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Context Tuning for In-Context Optimization
ICML 2026最新论文，提出上下文调优方法，让大模型在上下文优化中表现更高效。
arXiv:2507.04221v3 Announce Type: replace-cross Abstract: We introduce Context Tuning, a simple and effective method to significantly enhance few-shot…
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In-Context Graphical Inference
新的上下文图推理范式，无需参数更新即可在图中实现快速预测与结构归纳。
arXiv:2606.05042v1 Announce Type: cross Abstract: Marginal inference in discrete graphical models forces a choice between exactness and scalability: e…
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Learning without training: The implicit dynamics of in-context learning
不做梯度更新也能学习？这篇论文揭示了上下文学习背后的隐式动力学机制
arXiv:2507.16003v4 Announce Type: replace Abstract: One of the most striking features of Large Language Models (LLMs) is their ability to learn in-con…
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Toward Robust In-Context Learning: Leveraging Out-of-distribution Proxies for Target Inaccessible Demonstration Retrieval
ACL 2026新研究，用分布外数据代理解决目标不可访问时的上下文示例检索，显著增强大模型ICL鲁棒性。
arXiv:2606.00014v1 Announce Type: cross Abstract: Although studies have demonstrated that Large Language Models (LLMs) can perform well on Out-of-Dist…
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MEMOR-E: In-Context and Fine-Tuned LLM Personalization for Alzheimer's Assistive Robotics
为阿尔茨海默症辅助机器人量身定制，融合上下文学习与微调的LLM个性化方案
arXiv:2605.23941v1 Announce Type: new Abstract: Alzheimer's disease is a neurodegenerative disorder marked by progressive declines in memory and langu…
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Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective
从形式语言学习视角，剖析微调与上下文学习的根本差异，ACL 2026 重磅论文。
arXiv:2604.23267v2 Announce Type: replace-cross Abstract: Large language models (LLMs) operate in two fundamental learning modes - fine-tuning (FT) an…
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ACIL: Auto Chain of Thoughts for In-Context Learning
提出ACIL方法，自动化链式思维推理与上下文学习结合，提升LLM多步推理能力。
arXiv:2605.17088v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have shown that Chain-of-Thought (CoT) reasoning can s…