REPAIR: Robust Editing via Progressive Adaptive Intervention and
  Reintegration
Post-training for large language models (LLMs) is constrained by the high
cost of acquiring new knowledge or correcting errors and by the unintended side
effects that frequently arise from retraining. To address these issues, we
introduce REPAIR (Robust Editing via Progressive Adaptive Intervention and
Reintegration), a lifelong editing framework designed to support precise and
low-cost model updates while preserving non-target knowledge. REPAIR mitigates
the instability and conflicts of large-scale sequential edits through a
closed-loop feedback mechanism coupled with dynamic memory management.
Furthermore, by incorporating frequent knowledge fusion and enforcing strong
locality guards, REPAIR effectively addresses the shortcomings of traditional
distribution-agnostic approaches that often overlook unintended ripple effects.
Our experiments demonstrate that REPAIR boosts editing accuracy by 10%-30%
across multiple model families and significantly reduces knowledge forgetting.
This work introduces a robust framework for developing reliable, scalable, and
continually evolving LLMs.
68e322f873e20ab577841fc6
Yisu Wang
68e322f873e20ab577841fc7
Ming Wang
claimed_verified
2025-10-09T06:52:06.344Z
644378a96cea0db46dc96b39
https://cdn-avatars.huggingface.co/v1/production/uploads/644378a96cea0db46dc96b39/wjGNaXjfhSdWr8kM6Amai.jpeg
Ming Wang
user
sci-m-wang
68e322f873e20ab577841fc8
Haoyuan Song
68e322f873e20ab577841fc9
Wenjie Huang
68e322f873e20ab577841fca
Chaozheng Wang
68e322f873e20ab577841fcb
Yi Xie
68e322f873e20ab577841fcc
Xuming Ran
REPAIR: Robust Editing via Progressive Adaptive Intervention and
  Reintegration
Post-training for large language models (LLMs) is constrained by the high
cost of acquiring new knowledge or correcting errors and by the unintended side
effects that frequently arise from retraining. To address these issues, we
introduce REPAIR (Robust Editing via Progressive Adaptive Intervention and
Reintegration), a lifelong editing framework designed to support precise and
low-cost model updates while preserving non-target knowledge. REPAIR mitigates
the instability and conflicts of large-scale sequential edits through a
closed-loop feedback mechanism coupled with dynamic memory management.
Furthermore, by incorporating frequent knowledge fusion and enforcing strong
locality guards, REPAIR effectively addresses the shortcomings of traditional
distribution-agnostic approaches that often overlook unintended ripple effects.
Our experiments demonstrate that REPAIR boosts editing accuracy by 10%-30%
across multiple model families and significantly reduces knowledge forgetting.
This work introduces a robust framework for developing reliable, scalable, and
continually evolving LLMs.
REPAIR is a lifelong editing framework for large language models that enhances editing accuracy and reduces knowledge forgetting through progressive adaptive intervention and reintegration.
REPAIR Robust Editing Progressive Adaptive Intervention Reintegration lifelong editing framework large language models closed-loop feedback mechanism dynamic memory management knowledge fusion locality guards knowledge forgetting