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Paper · arXiv

PRISM Edit: One Vector for All Temporal Answers

Model editing keeps large language models (LLMs) up to date without retraining, but temporal facts expose a limitation of the prevailing locate-and-edit paradigm: an update is not always a replacement. When a fact changes, the new answer should become current while the old answer may remain correct in historical time contexts. Building on this insight, we use causal tracing to show that LLMs already support this distinction via a two-stage internal computation: early MLP layers retrieve a time-agnostic subject representation, and later layers modulate it with temporal context to yield the time-correct answer. Motivated by this finding, we introduce PRISM Edit, which optimizes a single pol...

AuthorsChen Huang, Qi Zheng, Ruiqin Zheng et al.
Primary categorycs.LG
Published13 July 2026
Last updated at source31 August 2026
cs.LGcs.AI
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