PRISM: Breaking Modal Boundaries — How a Unified Architecture Handles ECG, Images, and Continuous Signals

The PRISM project aims to build a truly modality-agnostic sequence model using the S4D-Complex structure and gated Delta rule, enabling unified processing of ECG, images, and continuous signals. It challenges the traditional paradigm of 'one model per modality' in deep learning and opens up new paths for multimodal AI infrastructure.

In the development of deep learning, specialized designs (CNN for images, Transformer for text, RNN/LSTM for time series) lead to high R&D costs and a fragmented technical ecosystem. PRISM addresses this situation with a question: Can we design a single architecture to handle ECG, images, and continuous signals? Its core insight is that different data types are essentially spatiotemporal sequences, requiring a suitable unified modeling tool.

PRISM's core components include:

1. S4D-Complex: A state space model extended to the complex domain, efficiently capturing long-range dependencies and naturally adapting to oscillatory and phase characteristics of periodic signals (e.g., ECG);
2. Gated Delta Rule: An adaptive state update mechanism that controls the fusion ratio of new information and historical states via gating weights, with the Delta rule calculating update increments; Unified process: Serialization encoding → S4D-Complex processing → Gated state update → Task-specific decoding.

PRISM's modality-agnostic特性 applies to multiple domains:

• Healthcare: Unified processing of multi-source physiological signals (ECG, blood oxygen, blood pressure, etc.) for integrated diagnosis; long-range modeling to identify long-term trends of chronic diseases;
• Industrial IoT: Natively processing heterogeneous sensor data (temperature, vibration, vision, etc.) to simplify predictive maintenance;
• Scientific Research: Unified processing of cross-domain data (climate, ocean, ecology) to facilitate pattern discovery and causal inference.

Advantages: High parameter efficiency (replacing multiple specialized models), cross-modal transfer learning, simplified deployment, elegant theory; Challenges: Slightly inferior performance on specific tasks compared to specialized models, poor interpretability, high training complexity (requiring large datasets and complex strategies), difficulty in modal balance (avoiding dominance by a single modality).

PRISM's future evolution directions:

• Incorporate more modalities (text, audio, point clouds, graph structures, etc.);
• Ultra-large-scale multimodal pre-training to learn general world representations;
• Integration of neural symbols (perception + logical reasoning);
• Edge deployment optimization to adapt to mobile/embedded systems.

The value of PRISM is not only as a technical tool but also as a carrier of the philosophy that 'unification is more powerful than division'. By finding common patterns behind data, it simplifies engineering implementation and deepens the understanding of the essence of intelligence. Like a prism, it refracts colorful data into a unified spectrum of intelligence, serving as an important milestone toward general artificial intelligence.