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。
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.
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.
Seokju Cho, Ryo Hachiuma, Abhishek Badki +8 authors
SpatialClaw is a training-free framework that uses code as an action interface to enable flexible, stateful spatial reasoning in vision-language models, achieving superior performance across diverse 3D/4D spatial reasoning tasks.
Weiyun Wang, Zhe Chen, Wenhai Wang +8 authors
A preference optimization process enhances multimodal reasoning, particularly Chain-of-Thought performance, in large language models through automated data construction and mixed preference optimization, demonstrating significant improvements on benchmarks.
Huan-ang Gao, Jiayi Geng, Wenyue Hua +24 authors
This survey reviews self-evolving agents, focusing on mechanisms for adaptation in components like models, memory, and architecture, and discusses challenges in safety, scalability, and co-evolutionary dynamics.
Long Phan, Alice Gatti, Ziwen Han +661 authors
HLE is a challenging multi-modal benchmark that highlights the limitations of current LLMs in closed-ended academic questions.
Run-Ze Fan, Zengzhi Wang, Pengfei Liu
TextbookReasoning and MegaScience datasets, along with a comprehensive evaluation system, enhance scientific reasoning in AI by providing high-quality, verifiable data and outperforming existing models.
Tingchen Fu, Jiawei Gu, Yafu Li +2 authors
An empirical analysis of MathIF identifies a tension between enhancing reasoning capacity and maintaining instruction adherence in large language models.
Chunting Zhou, Lili Yu, Arun Babu +7 authors
Transfusion combines language modeling and diffusion training to develop a multi-modal transformer model that scales effectively across text and image data, matching the performance of specialized models.
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.
NVIDIA, Alisson Azzolini, Hannah Brandon +42 authors
Cosmos-Reason1 models, using hierarchical and two-dimensional ontologies for physical common sense and embodied reasoning, generate embodied decisions through multimodal large language models trained in vision and Physical AI stages.
Zhiyong Wu, Zhenyu Wu, Fangzhi Xu +8 authors
OS-Atlas improves GUI agent performance through a large open-source GUI grounding dataset and model innovations.
Jian Yang, Jiaxi Yang, Ke Jin +7 authors
A human-curated benchmark (CodeArena) and a large synthetic instruction corpus (SynCode-Instruct) are introduced to evaluate code LLMs based on human preference alignment, revealing performance differences between open-source and proprietary models.
Rogerio Bonatti, Dan Zhao, Francesco Bonacci +8 authors
Windows Agent Arena provides a general, reproducible environment for evaluating multi-modal agent performance on Windows OS tasks, with scalability and parallelizability features.
Yupeng Chang, Xu Wang, Jindong Wang +13 authors
A comprehensive review of evaluation methods for large language models (LLMs) focusing on tasks, methods, and future challenges.
Haotian Liu, Chunyuan Li, Yuheng Li +1 authors
Modifications to LLaVA using CLIP-ViT-L-336px and academic VQA data improve multimodal performance, achieving state-of-the-art results with limited training data.
Dawei Li, Bohan Jiang, Liangjie Huang +10 authors
A survey of LLM-based judgment and assessment explores definitions, taxonomy, benchmarks, challenges, and future directions for the LLM-as-a-judge paradigm.
Tianbin Li, Yanzhou Su, Wei Li +15 authors
GMAI-VL, a vision-language model trained on the GMAI-VL-5.5M dataset, achieves top performance in multimodal medical tasks by integrating visual and textual information.
Zheng Cai, Maosong Cao, Haojiong Chen +97 authors
InternLM2 is an open-source LLM that outperforms predecessors through innovative pre-training and optimization techniques, including Supervised Fine-Tuning and Conditional Online Reinforcement Learning from Human Feedback.
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