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arXiv:2602.20044v1 Announce Type: cross Abstract: Within twelve days of launch, an AI-native social platform exhibits extreme attention concentration, hierarchical role separation, and one-way attention flow, …
arXiv:2602.23368v1 Announce Type: cross Abstract: While Retrieval-Augmented Generation (RAG) has proven effective for generating accurate, context-based responses based on existing knowledge bases, it presents several …
arXiv:2602.23369v1 Announce Type: cross Abstract: Traditional multimodal retrieval systems rely primarily on bi-encoder architectures, where performance is closely tied to embedding dimensionality. Recent work, Think-Then-Embed …
arXiv:2602.23370v1 Announce Type: cross Abstract: Long-document topic segmentation plays an important role in information retrieval and document understanding, yet existing methods still show clear shortcomings …
arXiv:2602.23371v1 Announce Type: cross Abstract: Legal research in India involves navigating long and heterogeneous documents spanning statutes, constitutional provisions, penal codes, and judicial precedents, where …
arXiv:2602.23372v1 Announce Type: cross Abstract: GraphRAG systems improve multi-hop retrieval by modeling structure, but many approaches rely on expensive LLM-based graph construction and GPU-heavy inference. …
arXiv:2602.23374v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into enterprise knowledge management systems has been catalyzed by the Retrieval-Augmented Generation (RAG) …
arXiv:2602.23378v1 Announce Type: cross Abstract: Innovative HealthTech teams develop Artificial Intelligence (AI) systems in contexts where ethical expectations and organizational priorities must be balanced under …
arXiv:2602.23387v1 Announce Type: cross Abstract: Recent advancements in Large Audio Language Models (LALMs) have demonstrated exceptional performance in speech recognition and translation. However, existing models …
arXiv:2602.23388v1 Announce Type: cross Abstract: The rising demand for inclusive speech technologies amplifies the need for multilingual datasets for Natural Language Processing (NLP) research. However, …
arXiv:2602.23407v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in code generation, yet they remain prone to producing security vulnerabilities. Existing …
arXiv:2602.23409v1 Announce Type: cross Abstract: Quantum machine learning models using angle encoding naturally represent truncated Fourier series, providing universal function approximation capabilities with sufficient circuit …