Zhe Liu

LEAP reveals that separating evidence evaluation can drastically enhance the accuracy and interpretability of probabilistic forecasts in LLM applications.

SetMIR not only boosts conversion rates by over 3% but also streamlines retrieval by cutting down query redundancy by a third.

CAMIE boosts click-through rates by nearly 19% over traditional text-based retrieval methods by aligning item embeddings with user engagement patterns.

Achieving state-of-the-art robust estimation efficiency, DiffSAC slashes hypothesis evaluations from over 10,000 to just dozens.

StreamPI transforms VLA models by enabling them to retain temporal context and enhance spatial perception, outperforming traditional single-frame approaches.

LLMs can significantly boost forecasting accuracy, but their effectiveness is hampered by measurement limitations and sensitivity to input changes.

PertMind reveals that leveraging cellular perturbation data can significantly enhance LLMs' biological reasoning capabilities without extensive task-specific retraining.

Robots can now learn new tasks on-the-fly without retraining, drastically cutting down the time and resources needed for adaptation.