Featured work
🤝 MESHAgents: Multimodal Multi-Agent Reasoning in Medical AI
In this work, we introduce MESHAgents — a multi-agent framework where specialized agents reason over different clinical modalities including 3D meshes, text, and signals. Agents communicate via natural language to jointly interpret cardiac conditions, combining structure-aware perception with symbolic interaction. This approach improves transparency, modularity, and performance on clinical decision-making tasks.
Status: Accepted at MICCAI 2025
Towards Effective MLLM Jailbreaking Through Balanced On-Topicness and OOD-Intensity
We introduce Balanced Structural Decomposition (BSD) — a novel method to craft adversarial prompts that bypass safety filters in multimodal large language models (MLLMs). Unlike prior approaches, BSD decomposes malicious prompts into semantically aligned subtasks, blending relevance with subtle out-of-distribution cues. Tested on 13 MLLMs, BSD significantly outperforms existing jailbreak techniques, revealing new vulnerabilities in current safety systems.