3 citations · 3 across the 29 of their papers we have counts for
5 papers · 1 filter
Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operations
Junjue Wang, Weihao Xuan, Heli Qi +7
Operational disaster response goes beyond damage assessment, requiring responders to integrate multi-sensor signals, reason over road networks, populations and key facilities, plan…
Training-Free Safe Denoisers for Safe Use of Diffusion Models
Mingyu Kim, Dongjun Kim, Amman Yusuf +2
There is growing concern over the safety of powerful diffusion models (DMs), as they are often misused to produce inappropriate, not-safe-for-work (NSFW) content or generate copyri…
GeoEvolve: Automating Geospatial Model Discovery via Multi-Agent Large Language Models
Peng Luo, Xiayin Lou, Yu Zheng +2
Geospatial modeling provides critical solutions for pressing global challenges such as sustainability and climate change. Existing large language model (LLM)-based algorithm discov…
Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF
Syrine Belakaria, Joshua Kazdan, Charles Marx +5
Reinforcement learning from human feedback (RLHF) has become a cornerstone of the training and alignment pipeline for large language models (LLMs). Recent advances, such as direct…
MADiff: Offline Multi-agent Learning with Diffusion Models
Zhengbang Zhu, Minghuan Liu, Liyuan Mao +5
Offline reinforcement learning (RL) aims to learn policies from pre-existing datasets without further interactions, making it a challenging task. Q-learning algorithms struggle wit…