works on

From the 1 of 14 linked papers with an AI index.

activity
20242026
collaborators

14 papers

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.LG2026

Scaling World-Model Reinforcement Learning Through Diffusion Policy Optimization

Xiaoyuan Cheng, Wenxuan Yuan, Zhancun Mu +5

Model-based reinforcement learning (RL) can be effectively supported at scale through the use of world models. However, in practice, scaling such approaches remains fundamentally l…

cs.IR2026

Meta-Modal Agent: Sequential Evidence Routing for Missing-Modality Candidate Reranking

Jinze Wang, Yangchen Zeng, Tiehua Zhang +5

Missing modalities cause severe failures in multimodal recommender systems. User histories, item text, and visual evidence are frequently absent during cold-start scenarios, exactl…

cs.LG2026

Interdomain Attention: Beyond Token-Level Key-Value Memory

Naoki Kiyohara, Harrison Bo Hua Zhu, Riccardo El Hassanin +4

Transformers and deep state space models (SSMs) sit at opposite ends of a basic design choice: attention routes each query through a growing key-value (KV) cache by content-based m…

cs.IR2026

SG-LegalCite: A Principle-Augmented Benchmark for Legal Citation Retrieval in Singapore Law

Shannon Lee Yueh Ern, Kaidong Feng, Yingpeng Du +2

Legal citation in common-law systems depends not only on factual similarity, but also on the legal principle for which a precedent is invoked. However, existing benchmarks for lega…

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…