19 citations · 77 across the 60 of their papers we have counts for
62 papers
Act First, Reason Later: Accelerating On-Policy Distillation for Multi-Turn Agents via Reference-Conditioned Inverse Dynamics
Zubin Zheng, Jiahao Wu, Shaofeng Zhang +3
On-policy distillation (OPD) trains multi-turn language agents with dense teacher supervision on student-generated responses. However, standard think-then-act rollouts require leng…
Online Bin Packing with Per-Bin Maximum Delay
Tianhang Lu, Runtian Ren, Shengcai Liu
We study online bin packing with per-bin maximum delay: each sealed bin incurs a unit opening cost plus the longest waiting time among its items. Offline, this becomes a temporal-s…
Paging with Per-Replacement Maximum Delay
Tianhang Lu, Runtian Ren, Shengcai Liu
Classical paging serves every miss immediately. We study paging with per-replacement maximum delay, where loading a pending page costs one movement plus the age of its oldest outst…
AsmEvo: Agentic Assembly-Level Optimization of AMD GPU Kernels with Functional Equivalence Verification
Ji Liu, Puyuan Yang, Rongzhang Zheng +18
High-performance ML systems increasingly rely on GPU kernels whose editable source is unavailable, generated, or too distant from final machine code to expose remaining optimizatio…
Online Service with Per-Batch Maximum Delay
Tianhang Lu, Runtian Ren, Shengcai Liu +1
We study online service with one maximum-waiting-time charge per service batch. The persistent server endpoint prevents a phase-by-phase comparison with the offline optimum: an off…
Online Multi-Level Aggregation with Per-Batch Maximum Delay
Tianhang Lu, Runtian Ren, Shengcai Liu +1
We study online multi-level aggregation on finite rooted trees with a per-batch maximum-delay objective. A service pays for a rooted subtree and for the maximum waiting time among…
Not All Problems Are Best Modeled as MILP: A DSL-Centric Framework for Flexible and Accurate Optimization Modeling
Shaofeng Zhang, Hongyuan Su, Qingwen Peng +4
Solving combinatorial optimization problems (COPs) requires not only efficient algorithms but also carefully crafted formulations. While recent works have leveraged LLMs to automat…
Evolving Parallel Algorithm Portfolios via Potential-Aware Instance Generation with LLMs
Shaofeng Zhang, Shengcai Liu, Zhiyuan Wang +1
The Automatic Construction of Portfolios via Large Language Models (LLM-ACP) suffers from poor generalization in practical few-shot scenarios when solving complex combinatorial opt…
MuEvo: LLM-Driven Evolution of Multi-Heuristic Ensemble
Haoze Lv, Ning Lu, Shengcai Liu +2
Large language model-based automated heuristic design (LLM-AHD) has shown strong potential in discovering effective heuristics for combinatorial optimization problems. However, exi…
Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training
Zhiyuan Wang, Shengcai Liu, Jiahao Wu +5
Tool-using large language model (LLM) agents produce long, multi-turn trajectories, making gradient-based post-training memory-intensive. Evolution strategies (ES) enable memory-ef…
Learning-Augmented and Randomized Algorithms for Line Aggregation with Delays
Tianhang Lu, Runtian Ren, Shengcai Liu +1
This paper studies learning-augmented and randomized online aggregation with delays on a line metric. We consider advice given as online suggested service lengths, and evaluate the…
Learning-Augmented Algorithms for Online Vertex Cover
Tianhang Lu, Runtian Ren, Shengcai Liu
This paper studies learning-augmented online weighted vertex cover with local advice and a tradeoff parameter . We consider two graph settings: bipartite graphs and gen…
EMO: Energy Efficiency Modeling and Optimization for AI Workloads
Jiyu Luo, Shaoyu Chen, Jingwei Sun +3
The massive energy consumption of GPU-accelerated AI workloads challenges sustainable computing. We observe that execution asynchrony (e.g., CPU-GPU, concurrent streams, multi-GPU)…
Fuzzy-Geometric Branch-Point Modeling for Structure-Aware Augmentation of Handwritten Chinese Characters
Dongbin Jiao, Yibo Lyu, Qiulu Wei +3
Data scarcity and structural distortion significantly limit handwriting recognition in high-security authentication. Existing augmentation methods often cause topological and morph…
Large Language Model-Driven Cooperative Operator Ensemble Evolution for Permutation Flow Shop Scheduling
Rui Xu, Yufan Liao, Haoze Lv +3
The permutation flow shop scheduling problem (PFSP) is a classical NP-hard combinatorial optimization problem in intelligent manufacturing. In practice, PFSP is commonly addressed…
Policy and World Modeling Co-Training for Language Agents
Ning Lu, Baijiong Lin, Shengcai Liu +9
Reinforcement learning (RL) improves large language model (LLM) agents by teaching them which actions lead to high rewards, but provides little supervision on what those actions do…
General-Purpose Co-Evolutionary Construction of Parallel Algorithm Portfolios for Multi-Objective Binary Optimization
Zhiyuan Wang, Shengcai Liu, Shaofeng Zhang +1
Despite recent progress in constructing generalizable parallel algorithm portfolios (PAPs), no general-purpose approach is yet available for multi-objective binary optimization pro…
Neural QAOA: Differentiable Joint Graph Partitioning and Parameter Initialization for Quantum Combinatorial Optimization
Zubin Zheng, Jiahao Wu, Shengcai Liu
The quantum approximate optimization algorithm (QAOA) holds promise for combinatorial optimization but is constrained by limited qubits. While divide-and-conquer frameworks like QA…
AHD Agent: Agentic Reinforcement Learning for Automatic Heuristic Design
Haoze Lv, Ning Lu, Ziang Zhou +2
Automatic heuristic design (AHD) has emerged as a promising paradigm for solving NP-hard combinatorial optimization problems (COPs). Recent works show that large language models (L…
Multi-Objective Evolutionary Optimization of Chance-Constrained Multiple-Choice Knapsack Problems with Implicit Probability Distributions
Xuanfeng Li, Shengcai Liu, Wenjie Chen +2
The multiple-choice knapsack problem (MCKP) is a classic combinatorial optimization with wide practical applications. This paper investigates a significant yet underexplored extens…
Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model
Jiahao Wu, Ning Lu, Shengcai Liu +6
Reinforcement learning (RL) has become essential for post-training large language models (LLMs) in reasoning tasks. While scaling rollouts can stabilize training and enhance perfor…
OD-Gear: Online Decomposition and Group Sampling for Expert-Guided Adversarial Routing in Scalable Capacitated Vehicle Routing
Dongbin Jiao, Zisheng Chen, Xianyi Wang +3
Solving large-scale capacitated vehicle routing problems (CVRP) is hindered by the high complexity of classical heuristics and the limited generalization of neural solvers. To brid…
A Novel Population Initialization Method via Adaptive Experience Transfer for General-Purpose Binary Evolutionary Optimization
Zhiyuan Wang, Shengcai Liu, Shaofeng Zhang +1
Evolutionary Algorithms (EAs) are widely used general-purpose optimization methods due to their domain independence. However, under a limited number of function evaluations (#FEs),…
Personalized Treatment Outcome Prediction from Scarce Data via Dual-Channel Knowledge Distillation and Adaptive Fusion
Wenjie Chen, Li Zhuang, Ziying Luo +3
Personalized treatment outcome prediction based on trial data for small-sample and rare patient groups is a critical task in precision medicine. However, the high cost and scarcity…