1
High-Layer Attention Pruning with Rescaling
大模型剪枝新招：高层注意力剪枝加缩放，推理提速不损精度，论文原文速看。
arXiv:2507.01900v3 Announce Type: replace-cross Abstract: Pruning is a highly effective approach for compressing large language models (LLMs), signifi…
2
Scaling Laws: The Law Behind Every LLM
揭秘大模型性能增长的底层规律，一文读懂 Scaling Laws 如何指导 LLM 训练与架构设计。
Article URL: https://aidoses.substack.com/p/scaling-laws-the-law-behind-every Comments URL: https://news.ycombinator.com/item?id=49111433 Points: 2 # …
3
Where Larger Models Excel: The Primacy of Constraint-Guided Reasoning
揭示大模型优势源于约束引导推理，而非单纯规模扩大，颠覆传统认知。
arXiv:2606.26108v1 Announce Type: new Abstract: Larger language models consistently outperform smaller ones on reasoning benchmarks, yet the reasoning…
4
How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks
6G网络与轻量语言模型结合，探索极小型AI推理极限，通信智能化必读研究。
arXiv:2603.02156v2 Announce Type: replace-cross Abstract: Emerging 6G visions, reflected in ongoing standardization efforts within 3GPP, IETF, ETSI, I…
5
ReM-MoA: Reasoning Memory Sustains Mixture-of-Agents Scaling
提出ReM-MoA机制，用推理记忆解决混合代理规模化难题，提升多智能体协作效率。
arXiv:2606.24437v1 Announce Type: new Abstract: Mixture-of-Agents (MoA) architectures improve inference-time scaling by organizing multiple LLM agents…
6
Inverse Depth Scaling From Most Layers Being Similar
颠覆深度缩放传统认知：层间相似性导致“越深越差”的逆缩放现象，ICML 2026最新发现。
arXiv:2602.05970v2 Announce Type: replace Abstract: Neural scaling laws relate loss to model size in large language models (LLMs), yet depth and width…