Yucheng Wang

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.

Kimi K3's innovative architecture achieves a 2.5x scaling efficiency improvement, enabling robust performance across diverse long-horizon tasks.

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

Over-reliance on agentic decomposition can actually *hurt* audio understanding when a strong audio frontend already provides sufficient information, highlighting the importance of conditional evidence acquisition.

Forget painstakingly collecting real-world defect data: high-fidelity synthetic anomalies, automatically generated from product designs using an MLLM, can dramatically improve 3D anomaly detection.

Forget fixed residual connections: Attention Residuals let each layer selectively attend to previous layers, boosting performance and gradient flow in deep LLMs.

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

G-STAR tackles long-form, multi-speaker ASR by giving Speech-LLMs time-aware speaker tracking, enabling robust identity linking across chunks.