HyperPatch reformulates sequential n-ary knowledge editing as hypergraph manifold stability, using HGNN initialization, SimHash alignment plus Topological LoRA, and fused reasoning to achieve large H-Acc gains on MQuAKE benchmarks.
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Parameter-based knowledge editing in LLMs induces reasoning collapse via dimensional collapse and is consistently outperformed by a retrieval baseline across varied edit counts, knowledge complexity, and evaluation metrics.
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HyperPatch: Sequential Knowledge Editing Under n-ary Structural Drift
HyperPatch reformulates sequential n-ary knowledge editing as hypergraph manifold stability, using HGNN initialization, SimHash alignment plus Topological LoRA, and fused reasoning to achieve large H-Acc gains on MQuAKE benchmarks.
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Revisiting Parameter-Based Knowledge Editing in Large Language Models: Theoretical Limits and Empirical Evidence
Parameter-based knowledge editing in LLMs induces reasoning collapse via dimensional collapse and is consistently outperformed by a retrieval baseline across varied edit counts, knowledge complexity, and evaluation metrics.