探究大语言模型嵌入空间能否复现专家知识结构，为理解模型内部表示提供前沿视角
arXiv:2606.23394v1 Announce Type: new Abstract: Pretrained text embeddings are increasingly used as representational maps, yet high category separabil…
分阶段训练课程提升排序与分配任务的鲁棒性，构建渐进式表征学习策略。
arXiv:2606.09891v1 Announce Type: cross Abstract: Ranking in digital marketplaces is a dynamic exposure-allocation mechanism: displayed items shape di…
上下文如何重塑LLM中的真理表征？前沿研究揭示几何级语义重构机制。
arXiv:2601.06599v2 Announce Type: replace Abstract: Large Language Models (LLMs) often encode whether a statement is true as a vector in their residua…
提出从网络数据中学习因果表征的新方法，结合图结构与因果关系建模，为复杂网络分析提供全新视角。
arXiv:2509.01916v2 Announce Type: replace Abstract: Causal disentanglement from soft interventions is identifiable under the assumptions of linear int…
用大语言模型生成提示词来解释写作风格表征，破解黑箱模型的可解释性难题
arXiv:2606.05716v1 Announce Type: new Abstract: Style representation learning is a powerful tool for authorship analysis and modeling writing style, y…
分子文本表示如何影响大语言模型性能？这项实证研究揭示关键发现，为化学与AI交叉领域提供新思路。
arXiv:2606.03057v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which…
探索VAE隐空间如何被神经损失函数塑造，揭示潜变量表征的新视角
arXiv:2606.00635v1 Announce Type: new Abstract: Modern VAEs are rarely trained with the pointwise likelihood implied by the standard $\beta$-VAE objec…
从谱理论视角揭示大模型线性表示的形成机制，为理解LLM内部表征提供严谨数学框架。
arXiv:2506.08543v3 Announce Type: replace Abstract: High-level representations have become a central focus in enhancing AI transparency and control, s…
LLM如何构建强大表征，实现小样本高效监督学习？这篇论文给出了系统性答案。
arXiv:2603.11679v3 Announce Type: replace Abstract: As real-world datasets become more complex and heterogeneous, supervised learning is often bottlen…
揭秘潜动作模型失败根源，从无标签视频中学动作表示的突破性改进方案
arXiv:2605.20223v1 Announce Type: new Abstract: Latent action models (LAMs) aim to learn action-like representations from unlabeled videos by compress…
深度探究特征学习如何动态重塑神经网络函数空间的理论前沿成果，59页长文，理论研究者必读。
arXiv:2605.17718v1 Announce Type: cross Abstract: Feature learning is widely regarded as the key mechanism distinguishing neural networks from fixed-k…
用形状分析揭示数据增强如何重塑神经网络内部表征几何，为提升泛化能力提供新视角。
arXiv:2605.15306v1 Announce Type: new Abstract: Data augmentation is widely recognized for improving generalization in deep networks, yet its impact o…