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From the 1 of 10 linked papers with an AI index.

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10 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.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.MM2026

Through Their Eyes: Fixation-aligned Tuning for Personalized User Emulation

Lingfeng Huang, Huizhong Guo, Tianjun Wei +2

Large language model (LLM) agents are increasingly deployed as scalable user simulators for recommender system evaluation. Yet existing simulators perceive recommendations through…

cs.AI2026

When should I search more: Adaptive Complex Query Optimization with Reinforcement Learning

Wei Wen, Sihang Deng, Tianjun Wei +3

Query optimization is a crucial component for the efficacy of Retrieval-Augmented Generation (RAG) systems. While reinforcement learning (RL)-based agentic and reasoning methods ha…

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…