✨Zep: A Temporal Knowledge Graph Architecture for Agent Memory

📝 Summary:
Zep is a new AI agent memory service using a temporal knowledge graph for dynamic knowledge integration. It outperforms MemGPT in benchmarks and significantly improves temporal reasoning and cross-session synthesis for enterprise applications, reducing latency.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2501.13956
• PDF: https://arxiv.org/pdf/2501.13956
• Github: https://github.com/getzep/graphiti

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#AIAgents #KnowledgeGraphs #TemporalReasoning #AIArchitecture #ArtificialIntelligence

✨Stemming Hallucination in Language Models Using a Licensing Oracle

📝 Summary:
This study presents the Licensing Oracle, an architectural solution to eliminate language model hallucinations. It enforces truth constraints via formal validation against structured knowledge graphs, achieving perfect abstention precision and zero false answers where statistical methods fail.

🔹 Publication Date: Published on Nov 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06073
• PDF: https://arxiv.org/pdf/2511.06073

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#LLM #AIHallucination #KnowledgeGraphs #NLP #AIResearch

✨Assessing LLMs for Serendipity Discovery in Knowledge Graphs: A Case for Drug Repurposing

📝 Summary:
SerenQA evaluates LLMs for discovering surprising, valuable serendipitous answers in scientific knowledge graphs, focusing on drug repurposing. It uses a new serendipity metric. Experiments show LLMs struggle with genuine surprising insights.

🔹 Publication Date: Published on Nov 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12472
• PDF: https://arxiv.org/pdf/2511.12472
• Project Page: https://cwru-db-group.github.io/serenQA
• Github: https://github.com/CWRU-DB-Group/DrugKG

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#LLM #KnowledgeGraphs #DrugRepurposing #AI #Serendipity

✨GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation

📝 Summary:
GraphGen is a framework that enhances synthetic data generation for LLMs by constructing fine-grained knowledge graphs. It targets high-value knowledge gaps, uses multi-hop sampling, and style-controlled generation to create diverse and accurate QA pairs. This approach outperforms conventional me...

🔹 Publication Date: Published on May 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.20416
• PDF: https://arxiv.org/pdf/2505.20416
• Project Page: https://huggingface.co/spaces/chenzihong/GraphGen
• Github: https://github.com/open-sciencelab/GraphGen

✨ Datasets citing this paper:
• https://huggingface.co/datasets/chenzihong/GraphGen-Data

✨ Spaces citing this paper:
• https://huggingface.co/spaces/chenzihong/GraphGen

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#LLMs #KnowledgeGraphs #SyntheticData #FineTuning #NLP

✨Wikontic: Constructing Wikidata-Aligned, Ontology-Aware Knowledge Graphs with Large Language Models

📝 Summary:
Wikontic is a multi-stage pipeline that builds high-quality, ontology-consistent knowledge graphs from text. It achieves state-of-the-art performance in information retention and efficiency, providing structured grounding for LLMs.

🔹 Publication Date: Published on Nov 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.00590
• PDF: https://arxiv.org/pdf/2512.00590

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#KnowledgeGraphs #LLMs #Ontologies #NLP #AI

✨Prometheus: Unified Knowledge Graphs for Issue Resolution in Multilingual Codebases

📝 Summary:
Prometheus is a multi-agent system that uses a unified knowledge graph of code repositories to resolve real-world issues across multiple programming languages. It improves upon existing methods by handling diverse languages and real-world scenarios.

🔹 Publication Date: Published on Jul 26, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.19942
• PDF: https://arxiv.org/pdf/2507.19942
• Github: https://github.com/Pantheon-temple/Prometheus

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#KnowledgeGraphs #MultiAgentSystems #CodeAnalysis #SoftwareEngineering #AI

✨GraphAgents: Knowledge Graph-Guided Agentic AI for Cross-Domain Materials Design

📝 Summary:
GraphAgents is a multi-agent AI framework using knowledge graphs to solve complex materials design problems. It deploys specialized agents for tasks like evidence retrieval and graph traversal, outperforming single-shot LLMs. This approach effectively identifies sustainable PFAS alternatives, exp...

🔹 Publication Date: Published on Feb 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07491
• PDF: https://arxiv.org/pdf/2602.07491

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#AI #KnowledgeGraphs #AgenticAI #MaterialsDesign #MultiAgentSystems

✨Benchmarking Large Language Models for Knowledge Graph Validation

📝 Summary:
This paper introduces FactCheck, a benchmark to evaluate LLMs for knowledge graph fact validation. Experiments show LLMs are not yet stable or reliable, and RAG or multi-model consensus offer inconsistent improvements, highlighting the need for such a benchmark.

🔹 Publication Date: Published on Feb 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.10748
• PDF: https://arxiv.org/pdf/2602.10748
• Github: https://github.com/FactCheck-AI

✨ Datasets citing this paper:
• https://huggingface.co/datasets/FactCheck-AI/FactCheck

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#LLMs #KnowledgeGraphs #FactChecking #AIResearch #Benchmarking

✨BubbleRAG: Evidence-Driven Retrieval-Augmented Generation for Black-Box Knowledge Graphs

📝 Summary:
BubbleRAG improves graph-based RAG recall and precision for black-box knowledge graphs. It uses semantic anchoring and bubble expansion to find relevant subgraphs, achieving state-of-the-art results on multi-hop QA.

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.20309
• PDF: https://arxiv.org/pdf/2603.20309
• Github: https://github.com/limafang/BubbleRAG

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#RAG #KnowledgeGraphs #AI #NLP #MachineLearning