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Tuning Language Models by Mixture-of-Depths Ensemble
大模型调优新思路：用混合深度集成技术提升语言模型性能，值得关注！
arXiv:2410.13077v2 Announce Type: replace-cross Abstract: Transformer-based Large Language Models (LLMs) traditionally rely on final-layer loss for fi…
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Ensemble Learning for Large Language Models in Text and Code Generation: A Survey
权威综述，系统梳理LLM集成学习在文本与代码生成中的方法、挑战与未来方向。
arXiv:2503.13505v3 Announce Type: replace-cross Abstract: Generative Pretrained Transformers (GPTs) are foundational Large Language Models (LLMs) for …
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Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting
分层因果发现新范式，用LLM动态加权专家模型，提升因果推断准确性与可解释性。
arXiv:2606.10607v1 Announce Type: cross Abstract: Causal discovery aims to uncover causal structures from observational data, which is crucial for rea…
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Self-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM Reasoning
仅用两个推理样本即可实现LLM自一致性？CoT+PoT集成方案，大幅提升推理效率的新突破。
arXiv:2604.17433v2 Announce Type: replace-cross Abstract: Self-consistency (SC) is a popular technique for improving the reasoning accuracy of large l…
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CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery
大模型做因果发现时何时可信？这篇论文提出CauTion框架，动态评估LLM的集成信任度，提升因果推断鲁棒性。
arXiv:2606.03602v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the fundamental limitations of p…
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Consolidating Rewarded Perturbations for LLM Post-Training
把后训练循环从样本空间移到权重空间，用高斯扰动采样+奖励排序集成专家，开辟LLM微调新范式。
arXiv:2605.31494v1 Announce Type: cross Abstract: Post-training of language models is commonly framed as a sample-score-update loop implemented by gra…
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Local MDI+: Local Feature Importances for Tree-Based Models
提出Local MDI+方法，提升树模型局部特征重要性解释的准确性和可靠性
arXiv:2506.08928v2 Announce Type: replace Abstract: Tree-based ensembles such as random forests remain the go-to for tabular data over deep learning m…
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Soft Specialists: $\alpha$-R\'enyi Ensembles for Uncertainty-Aware LLM Post-Training
用α-Rényi集成提升LLM后训练的不确定性建模，学术前沿。
arXiv:2605.27747v1 Announce Type: cross Abstract: Existing training approaches for large language models learn a single set of parameters, based on la…
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Innovative Silicosis and Pneumonia Classification: Leveraging Graph Transformer Post-hoc Modeling and Ensemble Techniques
提出图Transformer后验建模与集成技术，实现矽肺和肺炎的精准分类，并发布新胸部X光数据集SVBCX。
arXiv:2501.00520v2 Announce Type: replace-cross Abstract: This paper presents a comprehensive study on the classification and detection of Silicosis-r…
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ChainLearn: A Blockchain-Based Capacity-Aware Framework for Federated Ensemble Learning
基于区块链的容量感知联邦集成学习框架，专为医学影像中资源不均的医院设计。
arXiv:2605.24418v1 Announce Type: new Abstract: Federated learning is used in medical imaging where privacy prohibits centralizing data. Standard fede…
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FruitEnsemble: MLLM-Guided Arbitration for Heterogeneous ensemble in Fine-Grained Fruit Recognition
将多模态大语言模型引入细粒度水果识别，通过仲裁机制实现异构集成，大幅提升准确率。
arXiv:2605.20892v1 Announce Type: new Abstract: Fine-grained fruit classification is a critical yet challenging task in agricultural computer vision, …
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Beyond Majority Voting: LLM Aggregation by Leveraging Higher-Order Information
顶会ICML2026新研究，利用高阶信息提升LLM聚合效果，超越传统多数投票方法。
arXiv:2510.01499v2 Announce Type: replace Abstract: With the rapid progress of multi-agent large language model (LLM) reasoning, how to effectively ag…
13
Improving Random Forests by Smoothing
通过平滑策略提升随机森林性能，创新方法值得关注
arXiv:2505.06852v2 Announce Type: replace Abstract: Random forest regression is a powerful non-parametric method that adapts to local data characteris…