✨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