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。
Zeyu Zhang, Ziliang Guo, Yihang Sun +14 authors
Metis introduces memory foundation models that embed persistent, dynamically evolving native memory states and autonomous storage procedures directly into foundation models via memory attention and gradient-free updates.
Zixuan Huang, Xin Xia, Yuxi Ren +11 authors
Large reasoning models can implicitly determine optimal stopping points for thinking, which SAGE-RL enhances by incorporating efficient reasoning patterns into pass@1 inference for improved accuracy and efficiency.
Yulin Li, Tengyao Tu, Li Ding +5 authors
ReBalance is a training-free framework that balances reasoning in large models by using confidence indicators to detect and correct overthinking and underthinking behaviors through dynamic steering vectors.
Zhiyuan Hu, Yibo Wang, Hanze Dong +5 authors
Explicit alignment of large reasoning models with deduction, induction, and abduction through a three-stage pipeline improves scalability and reliability in reasoning tasks.
Wenhan Liu, Xinyu Ma, Weiwei Sun +4 authors
A reasoning-intensive reranker, ReasonRank, achieves state-of-the-art performance in passage ranking tasks by using synthesized training data and a two-stage post-training approach with reinforcement learning.
Chengpeng Li, Mingfeng Xue, Zhenru Zhang +7 authors
START integrates external tools into large reasoning models to enhance capabilities, using techniques like Hint-infer and Hint Rejection Sampling Fine-Tuning, achieving high performance across various benchmarks.
Xiaoxi Li, Guanting Dong, Jiajie Jin +5 authors
Search-o1 enhances large reasoning models with an agentic retrieval-augmented generation mechanism and a Reason-in-Documents module to improve performance on complex reasoning tasks.
Junyu Zhang, Runpei Dong, Han Wang +8 authors
AlphaOne dynamically modulates reasoning in large models by introducing $\alpha$ moment and Bernoulli process for slow thinking, improving efficiency and capability across diverse domains.
Jianhao Yan, Yafu Li, Zican Hu +5 authors
LUFFY enhances zero-RL models with off-policy guidance, improving reasoning and generalization through balanced imitation and exploration.
Dacheng Li, Shiyi Cao, Chengkun Cao +6 authors
A hybrid test-time scaling framework improves code generation coverage and accuracy across various models and domains.
Zhiqin Yang, Jingwen Fu, Yuhan Liu +16 authors
This work proposes a structured ladder for scaling large reasoning models beyond human supervision by tracing autonomous rewards and self-generated experience, while identifying risks and evaluation dimensions.
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