@inproceedings{tian-etal-2026-p,
title = "{P}-Space: Puncturing the Null-Space to Resolve Cognitive Dissonance in Continual Knowledge Editing",
author = "Tian, Shiqiang and
Ding, Cheng and
Chen, Qin and
Zhou, Jie and
Li, Xin and
He, Liang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.757/",
pages = "15945--15964",
ISBN = "979-8-89176-462-0",
abstract = "Existing continual knowledge editing methods suffer from cognitive dissonance: after an edit, the model answers the edit prompt correctly but reverts to the old fact under complex equivalent queries. This means old and new facts coexist rather than the old being replaced. The root cause of cognitive dissonance is that existing methods fit the new fact on the target prompt without overwriting the old one. In constrained methods, static null-space projectors lock outdated representations into the protected subspace, forcing updates into orthogonal directions. To resolve cognitive dissonance, we propose P-Space (Puncturing the Null-Space to Resolve Cognitive Dissonance in Continual Knowledge Editing), which at each edit step (1) punctures the protected covariance to unprotect the outdated representation, (2) overwrites the old mapping in closed form along its original direction under the punctured projector, and (3) anchors the new fact into the covariance with adaptive scaling so that future edits preserve it. Experiments on two datasets show that P-Space outperforms existing methods while preserving general capabilities."
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<abstract>Existing continual knowledge editing methods suffer from cognitive dissonance: after an edit, the model answers the edit prompt correctly but reverts to the old fact under complex equivalent queries. This means old and new facts coexist rather than the old being replaced. The root cause of cognitive dissonance is that existing methods fit the new fact on the target prompt without overwriting the old one. In constrained methods, static null-space projectors lock outdated representations into the protected subspace, forcing updates into orthogonal directions. To resolve cognitive dissonance, we propose P-Space (Puncturing the Null-Space to Resolve Cognitive Dissonance in Continual Knowledge Editing), which at each edit step (1) punctures the protected covariance to unprotect the outdated representation, (2) overwrites the old mapping in closed form along its original direction under the punctured projector, and (3) anchors the new fact into the covariance with adaptive scaling so that future edits preserve it. Experiments on two datasets show that P-Space outperforms existing methods while preserving general capabilities.</abstract>
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%0 Conference Proceedings
%T P-Space: Puncturing the Null-Space to Resolve Cognitive Dissonance in Continual Knowledge Editing
%A Tian, Shiqiang
%A Ding, Cheng
%A Chen, Qin
%A Zhou, Jie
%A Li, Xin
%A He, Liang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F tian-etal-2026-p
%X Existing continual knowledge editing methods suffer from cognitive dissonance: after an edit, the model answers the edit prompt correctly but reverts to the old fact under complex equivalent queries. This means old and new facts coexist rather than the old being replaced. The root cause of cognitive dissonance is that existing methods fit the new fact on the target prompt without overwriting the old one. In constrained methods, static null-space projectors lock outdated representations into the protected subspace, forcing updates into orthogonal directions. To resolve cognitive dissonance, we propose P-Space (Puncturing the Null-Space to Resolve Cognitive Dissonance in Continual Knowledge Editing), which at each edit step (1) punctures the protected covariance to unprotect the outdated representation, (2) overwrites the old mapping in closed form along its original direction under the punctured projector, and (3) anchors the new fact into the covariance with adaptive scaling so that future edits preserve it. Experiments on two datasets show that P-Space outperforms existing methods while preserving general capabilities.
%U https://aclanthology.org/2026.findings-emnlp.757/
%P 15945-15964
Markdown (Informal)
[P-Space: Puncturing the Null-Space to Resolve Cognitive Dissonance in Continual Knowledge Editing](https://aclanthology.org/2026.findings-emnlp.757/) (Tian et al., Findings 2026)
ACL