AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Efficient semantic information processing and multi-hop knowledge reasoning have become essential technologies for intelligent information services and next-generation networked systems. To address inaccurate semantic understanding caused by short or ambiguous queries and insufficient reasoning capability under fragmen...
This work proposes Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation (D2F-ReAG), a novel paradigm that adaptively controls reasoning depth by judging the reliability of the root-level reasoning.
Document-level relation extraction (DocRE) aims to extract relations among multiple entities across extended contexts while maintaining consistency across predicted triples. Although large language models (LLMs) show remarkable reasoning capabilities in information extraction, their predictions are typically generated...
A novel framework KD-GAG is proposed that efficiently constructs KGs by distilling teacher LLMs' semantics and reasoning into a smaller student LLM and a preference-based subgraph pruning method to optimize the retrieval process.
Experiments show that DuPLeR achieves robust performance in data-scarce KGC scenarios, and a dual-pathway multimodal enhancement module regulates message passing with query-relevant multimodal signals and supplements entity representations after graph propagation.
Results indicate that integrating multi-source domain knowledge with relation-preserved retrieval and attribute-supported filtering provides more focused and inspectable evidence, thereby supporting more accurate complex material question answering.
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