Large Language Models (LLMs) is often hindered by the sensitivity of the self-attention mechanism to discretization errors. We identify the softmax operator as a bottleneck for quantization stability due to its sensitivity to outliers and state-dependent Jacobian. We theoretically establish that suppressing the norm of this Jacobian helps in bounding quantization-induced performance degradation

Compound retrieval-augmented question-answering (QA) systems present a fundamental evaluation challenge: manual annotation does not scale, yet automated evaluation lacks the ground truth necessary for calibration. We introduce a self-improving evaluation architecture that addresses this circular dependency through three contributions. First, iterative consensus synthesis: an algorithm that treats LLM-human

Industrial recommender systems typically operate in two stages: retrieving a candidate set from a large catalog, then ranking those candidates using contextual information. The ranking stage relies on features that summarize a user's prior interactions with the system. These features are often carefully hand-crafted, and designing and maintaining them is time-consuming and computationally expensive. In