Ming Kong

MetaRAG achieves a superior accuracy-efficiency trade-off in agentic RAG by aligning decision-making with the model's internal beliefs, outperforming traditional RL methods.

NGS-Marker effectively thwarts partial infringement in 3D Gaussian Splatting, ensuring robust copyright protection for 3D assets.

Confidence miscalibration in medical AI can be mitigated, leading to both higher diagnostic accuracy and improved trust in clinical applications.

Living-Harness enables agents to learn from past failures dynamically, leading to substantial performance improvements in interactive tasks.

LLMs can now be trained to prioritize task constraints intrinsically, resulting in a dramatic improvement in planning reliability.

Trajectory neglect in LLM agents can be significantly reduced using a novel reward mechanism that enhances focus on task goals without sacrificing training stability.

MLLMs can mislead medical professionals by misaligning confidence with accuracy, but a new calibration method cuts Expected Calibration Error by 40%.

MLLMs are often overconfident, but a new confidence-driven training and test-time scaling approach can boost accuracy by 8.8% across benchmarks.