[LG] Text-to-LoRA: Instant Transformer Adaption [Sakana AI] https://arxiv.org/abs/2506.06105

[LG] On the Closed-Form of Flow Matching: Generalization Does Not Arise from Target Stochasticity [CNRS] https://arxiv.org/abs/2506.03719

[CL] Don't Pay Attention [Avey AI] https://arxiv.org/abs/2506.11305

真正的知识管理，不是收藏，而是创造。

[LG] AutoRule: Reasoning Chain-of-thought Extracted Rule-based Rewards Improve Preference Learning [CMU] https://arxiv.org/abs/2506.15651

[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