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We demonstrate that sparse MoE architectures exhibit sub-linear scaling costs while maintaining reasoning fidelity across multi-step benchmarks. Our ablation studies reveal critical routing thresholds that optimize compute allocation without degradation.
A unified vision-language framework for real-time intraoperative assistance. Achieves 94.2% tool recognition accuracy and provides context-aware safety alerts validated across 12 surgical specialties.
Investigates zero-shot transfer capabilities of transformer-based parsers across agglutinative and polysynthetic languages. Proposes a morphology-aware attention mechanism that improves F1 scores by 18% on target languages.
A longitudinal study on bias amplification in reinforcement learning-based recommenders. Introduces a causal fairness constraint that reduces demographic disparity by 32% without compromising engagement metrics.
Proposes a gradient-based alignment framework that replaces discrete reward hacking with continuous policy shaping. Early results show 2.3x faster convergence on constitutional safety benchmarks.
Applies optimized NeRF pipelines to fragmentary archaeological data. Demonstrates sub-centimeter accuracy in virtual site restoration, enabling non-invasive cultural heritage preservation.
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