## Diagram: Technical Architecture for Reasoning-Based Question Answering System ### Overview The diagram illustrates a two-stage technical architecture for processing and answering reasoning-based questions. It combines off-the-shelf embedding models with custom reasoning components, featuring explicit example selection and conditional variational auto-encoder (CVAE) components. The system processes natural language queries through multiple stages of encoding, reasoning, and decoding. ### Components/Axes 1. **Pre-Processing Section (Left Side)** - Input Query Box: Contains sample questions (e.g., "Seven red apples...") - Off-the-Shelf Embedding Model: Standard NLP model for initial text representation - Reasoning Policy: Decision-making component for reasoning strategy - Example Bank: Repository of solved examples (shown with apple/toucan illustrations) - Reasoning Encoder/Decoder: CVAE components for structured reasoning 2. **Selection Section (Right Side)** - Input Query Box: Contains second sample question ("2 toucans...") - Selected Examples Highlight: Visual indicator of retrieved examples - Reasoning Skills Visualization: Circular diagram with colored dots representing reasoning steps - Reasoning Policy: Same component as in pre-processing section - CVAE Components: Mirroring the left section's encoder/decoder structure 3. **Connecting Elements** - Arrows showing data flow between components - Mathematical notation (Q for queries, R for responses, Z for latent representations) - Example Bank icon (classical building symbol) - Reasoning Skills icon (lightbulb) ### Detailed Analysis 1. **Pre-Processing Flow** - Input queries are first processed by an off-the-shelf embedding model - The reasoning policy determines how to handle the query - The system either generates a direct answer (R) or stores it in the example bank - The CVAE components (Reasoning Encoder/Decoder) process the query through latent space (Z) 2. **Selection Mechanism** - New queries trigger example retrieval from the bank - Selected examples are highlighted in red - The reasoning skills visualization shows step-by-step problem decomposition - The same CVAE architecture processes both direct and example-based reasoning 3. **Mathematical Notation** - Q: Represents input queries (e.g., "How many apples...") - R: Denotes system responses (e.g., "We add 7 to 2...") - Z: Latent space representation in the CVAE architecture ### Key Observations 1. The system uses both direct reasoning and example-based reasoning 2. The reasoning policy acts as a central decision-making component 3. The example bank serves as a knowledge repository for similar problems 4. The CVAE architecture enables structured reasoning through latent space manipulation 5. Visual elements (colors, icons) help distinguish different components ### Interpretation This architecture demonstrates a hybrid approach to question answering that combines: 1. **Pre-trained Language Models**: For initial text understanding 2. **Custom Reasoning Components**: For problem-solving logic 3. **Example-Based Learning**: Through the example bank mechanism 4. **Probabilistic Reasoning**: Via the conditional variational auto-encoder The system appears designed to handle arithmetic and simple logical reasoning tasks by: - First determining the appropriate reasoning strategy - Either generating a direct answer or retrieving similar examples - Processing through a structured reasoning pipeline - Maintaining consistency between direct and example-based reasoning paths The use of CVAE suggests the system can handle uncertainty in reasoning steps while maintaining coherent problem-solving trajectories. The explicit example selection mechanism indicates an emphasis on leveraging past solutions for new problems.