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。
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.
Inclusion AI, Tiwei Bie, Haoxing Chen +15 authors
LLaDA2.0-Uni is a unified discrete diffusion language model that integrates multimodal understanding and generation through a semantic discrete tokenizer, MoE-based backbone, and diffusion decoder, achieving performance comparable to specialized vision-language models while enabling efficient inference and high-fidelity image generation.
Guibin Zhang, Hejia Geng, Xiaohang Yu +22 authors
Agentic reinforcement learning transforms large language models into autonomous decision-making agents by leveraging temporally extended POMDPs, enhancing capabilities like planning and reasoning through reinforcement learning.
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.
Caixin Kang, Tianyu Yan, Sitong Gong +8 authors
Researchers introduce a new task and dataset for evaluating personality reasoning in multimodal language models, revealing significant gaps between accurate predictions and grounded reasoning processes.
Xinlei Yu, Zhangquan Chen, Yongbo He +34 authors
Latent space is emerging as a fundamental computational substrate for language-based models, offering advantages over explicit token-level approaches through continuous representation that mitigates linguistic redundancy and sequential inefficiency.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey +530 authors
Llama 3, a multilingual and multi-modal language model with 405B parameters, achieves competitive performance across tasks including image, video, and speech recognition when integrated through a compositional approach.
Yifan Jiang, Yueying Wang, Rui Zhao +4 authors
VideoP2R, a process-aware reinforcement fine-tuning framework, improves video reasoning and understanding by modeling perception and reasoning separately, achieving state-of-the-art results on multiple benchmarks.
Artidoro Pagnoni, Ram Pasunuru, Pedro Rodriguez +11 authors
A Byte Latent Transformer (BLT) matches tokenization-based LLM performance at scale with improved inference efficiency and robustness, using entropy-based byte patching.
Junying Chen, Zhenyang Cai, Ke Ji +5 authors
The proposed approach enhances medical reasoning in large language models using verifiable problems, guided search, and reinforcement learning to achieve superior performance in medical problem-solving.
Linhao Yu, Tianmeng Yang, Siyu Ding +8 authors
KnowRL is a knowledge-guided reinforcement learning framework that improves reasoning in language models by optimizing compact, interaction-aware guidance subsets through constrained subset search and addressing pruning interaction paradoxes.
Eric Zelikman, Georges Harik, Yijia Shao +3 authors
Quiet-STaR is an extension of the Self-Taught Reasoner that teaches language models to infer and generate rationales for each token, improving their ability to predict difficult tokens and answer questions without task-specific fine-tuning.
Akari Asai, Zeqiu Wu, Yizhong Wang +2 authors
Self-Reflective Retrieval-Augmented Generation (Self-RAG) enhances large language models by adaptively retrieving and reflecting on passages, improving factuality and performance across various tasks.
Liqiang Jing, Zhehui Huang, Xiaoyang Wang +6 authors
DSBench evaluates large language and vision-language models on realistic data science tasks, revealing significant performance gaps that indicate the need for improved autonomous agents.
Guilong Lu, Xuntao Guo, Rongjunchen Zhang +2 authors
BizFinBench is a benchmark for evaluating large language models in financial applications, revealing distinct performance patterns across various tasks.
Zhenting Wang, Qi Chang, Hemani Patel +8 authors
MCP-Bench evaluates large language models on complex, multi-step tasks requiring tool use, coordination, and planning across various domains.
Ryan Teknium, Jeffrey Quesnelle, Chen Guang
Hermes 3, a neutrally-aligned instruct and tool use model with strong reasoning and creative capabilities, achieves top performance on public benchmarks.
DeepSeek-AI, Xiao Bi, Deli Chen +84 authors
DeepSeek LLM, an open-source language model project, develops a large dataset and employs SFT and DPO to achieve performance surpassing LLaMA-2 70B and GPT-3.5 in various benchmarks and open-ended evaluations.
Chengyue Wu, Yukang Gan, Yixiao Ge +5 authors
A new post-pretraining method using expanded Transformer blocks for Large Language Models improves knowledge without catastrophic forgetting, yielding LLaMA Pro-8.3B that excels in general tasks, programming, and mathematics.
Xu Huang, Wenhao Zhu, Hanxu Hu +4 authors
BenchMAX evaluates multilingual large language models across advanced capabilities like instruction following, reasoning, and code generation, revealing language-specific performance gaps.
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