AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Experiments on LoCoMo and MemConflict demonstrate that the proposed EMIR framework and intent-guided multi-round retrieval mechanism improves long-term memory utilization, dynamic and static conflict handling, and complex retrieval performance, achieving relative improvements of more than 12\% in certain categories.
A Retrieval-Augmented Generation framework for scientific image quality assessment, designed to simultaneously address both the understanding track (SIQA-U) and the scoring track (SIQA-S) of the SIQA challenge, is proposed.
GARDRec, a Graph-grounded Adaptive Reasoning and Decision-aware Recommendation framework for LLM-based next-item ranking, is proposed, which improves candidate-ranking performance over representative baselines and verifies the contributions of graph projection, neighborhood retrieval, explicit decision features, rankin...
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.
This work introduces a verifiable benchmark of 500 deep research tasks spanning 31 topics and 10 major categories, with three query forms designed to probe complementary capabilities required for deep research.
The proposed knowledge-enhanced visual diagnostic system enhances the transparency of traditional Chinese medicine diagnostic reasoning and the interpretability of treatment plans through knowledge graph-driven visualization and multimodal interaction, offering a practical solution for trustworthy artificial intelligen...
This work introduces Model Automated Deployment Engine (MADE), a dual-agent coordination system that iteratively constructs and validates the deployment artifacts, updates its deployment belief based on execution feedback, and revisits invalid upstream artifacts until the model is successfully served as a ready-to-call...
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