11 citations · 15 across the 12 of their papers we have counts for
13 papers
CHILL-Harness: Counterfactual Harness Learning for Efficient Reasoning in Long-Horizon Agents
Jiarun Fu, Lizhong Ding, Sida Chen +6
Agent harnesses have become the operational infrastructure of modern large language model agents, coordinating context, tools, verification, and execution control to translate late…
StreamTGN: A GPU-Efficient Serving System for Streaming Temporal Graph Neural Networks
Lingling Zhang, Pengpeng Qiao, Zhiwei Zhang +2
Temporal Graph Neural Networks (TGNs) achieve state-of-the-art performance on dynamic graph tasks, yet existing systems focus exclusively on accelerating training -- at inference t…
Astro: Activation-guided Structured Regularization for Outlier-Robust LLM Post-Training Quantization
Xi Chen, Ming Li, Junxi Li +5
Weight-only post-training quantization (PTQ) is crucial for efficient Large Language Model (LLM) deployment but suffers from accuracy degradation caused by weight and activation ou…
Graph-Based Feature Augmentation for Predictive Tasks on Relational Datasets
Lianpeng Qiao, Ziqi Cao, Kaiyu Feng +2
Data has become a foundational asset driving innovation across domains such as finance, healthcare, and e-commerce. In these areas, predictive modeling over relational tables is co…
DeepFaith: A Domain-Free and Model-Agnostic Unified Framework for Highly Faithful Explanations
Yuhan Guo, Lizhong Ding, Shihan Jia +6
Explainable AI (XAI) builds trust in complex systems through model attribution methods that reveal the decision rationale. However, due to the absence of a unified optimal explanat…
ITPNet: Towards Instantaneous Trajectory Prediction for Autonomous Driving
Rongqing Li, Changsheng Li, Yuhang Li +5
Trajectory prediction of agents is crucial for the safety of autonomous vehicles, whereas previous approaches usually rely on sufficiently long-observed trajectory to predict the f…
DREAM: Domain-agnostic Reverse Engineering Attributes of Black-box Model
Rongqing Li, Jiaqi Yu, Changsheng Li +3
Deep learning models are usually black boxes when deployed on machine learning platforms. Prior works have shown that the attributes (e.g., the number of convolutional layers) of a…
Multivariate Time Series Cleaning under Speed Constraints
Aoqian Zhang, Zexue Wu, Yifeng Gong +2
Errors are common in time series due to unreliable sensor measurements. Existing methods focus on univariate data but do not utilize the correlation between dimensions. Cleaning ea…
Fira: Can We Achieve Full-rank Training of LLMs Under Low-rank Constraint?
Xi Chen, Kaituo Feng, Changsheng Li +4
Low-rank training has emerged as a promising approach for reducing memory usage in training Large Language Models (LLMs). Previous methods either rely on decomposing weight matrice…
Keypoint-based Progressive Chain-of-Thought Distillation for LLMs
Kaituo Feng, Changsheng Li, Xiaolu Zhang +3
Chain-of-thought distillation is a powerful technique for transferring reasoning abilities from large language models (LLMs) to smaller student models. Previous methods typically r…
Counting Cohesive Subgraphs with Hereditary Properties
Rong-Hua Li, Xiaowei Ye, Fusheng Jin +3
Counting small cohesive subgraphs in a graph is a fundamental operation with numerous applications in graph analysis. Previous studies on cohesive subgraph counting are mainly base…
On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving
Kaituo Feng, Changsheng Li, Dongchun Ren +2
End-to-end motion planning models equipped with deep neural networks have shown great potential for enabling full autonomous driving. However, the oversized neural networks render…
Multi-Prior Learning via Neural Architecture Search for Blind Face Restoration
Yanjiang Yu, Puyang Zhang, Kaihao Zhang +4
Blind Face Restoration (BFR) aims to recover high-quality face images from low-quality ones and usually resorts to facial priors for improving restoration performance. However, cur…