works on

From the 1 of 9 linked papers with an AI index.

activity
20242026
collaborators

9 papers

cs.LG2026

PLAN: Parallel Liquid-Inspired Approximation Network for Efficient Representation Learning in Flexible Job Shop Scheduling

Dhivya Dharshini Kannan, Wei Zhang, Jieyi Bi +5

Deep reinforcement learning (DRL) approaches for flexible job shop scheduling (FJSP) heavily rely on attention-centric architectures to achieve state-of-the-art performance. Howeve…

cs.IR2026

Think When Needed: Model-Aware Reasoning Routing for LLM-based Ranking

Huizhong Guo, Tianjun Wei, Dongxia Wang +4

The paper introduces a lightweight router that decides per query whether to apply reasoning (chain‑of‑thought) or direct inference with large language models for ranking, using pre…

cs.SE2026

SkillMOO: Multi-Objective Optimization of Agent Skills for Software Engineering

Jingzhi Gong, Ruizhen Gu, Zhiwei Fei +7

Agent skills are increasingly used to configure coding agents for software engineering (SE) tasks, yet current practice treats them as static, hand-crafted assets, or evolved on pa…

cs.HC2026

Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation

Tianjun Wei, Huizhong Guo, Yingpeng Du +4

User simulation is increasingly vital to develop and evaluate recommender systems (RSs). While Large Language Models (LLMs) offer promising avenues to simulate user behavior, they…

cs.SE2026

LLMs Are Not a Silver Bullet: A Case Study on Software Fairness

Xinyue Li, Sixuan Li, Ying Xiao +4

Fairness is a critical requirement for human-related, high-stakes software systems, motivating extensive research on bias mitigation. Prior work has largely focused on tabular data…

cs.IR2026

MMGRid: Navigating Temporal-aware and Cross-domain Generative Recommendation via Model Merging

Tianjun Wei, Enneng Yang, Yingpeng Du +3

Model merging (MM) offers an efficient mechanism for integrating multiple specialized models without access to original training data or costly retraining. While MM has demonstrate…