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Diffract: Spectral View of LLM Domain Adaptation
用谱视角剖析大模型领域适应，Diffract方法为持续预训练提供新洞察。
arXiv:2608.10850v1 Announce Type: new Abstract: We study continual pre-training (CPT) as a mechanism for adapting general-purpose large language model…
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Continual Video-MLLM Adaptation over Evolving Domains
视频多模态大模型如何应对连续演化的新领域？该论文探索了模型在异构域数据流中持续学习的难题。
arXiv:2607.18716v1 Announce Type: new Abstract: Video multimodal large language models have shown strong capability in video understanding, yet their …
3
LFM: Leveraging Foundation Models for Source-Free Universal Domain Adaptation
利用基础模型的强泛化能力，无需源数据即可高效适应目标域，摆脱传统调阈值和聚类等繁琐操作。
arXiv:2607.17653v1 Announce Type: cross Abstract: Source-free universal domain adaptation (SF-UniDA) adapts a pre-trained source model to an unlabeled…
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CANDI: Contextual Alignment for Niche Domains Question Answering
针对小众领域问答的上下文对齐新方法，提升大模型在专业场景下的精准回答能力。
arXiv:2607.11891v1 Announce Type: new Abstract: The deployment of large language models (LLMs) in specialized domains like medical diagnostics and fin…
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Bridging Data Gaps in Structural Fragility Modeling through Transfer Learning: Methodology and Case Studies
迁移学习解决结构脆弱性建模中数据稀缺难题，方法首创且案例充分。
arXiv:2606.18567v1 Announce Type: cross Abstract: This paper presents a methodology-centered transfer learning framework for fragility adaptation unde…
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Evaluating Hallucinations in Domain-Adapted Large Language Models
新研究如何评估领域适应大模型中的幻觉问题，包含13页详细实验与分析。
arXiv:2606.07521v1 Announce Type: new Abstract: This study investigates the phenomenon of hallucinations in domain-adapted Large Language Models (LLMs…
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Auditing Training Data in Domain-adapted LLMs: LoRA-MINT
提出LoRA-MINT方法，专为审计领域适应型LLM的训练数据，有效追踪数据来源与隐私风险。
arXiv:2606.06946v1 Announce Type: cross Abstract: We present LoRA-MINT, a new methodology for Membership Inference Test (MINT) applied to recent Large…
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Mining Useful General Data for Low-Resource Domain Adaptation
利用通用数据解决低资源领域大模型适配难题，为领域迁移提供新思路
arXiv:2511.07380v2 Announce Type: replace Abstract: Adapting large language models (LLMs) to low-resource domains remains challenging due to the scarc…
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Domain Adaptation with a Single Vision-Language Embedding
利用单一视觉语言嵌入实现高效域适应，方法简洁且效果显著。
arXiv:2410.21361v2 Announce Type: replace-cross Abstract: Domain adaptation has been extensively investigated in computer vision but still requires ac…