[LG] HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges [Microsoft Research Asia] https://arxiv.org/abs/2506.15196

[LG] Dense SAE Latents Are Features, Not Bugs [MIT & ETH Zürich] https://arxiv.org/abs/2506.156

[LG] Optimal Embedding Learning Rate in LLMs: The Effect of Vocabulary Size [UC Berkeley & Microsoft Research] https://arxiv.org/abs/2506.15025

[CL] Approximating Language Model Training Data from Weights [Cornell University] https://arxiv.org/abs/2506.155

不被外界的噪音裹挟，不被内心的惯性驱动，清清楚楚地知道自己要什么，不要什么，然后全力以赴，成为一个清醒的、主动的“人生选择者”。

[CL] Sampling from Your Language Model One Byte at a Time [University of Washington] https://arxiv.org/abs/2506.14123

[LG] Transformers Learn Faster with Semantic Focus [IBM Research] https://arxiv.org/abs/2506.14095

[CL] From Bytes to Ideas: Language Modeling with Autoregressive U-Nets [FAIR at Meta] https://arxiv.org/abs/2506.14761

[CL] Reasoning with Exploration: An Entropy Perspective [RUC & MSRA & SJTU] https://arxiv.org/abs/2506.14758

[LG] Less is More: Undertraining Experts Improves Model Upcycling [Université de Montréal & Concordia University] https://arxiv.org/abs/2506.14126

工作量会像气体一样膨胀填满给定的时间容器，因此主动设定紧迫的截止日期能激发最高效率，让你成为时间的主人。

[LG] Wanting to Be Understood Explains the Meta-Problem of Consciousness [Google DeepMind] https://arxiv.org/abs/2506.12086