Hyper-Align is a hypergraph-native framework that serializes high-order relations into LLM-compatible tokens via HIDT-O templates and a HIP projector, outperforming graph-centric methods on HyperAlign-Bench.
arXiv preprint arXiv:2308.06374 , year=
6 Pith papers cite this work. Polarity classification is still indexing.
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EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.
A multi-agent LLM-based framework extracts knowledge graphs from 50 real Ethernet switch manuals with 0.97-0.99 correctness to enable downstream test case specification generation.
VeriLLMed is an interactive visual debugging tool that maps LLM diagnostic reasoning to knowledge graphs to identify and categorize relation, branch, and missing errors.
AGI robots learn and deduce using Belnap's 4-valued bilattice and Closed Knowledge Assumption to expand knowledge while supporting inconsistencies and providing logical security.
A survey classifying hallucination phenomena specific to large foundation models, establishing evaluation criteria, examining mitigation strategies, and discussing future directions.
citing papers explorer
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Hypergraph as Language
Hyper-Align is a hypergraph-native framework that serializes high-order relations into LLM-compatible tokens via HIDT-O templates and a HIP projector, outperforming graph-centric methods on HyperAlign-Bench.
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EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval
EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.
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Supporting System Testing with a Multi-Agent LLM-based Framework for Knowledge Graph Extraction: A Case Study with Ethernet Switch Systems
A multi-agent LLM-based framework extracts knowledge graphs from 50 real Ethernet switch manuals with 0.97-0.99 correctness to enable downstream test case specification generation.
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VeriLLMed: Interactive Visual Debugging of Medical Large Language Models with Knowledge Graphs
VeriLLMed is an interactive visual debugging tool that maps LLM diagnostic reasoning to knowledge graphs to identify and categorize relation, branch, and missing errors.
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Neuro-Symbolic Strong-AI Robots with Closed Knowledge Assumption: Learning and Deductions
AGI robots learn and deduce using Belnap's 4-valued bilattice and Closed Knowledge Assumption to expand knowledge while supporting inconsistencies and providing logical security.
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A Survey of Hallucination in Large Foundation Models
A survey classifying hallucination phenomena specific to large foundation models, establishing evaluation criteria, examining mitigation strategies, and discussing future directions.