3 papers
cs.AI2026
LLM Reasoning for Subjective Tasks: Failure Modes, Mitigation, and Dynamic Reasoning Routing
Juncheng Dong, Ding Tong, Ishan Gupta +1
Recommendation systems thrive on personalization, where ''correctness'' is rarely a binary truth but a matter of subjective human preference. As Large Language Models (LLMs) are de…
cs.AI2026
Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading
Polydoros Giannouris, Yuechen Jiang, Lingfei Qian +5
Many sequential decision-making problems exhibit hierarchical structure, where high-level semantic choices constrain downstream actions and feedback is delayed and ambiguous. Learn…
cs.IR2025
Can Explanations Improve Recommendations? Evidence from Prediction-Informed Explanations
Yuyan Wang, Pan Li, Minmin Chen
Recommender systems are central to digital platforms, yet they face a fundamental trade-off between accuracy and explainability. Black-box models achieve strong performance but lac…