Customer-obsessed science

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

Large language models (LLMs) are increasingly used as judges to evaluate, rank, and supervise other models, yet their reliability in judging LLMs' reasoning process under long-context settings remains underexplored. Existing benchmarks either overly rely on human annotators, who may miss subtle flaws in lengthy reasoning chains, or focus solely on final responses while ignoring the underlying context and

This paper presents SOMO, a scalable framework for cross-embodiment grasp synthesis that transfers to novel robot hands using only their hand description (i.e., a Unified Robot Description Format (URDF) file), without requiring any hand–object interaction annotations. Unlike prior approaches that rely on hand-specific models or annotated grasp data for each embodiment, SOMO introduces a shared Morphology-Prior