AINeutralarXiv – CS AI · May 126/10
🧠Researchers present SKG-VLA, an AI system that uses Scene Knowledge Graphs to improve decision-making in large-scale complaint handling by integrating multimodal evidence (text, images, metadata) with structured reasoning about entities, policies, and temporal events. The approach demonstrates improved accuracy and robustness across policy-grounded reasoning and long-tail scenarios.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce TESSERA, a neuro-symbolic framework that combines Large Language Models with Monte Carlo Tree Search to extract multi-step explanations from knowledge graphs, specifically for drug-disease mechanism discovery. The system uses LLMs for local judgments rather than autonomous generation, enforcing structural constraints through knowledge graphs while employing MCTS for principled credit assignment across extended reasoning chains.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers propose HAGE, a weighted multi-relational memory framework that improves how large language model agents retrieve and traverse information by treating memory as a dynamic graph rather than static lookups. The system uses reinforcement learning to optimize edge representations and routing behavior, achieving better long-horizon reasoning accuracy with improved efficiency compared to existing agentic memory systems.
AINeutralarXiv – CS AI · May 126/10
🧠MAGE introduces a novel framework for self-evolving language model agents that uses co-evolutionary knowledge graphs to preserve learned knowledge across iterations without modifying the base model. The system externalizes learning into structured memory subgraphs, enabling frozen backbone models to improve through retrieved guidance while maintaining inference stability across nine diverse benchmarks.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduced PrimeKG-CL, a benchmark dataset for continual graph learning built from nine biomedical databases with 129K+ nodes and 8.1M+ edges across two temporal snapshots (2021-2023). The work evaluates how different machine learning strategies handle evolving biomedical knowledge graphs, revealing that decoder choice and learning strategy interact significantly and that standard metrics fail to distinguish between retaining valid facts and forgetting outdated ones.
🏢 Hugging Face
AINeutralarXiv – CS AI · May 126/10
🧠Researchers developed a reflective storytelling agent that combines large language models with knowledge graphs and argumentation theory to generate personalized narratives for older adults. Testing with 55 participants showed the system successfully identified personally relevant purposes in two-thirds of narratives, with argument-based grounding and hallucination detection significantly improving perceived consistency and clarity.
AINeutralarXiv – CS AI · May 126/10
🧠PathISE is a novel framework that enables knowledge graph question-answering systems to learn effective supervision signals from answer-level labels alone, eliminating the need for expensive intermediate annotations. By using a transformer-based estimator to identify informative relation paths and distilling them into LLM path generators, the approach achieves competitive state-of-the-art performance while reducing resource requirements for training.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers present a modular, provenance-aware pipeline that converts handwritten archival tables into Knowledge Graphs while maintaining transparency through intermediate inspection points. The approach combines table structure recognition, handwriting recognition, and semantic interpretation while tracking data lineage to ensure all extracted information remains traceable to its source, addressing the opacity problem in end-to-end AI systems.
AINeutralarXiv – CS AI · May 125/10
🧠Researchers propose a multi-level graph attention network framework that uses contrastive learning to improve knowledge-graph-based recommendation systems. The approach addresses limitations in existing methods by leveraging multi-view learning and self-supervised techniques to better model user preferences and item representations.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce HOME-KGQA, a new benchmark dataset for evaluating knowledge graph question answering systems on household activities using multimodal data. The dataset reveals significant performance gaps in current LLM-based KGQA methods, highlighting critical challenges for real-world deployment of AI systems that combine language models with structured knowledge.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers introduce AdaTKG, a novel machine learning approach for temporal knowledge graph reasoning that maintains adaptive per-entity memory updated with each interaction, enabling better predictions on evolving relational data and improved handling of unseen entities compared to existing static representation methods.
AIBullisharXiv – CS AI · May 116/10
🧠GraphReAct introduces a new reasoning-acting framework that enhances large language models for multi-step inference over graph-structured data by combining topological and semantic retrieval actions with context refinement. The framework demonstrates consistent improvements over existing methods across six benchmark datasets, advancing how AI systems can reason about interconnected, structured information.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers introduce a neuro-symbolic framework combining Logic-Augmented Generation and Active Inference to extract and formalize tacit knowledge into machine-interpretable Knowledge Graphs. The approach addresses a critical gap in knowledge engineering by capturing implicit assumptions and contextual expertise from procedural domains like manufacturing, demonstrated through analysis of assembly repair videos.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers propose DCGL, a dual-channel graph learning framework that combines Knowledge Graphs with Large Language Models to improve recommendation systems. The method addresses limitations in current approaches by separately modeling semantic and behavioral patterns, using contrastive learning and adaptive fusion to achieve better performance across sparse and active user scenarios.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers introduce HYPER, a foundation model for predicting missing connections in knowledge hypergraphs that can generalize to novel entities and relation types unseen during training. The model advances inductive link prediction by encoding entity positions within hyperedges, enabling transfer learning across relations of varying complexity, with evaluation on 16 new datasets showing consistent outperformance of existing methods.
AINeutralarXiv – CS AI · May 96/10
🧠Researchers introduce TGS-RAG, a framework that combines text and graph-based retrieval to improve how large language models answer complex questions. The system addresses limitations in existing approaches by enabling bidirectional communication between text and structured data, improving both accuracy and computational efficiency in multi-hop reasoning tasks.
AINeutralarXiv – CS AI · May 96/10
🧠Researchers propose using graphlets—small recurring subgraph patterns—as structural tokens for Knowledge Graph Foundation Models (KGFMs), enabling better transfer learning across diverse knowledge graphs. Testing on 51 knowledge graphs demonstrates that this approach outperforms existing KGFMs for zero-shot link prediction tasks.
AIBullisharXiv – CS AI · May 96/10
🧠Researchers propose a Knowledge Graph-based approach to improve AI-assisted formal verification of hardware designs, addressing the challenge of generating accurate SystemVerilog Assertions from natural-language specifications. By structuring design information from RTL code, specifications, and tool feedback into a queryable knowledge graph, the method achieves higher compilation success rates and formal coverage (78.5%-99.4%) while reducing syntax errors, though complex temporal reasoning remains challenging.
AINeutralarXiv – CS AI · May 16/10
🧠Researchers have developed an agentic framework that uses knowledge graphs to help large language models understand and reason about AI policy documents. The system was tested on multiple AI safety regulations, demonstrating that knowledge graph augmentation improves LLM performance across various reasoning tasks from simple entity lookup to complex cross-policy inference.
AINeutralarXiv – CS AI · Apr 206/10
🧠A comprehensive survey examines how Large Language Models can be effectively integrated with graph-based data structures to improve reasoning, retrieval, and decision-making across domains. The research categorizes integration approaches by purpose, graph type, and strategy, providing practitioners with guidance on selecting appropriate techniques for specific applications in healthcare, finance, robotics, and other fields.
AINeutralarXiv – CS AI · Apr 206/10
🧠Researchers present a novel method combining Large Language Models and Knowledge Graphs to enhance the interpretability of Machine Learning models in manufacturing environments. The approach stores domain-specific data and ML results in a structured knowledge graph, then uses an LLM to generate user-friendly explanations of ML predictions, demonstrating practical applicability in real-world manufacturing decision-making.
AINeutralarXiv – CS AI · Apr 156/10
🧠Researchers propose Opinion-Aware Retrieval-Augmented Generation (RAG) to address a critical bias in current LLM systems that treat subjective content as noise rather than valuable information. By formalizing the distinction between factual queries (epistemic uncertainty) and opinion queries (aleatoric uncertainty), the team develops an architecture that preserves diverse perspectives in knowledge retrieval, demonstrating 26.8% improved sentiment diversity and 42.7% better entity matching on real-world e-commerce data.
AIBullisharXiv – CS AI · Apr 156/10
🧠Researchers introduce KG-Reasoner, an end-to-end framework that uses reinforcement learning to train large language models to perform multi-hop reasoning over knowledge graphs without decomposing tasks into isolated pipeline steps. The approach demonstrates competitive or superior performance across eight reasoning benchmarks by enabling LLMs to dynamically explore reasoning paths and backtrack when necessary.
AINeutralarXiv – CS AI · Apr 156/10
🧠Researchers propose a graph-based soft prompting framework that enables LLMs to reason over incomplete knowledge graphs by processing subgraph structures rather than explicit node paths, achieving state-of-the-art results on multi-hop question-answering benchmarks while reducing computational costs through a two-stage inference approach.
AIBullisharXiv – CS AI · Apr 156/10
🧠Researchers introduce RALP, a novel method that uses chain-of-thought prompts with large language models to improve knowledge graph predictions, outperforming traditional embedding models by over 5% on standard benchmarks while better handling unseen entities, relations, and numerical data.
