1 citations · 1 across the 9 of their papers we have counts for
11 papers
Network World Models as Environments for Algorithm Design on Complex Systems
Rishab Alagharu, Hongji Pu, Zeeshan Memon +3
World models, which simulate an environment and predict how it changes under actions, are increasingly used in real-world applications such as robotics. Complex systems call for th…
AlgoBench: Benchmarking Algorithmic Adaptation in Code Generation
Xinyuan Song, Zekun Cai, Liang Zhao
High pass rates on established programming benchmarks such as HumanEval and LiveCodeBench do not always show whether a model can reason about algorithms. Many fixed benchmarks even…
AlgoSkill: Learning to Design Algorithms by Scheduling Human-Like Skills
Xinyuan Song, Zekun Cai, Liang Zhao
Designing an algorithm from a natural-language problem statement requires identifying the problem structure, reading constraints, choosing a suitable paradigm, checking correctness…
When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs
Xinyuan Song, Zekun Cai, Liang Zhao
Recursive self-training can degrade neural generative models when generated data is reused without fresh human data or external quality control. We study this risk in code LLMs, wh…
LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning
Hongwei Jin, Keunju Song, Zeeshan Memon +5
AC optimal power flow (ACOPF) is foundational yet computationally expensive in power grid operations, driving learning-based surrogates for large-scale grid analysis. These surroga…
Towards Systematic Generalization for Power Grid Optimization Problems
Zeeshan Memon, Yijiang Li, Hongwei Jin +2
AC Optimal Power Flow (ACOPF) and Security-Constrained Unit Commitment (SCUC) are fundamental optimization problems in power system operations. ACOPF serves as the physical backbon…
StructPrune: Structured Global Pruning asymptotics with GPU Memory
Xinyuan Song, Guangji Bai, Liang Zhao
Pruning is critical for scaling large language models (LLMs). Global pruning achieves strong performance but requires memory, which is infeasible for billion-param…
Continuous Domain Generalization
Zekun Cai, Yiheng Yao, Guangji Bai +4
Real-world data distributions often shift continuously across multiple latent factors such as time, geography, and socioeconomic contexts. However, existing domain generalization a…
FedSpaLLM: Federated Pruning of Large Language Models
Guangji Bai, Yijiang Li, Zilinghan Li +2
Large Language Models (LLMs) achieve state-of-the-art performance but are challenging to deploy due to their high computational and storage demands. Pruning can reduce model size,…
Structural Disentanglement of Causal and Correlated Concepts
Qilong Zhao, Shiyu Wang, Zeeshan Memon +5
Controllable data generation aims to synthesize data by specifying values for target concepts. Achieving this reliably requires modeling the underlying generative factors and their…
Continuous Temporal Domain Generalization
Zekun Cai, Guangji Bai, Renhe Jiang +2
Temporal Domain Generalization (TDG) addresses the challenge of training predictive models under temporally varying data distributions. Traditional TDG approaches typically focus o…