[LG] These are Not All the Features You are Looking For: A Fundamental Bottleneck In Supervised Pretraining [Facebook AI Research (FAIR) at Meta & New York University] https://arxiv.org/abs/2506.18221

当你给出“新答案”的瞬间，你会发现，那道反复折磨你的题，就这么烟消云散了。

[LG] Robust Reward Modeling via Causal Rubrics [Google DeepMind] https://arxiv.org/abs/2506.16507

[LG] Latent Concept Disentanglement in Transformer-based Language Models [Purdue University & University of Southern California] https://arxiv.org/abs/2506.16975

[CL] When Does Divide and Conquer Work for Long Context LLM? A Noise Decomposition Framework [University of Chicago & Together AI] https://arxiv.org/abs/2506.16411

[LG] On the Theoretical Understanding of Identifiable Sparse Autoencoders and Beyond [Peking University & MIT] https://arxiv.org/abs/2506.15963

[CL] EvoLM: In Search of Lost Language Model Training Dynamics [Harvard & Stanford & EPFL] https://arxiv.org/abs/2506.16029

真正的自由，是从不再解释开始。当你不再需要向世界证明什么时，你才真正开始拥有了自己的人生。

[CV] Align Your Flow: Scaling Continuous-Time Flow Map Distillation [NVIDIA] https://arxiv.org/abs/2506.14603

[LG] Taming Polysemanticity in LLMs: Provable Feature Recovery via Sparse Autoencoders [Yale University & Shanghai Jiao Tong University] https://arxiv.org/abs/2506.14002

[LG] Flat Channels to Infinity in Neural Loss Landscapes [EPFL & Flatiron Institute] https://arxiv.org/abs/2506.14951

[LG] GrokAlign: Geometric Characterisation and Acceleration of Grokking [Rice University & Brown University] https://arxiv.org/abs/2506.12284