@inproceedings{wu-etal-2026-reason,
    title = "Reason-{KE}++: Aligning the Process, Not Just the Outcome, for Faithful {LLM} Knowledge Editing",
    author = "Wu, Yuchen  and
      Ding, Liang  and
      Shen, Li  and
      Tao, Dacheng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1137/",
    pages = "22639--22655",
    ISBN = "979-8-89176-395-1",
    abstract = "Aligning Large Language Models (LLMs) to be faithful to new knowledge in complex, multi-hop reasoning tasks is a critical, yet unsolved, challenge. We find that SFT-based methods, e.g., Reason-KE, while state-of-the-art, suffer from a ``faithfulness gap'': they optimize for format mimicry rather than sound reasoning. This gap enables the LLM{'}s powerful parametric priors to override new contextual facts, resulting in critical factual hallucinations (e.g., incorrectly reasoning ``Houston'' from ``NASA'' despite an explicit edit). To solve this core LLM alignment problem, we propose **Reason-KE++**, an SFT+RL framework that instills process-level faithfulness. Its core is a Stage-aware Reward mechanism that provides dense supervision for intermediate reasoning steps (e.g., Decomposition, Sub-answer Correctness). Crucially, we identify that naive outcome-only RL is a deceptive trap for LLM alignment: it collapses reasoning integrity (e.g., 19.00{\%} Hop acc) while superficially boosting final accuracy. Our process-aware framework sets **a new SOTA of 95.48{\%}** on MQUAKE-CF-3k (+5.28{\%}), demonstrating that for complex tasks, aligning the reasoning process is essential for building trustworthy LLMs."
}