A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Sertaç Karaman, Emilio Frazzoli
Introduces PRM* and RRT*, provably asymptotically optimal sampling-based motion planning algorithms, with analysis via random geometric graphs.
Shirui Pan, Linhao Luo, Yufei Wang, et al.
This paper presents a roadmap for unifying large language models and knowledge graphs through three frameworks: KG-enhanced LLMs, LLM-augmented KGs, and synergized LLMs+KGs.
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Proposes a multi-granularity context-enhanced RAG framework that retrieves over multimodal knowledge graphs to improve multimodal reasoning.
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GraphThink uses task graphs to enhance LLM planning for long-horizon embodied tasks, with GRPO-based reward design.
Aldo Pareja, Giacomo Domeniconi, Jie Chen, et al.
EvolveGCN adapts GCN parameters over time using an RNN to handle dynamic graphs without relying on node embeddings.
Saman Marandi, Yu-Shu Hu, Mohammad Modarres
This paper presents a framework for automated construction of Dynamic Master Logic models as knowledge graphs from technical documentation using retrieval-augmented large language models, validated on a nuclear reactor system.
Ling Yue, K. Bhandari, Ching-Yun Ko, et al.
This survey reviews methods for designing and optimizing LLM agent workflows, treating them as agentic computation graphs and organizing literature by when workflow structure is determined.
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This paper introduces a mixture model CNN framework for geometric deep learning on graphs and manifolds, achieving state-of-the-art results in deformable 3D shape analysis.
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This paper presents a unified geometric deep learning framework that generalizes CNNs and other architectures to non-Euclidean domains such as graphs and manifolds.
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This paper provides an overview of geometric deep learning, an emerging field that extends deep learning techniques to non-Euclidean data such as graphs and manifolds.
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This survey systematically reviews Knowledge Distillation on Graphs (KDG), a technique combining graph neural networks and knowledge distillation to deploy efficient models on graphs.
R. Rashmi, V. Upadhya
MAHA is a novel multimodal RAG architecture that uses modality-aware knowledge graphs and hybrid retrieval to improve unstructured data retrieval.