[CL] Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models

[Duke University & Meta]

https://arxiv.org/abs/2506.17585

[LG] In-Context Learning Strategies Emerge Rationally

[Stanford University & Harvard University]

https://arxiv.org/abs/2506.17859

[LG] Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection

[Microsoft]

https://arxiv.org/abs/2506.18145

[LG] The 4th Dimension for Scaling Model Size

[University of Illinois at Urbana-Champaign & University of Toronto]

https://arxiv.org/abs/2506.18233

[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