8 citations · 13 across the 9 of their papers we have counts for
5 papers · 1 filter
Steering Vision-Language Models for Visual-Contextual Privacy in Location Disclosure
Wenyue Chen, Yuhan Cheng, Ruixin Yang +5
Vision-language models (VLMs) can infer sensitive information such as geolocation from subtle visual cues, creating privacy risks when responses disclose more detail than the visua…
Reinforcement Learning over Predictive Distributions for LLM Regression
Jungsoo Park, Hyungjoo Chae, Ethan Mendes +4
Large language models (LLMs) have emerged as flexible regressors capable of predicting real-valued quantities from heterogeneous inputs. Yet most LLM regression objectives optimize…
Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?
Ruixin Yang, Ethan Mendes, Arthur Wang +4
Vision-language models (VLMs) have demonstrated strong performance in image geolocation, a capability further sharpened by frontier multimodal large reasoning models (MLRMs). This…
Making Expert Reasoning Learnable with Self-Distillation
Ethan Mendes, Jungsoo Park, Alan Ritter
Improving the reasoning capabilities of large language models (LLMs) typically relies either on the model's ability to sample a correct solution to be reinforced or the existence o…
GeoRC: A Benchmark for Geolocation Reasoning Chains
Mohit Talreja, Joshua Diao, Jim Thannikary James +6
Vision Language Models (VLMs) are good at recognizing the global location of a photograph -- their geolocation prediction accuracy rivals the best human experts. But many VLMs are…