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HyperPatch proposes a parameter-preserving framework for sequential knowledge editing under n-ary structural drift, using hypergraph neural networks to maintain event integrity. It achieves 96.24% and 21.06% relative improvements in Hop-wise Accuracy on MQuAKE-CF and MQuAKE-T benchmarks, respectively.
Introduces GHI, a Graphormer-over-conditioned-hypergraph-incidence framework for aspect-based sentiment analysis that represents linguistic evidence as token–hyperedge incidence relations, achieving state-of-the-art results on six benchmarks with only 247M parameters.
This paper proposes Hyper-Align, a framework that serializes hypergraph structures into tokens via HIDT-O and HIP, enabling LLMs to process high-order relationships, and introduces HyperAlign-Bench for evaluation.
Aperio is a programming language designed to reduce the translation cost between human mental models and LLM code generation by using a structural model based on recursive hypergraphs of typed units called loci.
HEAR is an enterprise agentic reasoner using a Stratified Hypergraph Ontology to perform multi-hop reasoning over heterogeneous business systems, achieving up to 94.7% accuracy on supply-chain tasks.