眼动数据缺失是认知负荷评估的长期痛点，MambaGaze用双向Mamba显式建模缺失模式，做可穿戴AI或人机交互的团队可以直接参考其边缘部署方案。
眼动追踪数据在实时认知负荷评估中面临两大挑战：眨眼和追踪失败导致的数据频繁缺失，以及长时程依赖的高效建模。MambaGaze框架通过显式缺失数据建模（XMD编码）和双向Mamba-2结构解决这些问题，在CLARE和CL-Drive数据集上分别达到76.8%和73.1%的准确率，超越CNN、Transformer等基线4-12个百分点。在NVIDIA Jetson边缘设备上实现43-68 FPS的实时推理，功耗低于7.5W，适合可穿戴认知负荷监测场景。该工作为安全关键应用（如驾驶员警觉监控、自动化驾驶舱辅助）提供了可行的AI方案。
MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data
Real-time cognitive load assessment from eye-tracking signals could potentially enable adaptive human-centered-AI such as safety-critical applications such as driver vigilance monitoring or automated flight deck assistance, yet two challenges persist: handling frequent data missingness from blinks and tracking failures, and efficiently modeling long-range temporal dependencies. We propose MambaGaze, a framework that addresses these challenges through 1) XMD encoding, which augments raw features with observation masks and time-deltas to explicitly model data uncertainty, and 2) bidirectional Mamba-2, which captures temporal dependencies with linear computational complexity. Experiments on CLARE and CL-Drive datasets under leave-one-subject-out evaluation show that MambaGaze achieves 76.8% and 73.1% accuracy, respectively, outperforming CNN, Transformer, ResNet, and VGG baselines by 4-12 percentage points. Edge deployment benchmarks on NVIDIA Jetson platforms demonstrate real-time inference at 43-68 FPS with power consumption below 7.5W, confirming feasibility for wearable cognitive load monitoring.