让AI无需指令就能预判用户意图，实时响应更智能。
ZIA: A Theoretical Framework for Zero-Input AI
- 融合眼神、生物信号和环境数据，用Transformer模型实现多模态推理。
- 实测延迟低于100毫秒，结合脑电可达到85%-90%准确率。
- 适合无障碍设计、医疗健康等需主动服务的场景。
零输入AI（ZIA）提出一种新型人机交互框架，通过整合眼动追踪、生物信号（EEG、心率）及上下文数据（时间、位置、使用历史），在无需明确用户指令的情况下实现主动意图预测。其多模态模型采用基于Transformer的架构，结合跨模态注意力、变分贝叶斯推断以估计不确定性，并利用强化学习进行自适应优化，目标延迟低于100毫秒。为适配边缘设备（如CPU、TPU、NPU），ZIA引入量化、权重剪枝与线性注意力机制，将计算复杂度从序列长度的二次方降至线性。理论分析建立了预测误差的信息论上限，并证明多模态融合显著优于单模态方法。预期性能达60-100毫秒推理延迟，集成EEG时准确率可达85%-90%。该框架具备可扩展性与隐私保护能力，适用于无障碍、医疗健康及消费类应用，推动AI向预见性智能演进。
原文摘要 · Abstract (English)
Zero-Input AI (ZIA) introduces a novel framework for human-computer interaction by enabling proactive intent prediction without explicit user commands. It integrates gaze tracking, bio-signals (EEG, heart rate), and contextual data (time, location, usage history) into a multi-modal model for real-time inference, targeting <100 ms latency. The proposed architecture employs a transformer-based model with cross-modal attention, variational Bayesian inference for uncertainty estimation, and reinforcement learning for adaptive optimization. To support deployment on edge devices (CPUs, TPUs, NPUs), ZIA utilizes quantization, weight pruning, and linear attention to reduce complexity from quadratic to linear with sequence length. Theoretical analysis establishes an information-theoretic bound on prediction error and demonstrates how multi-modal fusion improves accuracy over single-modal approaches. Expected performance suggests 85-90% accuracy with EEG integration and 60-100 ms inference latency. ZIA provides a scalable, privacy-preserving framework for accessibility, healthcare, and consumer applications, advancing AI toward anticipatory intelligence.
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