1 citations · 1 across the 6 of their papers we have counts for
7 papers
Mitigating Capability Degradation in LLM Self-Improvement via Representation Geometry Regularization
Jiaqi Tian, Ting Long
On-Policy Self-distillation is a self-improvement paradigm that enables large language models (LLMs) to improve their capabilities by leveraging the same model as both teacher and…
Ruyi2.5 Technical Report
Huan Song, Shuyu Tian, Qingfei Zhao +5
We present Ruyi2.5, a multimodal familial model built on the AI Flow framework. Extending Ruyi2's "Train Once, Deploy Many" paradigm to the multimodal domain, Ruyi2.5 constructs a…
Privacy-Aware Camera 2.0 Technical Report
Huan Song, Shuyu Tian, Ting Long +5
With the increasing deployment of intelligent sensing technologies in highly sensitive environments such as restrooms and locker rooms, visual surveillance systems face a profound…
Theoretical Foundations of Scaling Law in Familial Models
Huan Song, Qingfei Zhao, Ting Long +4
Neural scaling laws have become foundational for optimizing large language model (LLM) training, yet they typically assume a single dense model output. This limitation effectively…
Constructing a Question-Answering Simulator through the Distillation of LLMs
Haipeng Liu, Ting Long, Jing Fu
The question-answering (QA) simulator is a model that mimics real student learning behaviors and predicts their correctness of their responses to questions. QA simulators enable ed…
Personalized Education with Ranking Alignment Recommendation
Haipeng Liu, Yuxuan Liu, Ting Long
Personalized question recommendation aims to guide individual students through questions to enhance their mastery of learning targets. Most previous methods model this task as a Ma…
HierLLM: Hierarchical Large Language Model for Question Recommendation
Yuxuan Liu, Haipeng Liu, Ting Long
Question recommendation is a task that sequentially recommends questions for students to enhance their learning efficiency. That is, given the learning history and learning target…