不只盯算力，用能源感知蒸馏为代码大模型瘦身，兼顾性能与可持续性。
arXiv:2608.17515v1 Announce Type: cross Abstract: Background: Large Language Models (LLMs) are increasingly being applied to Software Engineering (SE)…
用分布视角重新拆解知识蒸馏，带你理解模型压缩背后的数学本质，适合深度学习进阶读者。
arXiv:2608.15215v1 Announce Type: cross Abstract: Token-level knowledge distillation (KD) matches two conditional distributions per position, yet the …
联邦蒸馏遇上数据分布偏移？域感知代理方案给出稳健新解，跨域协作更可靠。
arXiv:2608.08525v1 Announce Type: new Abstract: Federated Learning is a distributed machine learning paradigm that trains a global model by aggregatin…
新方法让大模型多语言数学推理更强，策略蒸馏带来显著提升，值得一看。
arXiv:2608.05802v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) is emerging as a promising alternative to reinforcement learning for LL…
用离线Top-K logits和融合分块KL损失，大幅降低大模型蒸馏成本，兼顾效率与质量。
arXiv:2608.03796v1 Announce Type: cross Abstract: Small language models are often the only option for deployment under tight latency, cost, and on-pre…
用思维链蒸馏提升表格重排序效果，TabRank创新方法亮相。
arXiv:2607.25182v1 Announce Type: cross Abstract: The ability to retrieve relevant tables for answering questions is a key task for structured informa…
生成式大模型蒸馏奖励模型新方法，解决偏好标注昂贵难题。
arXiv:2601.14032v2 Announce Type: replace Abstract: Reward models (RMs) play a pivotal role in aligning large language models (LLMs) with human prefer…
图半监督蒸馏新方法被KDD2026录用，利用文本属性提升图表示学习效率。
arXiv:2607.20477v1 Announce Type: new Abstract: {\em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph top…
蒸馏强化学习新范式，让LLM后训练更高效、更可控。
arXiv:2607.17247v1 Announce Type: cross Abstract: Large language model (LLM) post-training is essential for improving reasoning, adaptation, and align…
利用强大教师模型终身蒸馏知识，让弱LLM智能体也能高效部署，新方法AgentBrew突破性能瓶颈。
arXiv:2607.16851v1 Announce Type: new Abstract: Deploying LLM agents typically requires a compact test-time student, even if a stronger teacher is ava…
用知识蒸馏技术自动化评估AI导师的教学质量，FATE方法为教育大模型评价提供新思路
arXiv:2607.10647v1 Announce Type: new Abstract: The rapid integration of Large Language Models (LLMs) into K-12 and higher education has outpaced the …
揭露硅谷指控中国公司“蒸馏”AI知识的内幕，一场悄无声息的技术冷战正在开打。
Article URL: https://www.washingtonpost.com/national-security/2026/07/06/why-anthropic-alleges-chinese-firms-are-distilling-knowledge-claude/ Comments…
让前沿AI老师互相竞技，再合作打造可验证课程，帮助学生超越模仿式学习。
arXiv:2607.08255v1 Announce Type: new Abstract: Large language models increasingly serve as teachers generating training data for smaller students. Pr…
用贝叶斯教师指导SGD蒸馏，理论框架与实用指南双管齐下，读懂知识蒸馏新范式。
arXiv:2601.01484v2 Announce Type: replace Abstract: Knowledge Distillation (KD) is a central paradigm for transferring knowledge from a large teacher …
新方法通过证据知识蒸馏让大语言模型高效量化不确定性，无需额外推理开销
arXiv:2507.18366v2 Announce Type: replace Abstract: Accurate uncertainty quantification remains a key challenge for standard LLMs, prompting the adopt…
用大模型语义知识蒸馏到鸟瞰图表示，为自动驾驶感知开辟新路径
arXiv:2603.06576v2 Announce Type: replace-cross Abstract: The integration of Large Language Models (LLMs) into autonomous driving has attracted growin…
针对多模态大语言模型压缩难题，提出Token级响应-视觉注意力引导，提升蒸馏效果
arXiv:2607.02593v1 Announce Type: cross Abstract: While knowledge distillation (KD) is widely adopted for training lightweight models by leveraging su…
颠覆知识蒸馏常规认知：预训练表征只有等价类意义，匹配坐标是伪命题
arXiv:2607.03572v1 Announce Type: cross Abstract: Knowledge distillation is usually framed as a choice of what to match in the teacher - its logits, h…
将知识蒸馏引入混合xLSTM架构，探索高效模型压缩新方向
arXiv:2603.15590v2 Announce Type: replace Abstract: There have been numerous attempts to distill quadratic attention-based large language models (LLMs…
用蒸馏法撬开大模型的隐藏偏好，检测供应链里植入的隐蔽偏见，为AI安全把关。
arXiv:2607.01208v1 Announce Type: cross Abstract: Language models deployed in high-stakes roles can potentially favor certain entities, brands, or vie…