元学习+LoRA让大模型快速适应跨域偏好，个性化调校从此更聪明高效。
arXiv:2608.12389v1 Announce Type: new Abstract: Cross-domain zero- or few-shot personalization aims to generate user-preferred responses in unseen con…
元学习攻克多语言LLM对齐难题，让偏好学习适应不同语言文化
arXiv:2607.13315v1 Announce Type: new Abstract: Unequal availability of human preference data across languages poses a significant challenge for align…
一作质问持续学习何时真正需要「学习」，揭示任务无关场景下模型无需更新即可泛化，引发对学习本质的再思考。
arXiv:2607.07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable mo…
突破元学习局限，提出通用算法隐式学习新框架，重新定义“通用”概念。
arXiv:2602.14761v2 Announce Type: replace-cross Abstract: Current meta-learning methods are constrained to narrow task distributions with fixed featur…
不用标注数据就能生成训练任务，这套元学习新方法让模型学会举一反三更省力。
arXiv:2607.02850v1 Announce Type: new Abstract: Meta-learning without labeled data is crucial for real-world applications, where obtaining labeled dat…
人机协作遇上生成式元学习，新模型与算法带你探索AI与人类协同的边界。
arXiv:2607.00926v1 Announce Type: cross Abstract: Generalizing machine learning models to environments that differ from their training distribution re…
用大模型元学习生态先验，揭示人类学习与决策的内在规律，为AI与认知科学架起新桥梁。
arXiv:2509.00116v3 Announce Type: replace-cross Abstract: Human cognition is profoundly shaped by the environments in which it unfolds. Yet, it remain…
用元学习隐式平衡成本与性能，自动选择最佳LLM，降本增效新范式。
arXiv:2606.06178v1 Announce Type: cross Abstract: Large language models (LLMs) present a trade-off between performance and cost, where more powerful m…
AI Agents不再止于单次交互，探索在实地实验中持续学习的潜力与方法。
arXiv:2606.02458v1 Announce Type: new Abstract: Organizations routinely run experiments for A/B testing, yet the data generated from one experiment is…
将信任区域优化与元学习思想巧妙融合，提出持续学习新范式，在防止灾难性遗忘的同时实现高效知识迁移，实验数据丰富。
arXiv:2602.02417v2 Announce Type: replace Abstract: Continual learning aims to acquire tasks sequentially without catastrophic forgetting, yet standar…
MetaSICL提出元语音上下文学习方法，让听觉大模型更灵活适配新任务，无需大量微调数据。
arXiv:2601.18904v2 Announce Type: replace-cross Abstract: Auditory Large Language Models (LLMs) have demonstrated strong performance across a wide ran…
提出自进化元认知策略优化方法，让LLM红队测试更智能高效地发现安全漏洞。
arXiv:2605.10067v3 Announce Type: replace-cross Abstract: Red teaming is critical for uncovering vulnerabilities in Large Language Models (LLMs). Whil…
元学习在多语言口语单词分类中优于监督学习，新方法初探语言无关性
arXiv:2605.13084v2 Announce Type: replace-cross Abstract: Meta-learning has been shown to have better performance than supervised learning for few-sho…
OpenAI提出简单高效的元学习算法Reptile，通过重复采样任务和SGD更新，实现快速适应新任务
We’ve developed a simple meta-learning algorithm called Reptile which works by repeatedly sampling a task, performing stochastic gradient descent on i…
OpenAI 揭秘一阶元学习算法，深入分析 MAML 梯度近似的理论基础与优化技巧
用元学习+贝叶斯推断搞定复杂系统根因分析，无需完整因果图即可高效定位故障。
arXiv:2605.08786v2 Announce Type: replace Abstract: Root cause analysis (RCA) in complex systems is challenging due to error propagation across multip…