Hao Wang

Achieving a staggering 24.0x speedup in quantum error correction simulations while maintaining high fidelity opens new avenues for resource-efficient quantum computing.

SNLP slashes the number of symbolic bootstraps in encrypted Transformer inference by over 2.5 times while maintaining performance, revealing a new avenue for efficient FHE applications.

Achieving robust pose tracking and memory efficiency, KiloGS-SLAM can handle over 10,000 frames in challenging outdoor environments without sacrificing accuracy or detail.

MoRE achieves a staggering 44 percentage point increase in deployment success rates by seamlessly integrating behavior mode redirection into policy weights, eliminating the need for inference-time adjustments.

DMuon slashes training time for large models, achieving up to 163x faster optimizer steps while maintaining the benefits of matrix-aware updates.

SARA unlocks the potential of low-resource languages in multilingual models by aligning their expert routing with high-resource anchors, leading to measurable performance gains.

IOI achieves state-of-the-art simulation performance by decoupling deterministic motion from stochastic physical interactions, enabling robust zero-shot generalization to unseen tasks.

LoadKAN not only forecasts electricity demand with high accuracy but also deciphers complex relationships between mobility patterns and load, revealing insights that traditional black-box models obscure.

Forget the hardware-speed tradeoff in Ising machines: a single parameter tweak can unlock up to 27x speedups without extra resources.