7 citations · 7 across the 6 of their papers we have counts for
10 papers
Shuffle the Context: RoPE-Perturbed Self-Distillation for Long-Context Adaptation
Zichong Li, Chen Liang, Liliang Ren +3
Large language models (LLMs) increasingly operate in settings that require reliable long-context understanding, such as retrieval-augmented generation and multi-document reasoning.…
ThetaEvolve: Test-time Learning on Open Problems
Yiping Wang, Shao-Rong Su, Zhiyuan Zeng +13
Recent advances in large language models (LLMs) have enabled breakthroughs in mathematical discovery, exemplified by AlphaEvolve, a closed-source system that evolves programs to im…
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
SAS: Simulated Attention Score
Chuanyang Zheng, Jiankai Sun, Yihang Gao +12
The attention mechanism is a core component of the Transformer architecture. Various methods have been developed to compute attention scores, including multi-head attention (MHA),…
Decoder-Hybrid-Decoder Architecture for Efficient Reasoning with Long Generation
Liliang Ren, Congcong Chen, Haoran Xu +11
Recent advances in language modeling have demonstrated the effectiveness of State Space Models (SSMs) for efficient sequence modeling. While hybrid architectures such as Samba and…
Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection
Zheng Zhan, Liliang Ren, Shuohang Wang +5
Linear State Space Models (SSMs) offer remarkable performance gains in efficient sequence modeling, with constant inference-time computation and memory complexity. Recent advances,…
PaTH Attention: Position Encoding via Accumulating Householder Transformations
Songlin Yang, Yikang Shen, Kaiyue Wen +5
The attention mechanism is a core primitive in modern large language models (LLMs) and AI more broadly. Since attention by itself is permutation-invariant, position encoding is ess…
Phi-4-Mini-Reasoning: Exploring the Limits of Small Reasoning Language Models in Math
Haoran Xu, Baolin Peng, Hany Awadalla +11
Chain-of-Thought (CoT) significantly enhances formal reasoning capabilities in Large Language Models (LLMs) by training them to explicitly generate intermediate reasoning steps. Wh…
Reinforcement Learning for Reasoning in Large Language Models with One Training Example
Yiping Wang, Qing Yang, Zhiyuan Zeng +11
We show that reinforcement learning with verifiable reward using one training example (1-shot RLVR) is effective in incentivizing the math reasoning capabilities of large language…
Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs
Microsoft, :, Abdelrahman Abouelenin +73
We introduce Phi-4-Mini and Phi-4-Multimodal, compact yet highly capable language and multimodal models. Phi-4-Mini is a 3.8-billion-parameter language model trained on high-qualit…