用测试增强学习替代海量语料，为语言模型持续预训练省下高额计算成本，稳准快。
arXiv:2608.11788v1 Announce Type: cross Abstract: Continual pre-training (CPT) has been widely adopted as a method for domain adaptation in large lang…
零样本适应新领域，LELA让大模型做实体链接又快又准，学术干货值得一读
arXiv:2601.05192v2 Announce Type: replace Abstract: Entity linking (mapping ambiguous mentions in text to entities in a knowledge base) is a foundatio…
冻结LLM代理如何通过控制系统与数据集学习特定领域？这篇论文给出了系统方案。
arXiv:2607.25415v1 Announce Type: new Abstract: Production LLM agents are increasingly assembled from a frozen model wrapped in a harness: a prompt te…
多步图RAG引擎RAGU，用紧凑领域自适应LLM搞定复杂推理
arXiv:2607.11683v1 Announce Type: cross Abstract: Graph retrieval-augmented generation (GraphRAG) enhances large language models with structured knowl…
面向社科人文学科，巧用知识图谱与多语言语料让大模型精准适配领域需求。
arXiv:2607.05956v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into scientific research workflows, particularly for b…
少数据也能训出强安全大模型，这项研究用最少Token实现领域自适应预训练，大幅降低算力门槛。
arXiv:2507.02964v2 Announce Type: replace-cross Abstract: The increasing scale of AI workloads demands High-Performance Computing (HPC) infrastructure…
针对跨域计数中密度组成变化导致模型退化问题，提出条件特征对齐方法，避免全局域不变性带来的负面影响
arXiv:2506.17137v3 Announce Type: replace Abstract: Object counting models often degrade under cross-domain deployment because density composition var…
ICML 2026 接收！提出 RoCA 方法，显著提升端到端自动驾驶在不同场景下的鲁棒跨域适应能力。
arXiv:2506.10145v3 Announce Type: replace Abstract: End-to-end (E2E) autonomous driving has recently emerged as a new paradigm, offering significant p…
利用未标注目标数据与源域监督，实现稳健的跨域泛化，突破领域差异瓶颈。
arXiv:2605.29122v1 Announce Type: new Abstract: It is often desirable to generalize medical imaging AI models trained with dense annotations to data a…
领域自适应ASR的上下文竟成隐私漏洞？新研究揭示“帮助”背后的泄露风险。
arXiv:2605.28211v1 Announce Type: new Abstract: SpeechLLMs are increasingly deployed in professional settings where domain customisation is standard p…
LLM驱动的实体链接新框架，零样本迁移到新领域无需额外训练数据。
arXiv:2605.26956v1 Announce Type: new Abstract: Entity linking is a key component of many downstream NLP systems, yet existing approaches are often ti…
用细粒度事实核查与领域自适应技术，为医疗大模型幻觉问题提供系统解决方案
arXiv:2512.16189v3 Announce Type: replace Abstract: In healthcare, it is essential for any LLM-generated output to be reliable and accurate, particula…
用密集奖励实现领域自适应强化学习，突破代码生成边界
arXiv:2605.21180v1 Announce Type: new Abstract: Large language models show strong potential for automated code generation, but lack guarantees for cor…
如何让大模型理解高性能计算？这篇论文用领域自适应和RAG打造HPC专属LLM，实用又前沿。
arXiv:2605.16347v1 Announce Type: new Abstract: Modern scientific research increasingly depends on High-Performance Computing (HPC) infrastructures, y…