CORAL uses an agentic loop to adaptively refine retrieval corpora and queries in multilingual RAG based on evidence critique, yielding up to 3.58 percentage point accuracy gains on low-resource language cultural QA benchmarks.
A hybrid rag sys- tem with comprehensive enhancement on complex reason- ing
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
DEFENGRAPH integrates a dual-layer static-dynamic KG with LLMs via path retrieval, filtering, and re-ranking, raising reasoning-recall from 61.45% to 73.49% and ticket-action recall from 52.17% to 72.46% on GPT-4o in live red-blue cyber range data.
CRVA-TGRAG combines parent-document segmentation, ensemble retrieval, and teacher-guided fine-tuning to mitigate knowledge conflicts and improve accuracy in LLM-based CVE vulnerability analysis.
citing papers explorer
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CORAL: Adaptive Retrieval Loop for Culturally-Aligned Multilingual RAG
CORAL uses an agentic loop to adaptively refine retrieval corpora and queries in multilingual RAG based on evidence critique, yielding up to 3.58 percentage point accuracy gains on low-resource language cultural QA benchmarks.
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DEFENGRAPH: Knowledge Graph-Enhanced LLMs for Blue Team Cyber Defense
DEFENGRAPH integrates a dual-layer static-dynamic KG with LLMs via path retrieval, filtering, and re-ranking, raising reasoning-recall from 61.45% to 73.49% and ticket-action recall from 52.17% to 72.46% on GPT-4o in live red-blue cyber range data.
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Tug-of-War within A Decade: Conflict Resolution in Vulnerability Analysis via Teacher-Guided Retrieval-Augmented Generations
CRVA-TGRAG combines parent-document segmentation, ensemble retrieval, and teacher-guided fine-tuning to mitigate knowledge conflicts and improve accuracy in LLM-based CVE vulnerability analysis.