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20242026
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cs.CL2026

The Model Agreed, But Didn't Learn: Diagnosing Surface Compliance in Large Language Models

Xiaojie Gu, Ziying Huang, Weicong Hong +3

Large Language Models (LLMs) internalize vast world knowledge as parametric memory, yet inevitably inherit the staleness and errors of their source corpora. Consequently, ensuring…

cs.CL2026

Omanic: Towards Step-wise Evaluation of Multi-hop Reasoning in Large Language Models

Xiaojie Gu, Sherry T. Tong, Aosong Feng +8

Evaluating the reasoning abilities of large language models (LLMs) solely from final answers can obscure failures in intermediate steps, especially in multi-hop QA benchmarks witho…

cs.CL2026

Hierarchical Orthogonal Residual Spread for Precise Massive Editing in Large Language Models

Xiaojie Gu, Guangxu Chen, Yuheng Yang +2

Large language models (LLMs) exhibit exceptional performance across various domains, yet they face critical safety concerns. Model editing has emerged as an effective approach to m…

cs.CL2025

UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Language Models

Xiaojie Gu, Ziying Huang, Jia-Chen Gu +1

Lifelong learning enables large language models (LLMs) to adapt to evolving information by continually updating their internal knowledge. An ideal system should support efficient,…

cs.CL2025

CoheMark: A Novel Sentence-Level Watermark for Enhanced Text Quality

Junyan Zhang, Shuliang Liu, Aiwei Liu +4

Watermarking technology is a method used to trace the usage of content generated by large language models. Sentence-level watermarking aids in preserving the semantic integrity wit…