Standard Large Language Model (LLM) pretraining typically treats corpora as flattened token sequences, often overlooking the realworld context that humans naturally rely on to contextualize information.To bridge this gap, we introduce Knowledge Coordinate Conditioning (KoCo), a simple method that maps every document into a three-dimensional semantic coordinate.By prepending these coordinates as textual prefixes for pre-training, we aim to equip the model with explicit contextual awareness to learn the documents within the real-world knowledge structure.Experiment results demonstrate that KoCo significantly enhances performance across 10 downstream tasks and accelerates pre-training convergence by approximately 30%.Furthermore, our analysis indicates that explicitly modeling knowledge coordinates helps the model distinguish stable facts from noise, effectively mitigating hallucination in generated outputs.# Role: Knowledge Taxonomist & Data Curator # Core Task: Your task is to analyze the provided [TEXT] and its source [URL], and generate a single, concise "Knowledge Context Meta-Tag."This tag serves solely to inform the training model of the "position", "sourcle", and "attribute" of the text it will read within the broader human knowledge system.Note that your goal is to **classify text**, not **summarize the content of the text**.Do not expand or modify existing information.-# Step Guide: 1. **Analysis [URL]:** * Check the domain name (e.g

The National Center for Biotechnology Information (NCBI) Taxonomy is extensively used in biomedical and ecological research. Typical demands include computing the lowest common ancestor, determining descendant relationships, and listing the descendants of a node. However, existing tools often suffer from inefficient runtime performance. To address this challenge, our paper introduces a novel indexing method, BIT, designed specifically for tree-like data. BIT first encodes the tree-like structure into a bit-vector using the Polychotomic encoding algorithm, subsequently storing the bit-vector in a bitmap. By employing parallel bit operations, BIT significantly accelerates the speed of typical computational tasks. Experimental results on public datasets demonstrate that BIT outperforms baseline systems in task execution performance.

The data warehouse system Hive has emerged as an important facility for supporting data computing and storage. In particular, RCFile is a tailor-made data placement structure implemented in Hive, which is designed for the data processing efficiency. In this paper, we propose several optimized schemes based on RCFile and introduce EStore, which is an optimized data placement structure that is able to improve the query rate and reduce storage space for Hive. Specifically, it adopts both row-store and column-store in blocks, and further classifies the columns by the frequency of each table-column. Moreover, we also employ the classic RDP code to store files of the data table. We conduct experiments on a real cluster, and the results show that EStore has better features in terms of data query rate and storage space compared with RCFile.