3 papers
cs.IR2026
OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation
Haoyang Fang, Shuai Zhang, Yifei Ma +5
Domain-specific finetuning is essential for dense retrievers, yet not all data pairs contribute equally to the learning process. We introduce OPERA, a data pruning framework that e…
cs.CV2026
Wanderland: Geometrically Grounded Simulation for Open-World Embodied AI
Xinhao Liu, Jiaqi Li, Youming Deng +7
Reproducible closed-loop evaluation remains a major bottleneck in Embodied AI such as visual navigation. A promising path forward is high-fidelity simulation that combines photorea…
cs.LG2026
Optimal Design for Human Preference Elicitation
Subhojyoti Mukherjee, Anusha Lalitha, Kousha Kalantari +4
Learning of preference models from human feedback has been central to recent advances in artificial intelligence. Motivated by the cost of obtaining high-quality human annotations,…