Yi Liu

VLMs that ace digital document parsing benchmarks still stumble badly when faced with real-world scanned, warped, or photographed documents, revealing a significant "reality gap."

You can cut MLLM hallucinations in remote sensing tasks without any training by cleverly exploiting the model's own attention mechanisms to focus on relevant image regions.

LLM-powered pentesting agents fail not because of model limitations, but because they can't estimate task difficulty, leading to wasted effort and premature context exhaustion.