1
Enhancing LLM Training via Spectral Clipping
提出频谱裁剪技术，从频域视角优化大模型训练稳定性与效率，实验效果显著。
arXiv:2603.14315v2 Announce Type: replace Abstract: While spectral-based optimizers like Muon operate directly on the spectrum of updates, standard ad…
2
Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting
突破性后训练优化方案，让多模态时间序列预测表现更精准、更鲁棒。
arXiv:2605.29401v1 Announce Type: new Abstract: Time-Series Foundation Models (TSFMs) excel at zero-shot unimodal forecasting using numerical data, bu…
3
Learning to Reason Efficiently with A* Post-Training
用A*搜索算法进行模型后训练，提升大模型推理效率，是AI优化新思路。
arXiv:2605.24597v1 Announce Type: new Abstract: Many applications of large language models (LLMs) require deductive reasoning, yet models frequently p…
4
Prompt Segmentation and Annotation Optimisation: Controlling LLM Behaviour via Optimised Segment-Level Annotations
论文提出通过分段级注释优化提示工程，高效控制大模型行为，显著降低计算开销与搜索空间。
arXiv:2605.14561v1 Announce Type: new Abstract: Prompt engineering is crucial for effective interaction with generative artificial intelligence system…