MRINMR

MRINMR, or Multimodal Reasoning via Interpretable Neural Modules and Residuals, is a theoretical framework in artificial intelligence that envisions a modular neural architecture capable of processing multiple data modalities and performing structured reasoning. The goal is to combine interpretable reasoning steps with the representational power of deep networks, enabling step-by-step inference and transparent troubleshooting of failures.

Core components include modality-specific perception modules (for text, images, and audio), a reasoning controller that selects and sequences neural modules, a differentiable memory for storing intermediate facts, and an inference mediator that integrates outputs across modalities. The architecture emphasizes explicit intermediate representations and iterative refinement, often employing residual updates to propagate information across reasoning steps.

Common tasks envisioned for MRINMR include visual question answering, cross-modal retrieval, robotics planning, and medical decision support, especially where cross-modal evidence must be combined and justification of conclusions is desirable.

Evaluation focuses on accuracy and reasoning traceability, along with ablation studies that assess each module's contribution. Challenges include data alignment across modalities, computational efficiency, and ensuring the readability of the reasoning trace. Critics note that modular designs may underperform end-to-end systems on some benchmarks and can require substantial annotation to guide module behavior.

Relationship to broader fields: MRINMR sits within neuro-symbolic AI, modular neural networks, and differentiable programming, sharing goals of interpretability and systematic reasoning. As of the present, MRINMR is discussed mainly in theoretical and exploratory contexts, with limited publicly available implementations. See also neuro-symbolic AI, modular neural networks, differentiable programming, multimodal learning.