Revisiting Ripple Effects in Knowledge Editing through Pressure-Aware Joint Neighborhood Optimization

Abstract:Single-edit updates in large language models can trigger ripple effects across local knowledge neighborhoods: desirable propagation to related facts and unintended perturbation of preserved ones. Existing methods address these two effects separately, without explicitly modeling their coupling. We challenge this separation through an analysis of ripple responses across typical baselines, identifying two coupled design pressures: editable-side coordination and preserved-side leakage. We propose Joint Neighborhood Optimization (JNO), a new knowledge-editing framework to formalize and jointly address both pressures at the target-planning stage. JNO instantiates this principle through Pressure-Aware Coordination (PAC), which jointly optimizes neighborhood target representations under coupled constraints, and a semantic pre-execution gate that rejects high-risk target plans before parameter execution. Experiments on RippleEdits show JNO improves propagation and preservation metrics by at least 7.0% while preserving cross-backbone editing stability.

Artificial Intelligence (cs.AI)

arXiv:2606.01610 [cs.AI]

(or arXiv:2606.01610v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.01610 Focus to learn more arXiv-issued DOI via DataCite

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