Kai-Wei Chang

AudioICL-Bench is introduced, a diagnostic benchmark whose per-episode rules are resampled so that no correct answer is recoverable from prior knowledge, and its nine tasks are organized along two axes that separate what must be learned from demonstrations from what must be perceived in the signal, enabling failures to be attributed to either source.

Frontier multimodal models that master complex single-image visual reasoning collapse to sub-35% accuracy when asked to detect basic low-level noise and texture differences between two images.

Sparsifying communication topologies in LLM multi-agent systems can lead to significant efficiency gains without sacrificing accuracy, challenging conventional design approaches.

Federated learning can outperform centralized models in multi-agent safety without compromising data privacy, achieving a remarkable 43% reduction in attack success rates.

Multi-agent refinement can enhance diagram quality across multiple iterations, countering common pitfalls like quality drift and forgetting.