Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access to external knowledge, but adds retrieval latency, context overhead, and only shallow integration with the backbone. Parametric adaptation is efficient at inference time, but ent...
Ming-Yuan Li, Guang-Sheng Yu, Xu Wang et al.· 3 citations
K-Bench is introduced, a benchmark that scores LLM unlearning under agentic deployment and certifies forgetting by reading the model's final answer, where a model that refuses to answer already counts as having forgotten.
Guang-Sheng Yu, Yan-Na Jiang, Qin Wang et al.· 0 citations
Generalized least squares (GLS) analysis shows that, under exchangeability, the optimal symmetric linear combiner of latent embeddings is uniform, supporting majority vote as the natural default in standard SC while leaving room for weighting or pruning under heterogeneous prompt-template branches.
Guang-Sheng Yu, Litianyi Zhang, Qin Wang et al.· 0 citations
Privacy-sensitive organizations may run large language models (LLMs) in restricted or air-gapped environments while exporting selected diagnostic artifacts. We show that a compromised runtime component can hide sensitive information in intermediate activations that are allowed to leave the restricted environment. An of...
Ming-Yuan Li, Yan-Na Jiang, Guang-Sheng Yu et al.· 0 citations
The results support generated memory as a selective correction to direct retrieval and highlight routing when, which, and how strongly to intervene as the central challenge.
Ming-Yuan Li, Guang-Sheng Yu, Ju-Yuan Zhang et al.· 0 citations
AI-assisted claims can appear authoritative when evidence, analysis, human authorization, presentation, and correction history refer to different states. Provenance, attestation, and transparency expose history but alone do not specify the publication transition examined here. We develop Publication Authority as an exa...
Torsten Tiltack, Yi-Fei Dong, Kun Yu et al.· 0 citations
The results suggest that Engram can serve as a reusable external knowledge artifact, provided that the target has access to a compatible reader interface and target-side adaptation can further improve alignment when direct reader reuse is insufficient.
Mingyuan Li, Guangsheng Yu, Xu Wang et al.· 0 citations
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