Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Hongyuan Adam Lu, Z. L., Victor Wei +5 authors
A novel framework for improving large language model performance through textual frequency analysis, including laws, distillation, and curriculum training approaches.
Qwen, An Yang, Baosong Yang +39 authors
Qwen2.5, an enhanced series of large language models, demonstrates superior performance across various benchmarks and use cases through extensive pre-training and advanced post-training techniques.
Hao Liang, Zhengyang Zhao, Meiyi Qiang +22 authors
DataFlex is a unified framework for dynamic data-centric training of large language models that supports sample selection, domain mixture adjustment, and sample reweighting while maintaining compatibility with standard training workflows and enabling efficient large-scale deployment.
Keke Lian, Bin Wang, Lei Zhang +18 authors
A.S.E is a repository-level benchmark for evaluating the security of AI-generated code, highlighting challenges in secure coding and the limitations of LLMs in real-world scenarios.
An Yang, Anfeng Li, Baosong Yang +57 authors
Qwen3, a unified series of large language models, integrates thinking and non-thinking modes, reduces computational resources, and achieves state-of-the-art performance across various tasks and languages.
Chujie Zheng, Shixuan Liu, Mingze Li +9 authors
Group Sequence Policy Optimization (GSPO) is a reinforcement learning algorithm that improves training efficiency and performance of large language models by using sequence-level importance ratios and operations.
Bang Liu, Xinfeng Li, Jiayi Zhang +44 authors
This survey covers the design, evaluation, and improvement of intelligent agents based on modular, brain-inspired architectures, focusing on self-enhancement, multi-agent collaboration, and safety in AI systems.
Zehao Chen, Gongxun Li, Tianxiang Ai +8 authors
WMSS is a post-training paradigm that uses weak model checkpoints to identify and fill learning gaps, enabling continued improvement beyond conventional saturation points in large language models.
Lingrui Mei, Jiayu Yao, Yuyao Ge +12 authors
Context Engineering systematically optimizes information payloads for Large Language Models, addressing gaps in generating sophisticated, long-form outputs.
Loubna Ben Allal, Anton Lozhkov, Elie Bakouch +19 authors
SmolLM2, a small language model with 1.7 billion parameters, achieves strong performance through overtraining on diverse datasets, outperforming other recent small models.
Jingqi Tong, Yurong Mou, Hangcheng Li +11 authors
The "Thinking with Video" paradigm enhances multimodal reasoning by integrating video generation models, demonstrated through the Video Thinking Benchmark and improved performance on both vision and text tasks.
Xuying Ning, Katherine Tieu, Dongqi Fu +39 authors
Large language models are increasingly used as operational substrates for agent reasoning and execution in agentic systems, with code serving as a unified infrastructure layer across multiple domains and applications.
Guobin Shen, Chenxiao Zhao, Xiang Cheng +2 authors
VESPO addresses training instability in LLM reinforcement learning by using variational formulation with variance reduction to correct policy divergence without length normalization.
Yang Wang, Chenghao Xiao, Chia-Yi Hsiao +4 authors
LLMs struggle with understanding the nuanced, context-dependent meanings of Drivelological text, which appears nonsensical but contains deeper semantic layers.
Tianxin Wei, Ting-Wei Li, Zhining Liu +26 authors
Agentic reasoning redefines large language models as autonomous agents capable of planning, acting, and learning through continuous interaction in dynamic environments across single-agent and multi-agent frameworks.
Chenxu Wang, Chaozhuo Li, Songyang Liu +10 authors
Multi-agent LLM systems face fundamental limitations in achieving continuous self-improvement while maintaining safety alignment due to inherent statistical blind spots in isolated evolution.
Kaiyan Zhang, Yuxin Zuo, Bingxiang He +36 authors
Reinforcement Learning enhances Large Language Models for complex reasoning tasks, facing challenges in scalability and infrastructure as the field advances.
Shenzhi Wang, Le Yu, Chang Gao +15 authors
Token entropy patterns are crucial in RLVR, with high-entropy tokens significantly impacting reasoning performance and model optimization.
Wei Zhou, Jun Zhou, Haoyu Wang +16 authors
LLM-enhanced data preparation methods are transforming data-centric workflows from rule-based pipelines to prompt-driven, context-aware approaches, organized into data cleaning, integration, and enrichment tasks.
Yaowei Zheng, Richong Zhang, Junhao Zhang +2 authors
LlamaFactory is a unified framework enabling efficient fine-tuning of large language models across various tasks using a web-based user interface.
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