AI可可AI生活

[LG] HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges [Microsoft Research Asia] https://arxiv.org/abs/2506.15196 在小宇宙查看该单集文稿

[LG] Dense SAE Latents Are Features, Not Bugs [MIT & ETH Zürich] https://arxiv.org/abs/2506.156 在小宇宙查看该单集文稿

[LG] Optimal Embedding Learning Rate in LLMs: The Effect of Vocabulary Size [UC Berkeley & Microsoft Research] https://arxiv.org/abs/2506.15025 在小宇宙查看该单集文稿

[CL] Approximating Language Model Training Data from Weights [Cornell University] https://arxiv.org/abs/2506.155 在小宇宙查看该单集文稿

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”。 在小宇宙查看该单集文稿

[CL] Sampling from Your Language Model One Byte at a Time [University of Washington] https://arxiv.org/abs/2506.14123 在小宇宙查看该单集文稿

[LG] Transformers Learn Faster with Semantic Focus [IBM Research] https://arxiv.org/abs/2506.14095 在小宇宙查看该单集文稿

[CL] From Bytes to Ideas: Language Modeling with Autoregressive U-Nets [FAIR at Meta] https://arxiv.org/abs/2506.14761 在小宇宙查看该单集文稿

[CL] Reasoning with Exploration: An Entropy Perspective [RUC & MSRA & SJTU] https://arxiv.org/abs/2506.14758 在小宇宙查看该单集文稿

[LG] Less is More: Undertraining Experts Improves Model Upcycling [Université de Montréal & Concordia University] https://arxiv.org/abs/2506.14126 在小宇宙查看该单集文稿

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。 在小宇宙查看该单集文稿

[LG] Wanting to Be Understood Explains the Meta-Problem of Consciousness [Google DeepMind] https://arxiv.org/abs/2506.12086 在小宇宙查看该单集文稿

[CL] Multipole Attention for Efficient Long Context Reasoning [UC Berkeley] https://arxiv.org/abs/2506.13059 在小宇宙查看该单集文稿

[CL] Direct Reasoning Optimization：LLMs Can Reward And Refine Their Own Reasoning for Open-Ended Tasks [Microsoft] https://arxiv.org/abs/2506.13351 在小宇宙查看该单集文稿

[LG] Verifying the Verifiers：Unveiling Pitfalls and Potentials in Fact Verifiers [Yonsei University & Stanford University & University of Washington] https://arxiv.org/abs/2506.13342 在小宇宙查看该单集文稿

[CL] Refract ICL：Rethinking Example Selection in the Era of Million-Token Models [Google DeepMind] https://arxiv.org/abs/2506.12346 在小宇宙查看该单集文稿

人生的智慧不在于追求一步到位的完美答案，而在于始终为自己保留更多的选择权和可能性。 在小宇宙查看该单集文稿

[LG] Spectral Estimation with Free Decompression [UC Berkeley & University of Melbourne] https://arxiv.org/abs/2506.11994 在小宇宙查看该单集文稿

[LG] TreeRL: LLM Reinforcement Learning with On-Policy Tree Search [Tsinghua University & California Institute of Technology] https://arxiv.org/abs/2506.11902 在小宇宙查看该单集文稿

[CL] Large Language Models and Emergence: A Complex Systems Perspective [Santa Fe Institute] https://arxiv.org/abs/2506.11135 在小宇宙查看该单集文稿

[LG] Tversky Neural Networks: Psychologically Plausible Deep Learning with Differentiable Tversky Similarity [Stanford University] https://arxiv.org/abs/2506.11035 在小宇宙查看该单集文稿

[CL] You Only Fine-tune Once：Many-Shot In-Context Fine-Tuning for Large Language Model [Google & University of Florida] https://arxiv.org/abs/2506.11103 在小宇宙查看该单集文稿

现代人的焦虑很大程度源于不断的比较。我们习惯用别人的成就来衡量自己，这种"比较"就像快乐的小偷，偷走了本该属于我们的幸福感。 在小宇宙查看该单集文稿

[CL] Draft-based Approximate Inference for LLMs [FuriosaAI & UW-Madison] https://arxiv.org/abs/2506.08373 在小宇宙查看该单集文稿

[LG] On the Similarities of Embeddings in Contrastive Learning [Yonsei University] https://arxiv.org/abs/2506.09781 在小宇宙查看该单集文稿