Yiyan Qi

CostAda achieves top-tier discovery quality while using at least 50% less budget compared to existing methods, revolutionizing how we approach resource allocation in LLM search processes.

Multimodal models can now navigate the real-world web with VSearcher, an RL-trained agent that outperforms even proprietary models in complex search tasks.

Achieve up to 102% Sharpe Ratio improvement and 17.5% directional accuracy gain by unifying event-centric data construction and decision-oriented fine-tuning with a hierarchical gated reward model.

Attention-based re-ranking gets a boost: ReAttn's post-hoc re-weighting tames over-concentration and lexical bias, leading to more accurate and interpretable results without extra training.

Achieve state-of-the-art dynamic graph anomaly detection with limited labels by learning a robust decision boundary around normal data, outperforming methods that overfit to scarce anomalies.