[CL] Multipole Attention for Efficient Long Context Reasoning

[UC Berkeley]

https://arxiv.org/abs/2506.13059

[CL] Direct Reasoning Optimization：LLMs Can Reward And Refine Their Own Reasoning for Open-Ended Tasks

[Microsoft]

https://arxiv.org/abs/2506.13351

[LG] Verifying the Verifiers：Unveiling Pitfalls and Potentials in Fact Verifiers

[Yonsei University & Stanford University & University of Washington]

https://arxiv.org/abs/2506.13342

[CL] Refract ICL：Rethinking Example Selection in the Era of Million-Token Models

[Google DeepMind]

https://arxiv.org/abs/2506.12346

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。

[LG] Spectral Estimation with Free Decompression

[UC Berkeley & University of Melbourne]

https://arxiv.org/abs/2506.11994

[LG] TreeRL: LLM Reinforcement Learning with On-Policy Tree Search

[Tsinghua University & California Institute of Technology]

https://arxiv.org/abs/2506.11902

[CL] Large Language Models and Emergence: A Complex Systems Perspective

[Santa Fe Institute]

https://arxiv.org/abs/2506.11135

[LG] Tversky Neural Networks: Psychologically Plausible Deep Learning with Differentiable Tversky Similarity

[Stanford University]

https://arxiv.org/abs/2506.11035

[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