XMODE: A Technical Framework for Explainable Multi-Modal Data Exploration

Handling multi-modal data — structured relational tables, unstructured text, images — requires systems that are efficient, transparent, and robust. XMODE, introduced in the paper “Explainable Multi-Modal Data Exploration in Natural Language via LLM Agent,” provides a structured approach to these challenges. By using Directed Acyclic Graphs (DAGs) for workflow orchestration and Large Language Models (LLMs) for reasoning and adaptability, XMODE ensures that complex queries can be decomposed, executed, and explained with precision.

This post unpacks the core elements of XMODE, detailing how it transforms queries into DAGs, manages execution and state, handles failures dynamically, and integrates modular expert tools. The discussion extends to how XMODE’s principles apply to designing scalable multi-agent copilots.

Photo by Andrei Zolotarev on Unsplash

Core Challenges in Multi-Modal Data Exploration

Systems designed for multi-modal data exploration face inherent technical complexities:

• Heterogeneous Data Integration: Structured and unstructured data require seamless alignment across formats and semantics.
• Complex Query Orchestration: Natural language queries must be decomposed into interdependent tasks that execute in sequence or in parallel.
• Explainability: Users demand transparency in how tasks are performed and results are derived.
• Efficiency and Resilience: Execution must be optimized for performance while remaining robust against errors.

XMODE directly addresses these challenges by combining a structured task representation with intelligent execution.

Mapping Queries to a DAG

A Directed Acyclic Graph (DAG) serves as the execution blueprint in XMODE, capturing all tasks and their dependencies. The process of mapping a query to a DAG involves several structured steps:

1. Parsing the Query

• Intent Detection: The system identifies the goal of the query. In “Analyze the progression of cancer lesions in smokers over the past year,” the intent includes filtering patient data, analyzing medical images, and visualizing trends.
• Entity and Operation Extraction: Key entities (e.g., “smokers,” “lesions”) and required actions (e.g., retrieval, analysis, visualization) are identified.

2. Task Decomposition

The query is broken into atomic tasks, representing the smallest executable units:

• SQL generation to retrieve patient records.
• Image analysis to detect lesion progression.
• Aggregation and visualization of results.

3. Dependency Resolution

Dependencies between tasks are analyzed:

• Task outputs (e.g., SQL query results) feed into subsequent tasks (e.g., image analysis).
• Independent tasks, such as analyzing multiple images, are marked for parallel execution.

4. DAG Construction

The system organizes tasks into a directed graph:

• Nodes represent tasks.
• Edges capture dependencies, defining the execution order.

For example:

• Node 1: Retrieve patient data using SQL.
• Node 2: Analyze images for lesion progression (executed in parallel for each image).
• Node 3: Aggregate and visualize results.

This structured representation ensures tasks are logically sequenced and ready for optimized execution.

DAG Execution and State Management

The DAG governs task execution, ensuring efficiency and traceability:

Task Execution

• Sequential Execution: Tasks with dependencies (e.g., SQL retrieval followed by image analysis) execute in order.
• Parallel Execution: Independent tasks (e.g., analyzing multiple images) are executed concurrently, reducing latency.

Shared State Management

A centralized state object maintains intermediate outputs:

• SQL results feed directly into image analysis.
• Aggregated lesion data flows into the visualization task.
• This state ensures smooth transitions between tasks and provides full traceability.

Error Isolation

Failures are isolated to specific nodes without affecting unrelated tasks. For example, if SQL retrieval fails, image analysis tasks are unaffected and can proceed once data becomes available.

Dynamic Planning for Robustness

XMODE handles errors and unexpected conditions with dynamic replanning:

Failure Localization

• Failures are pinpointed to specific nodes.
• Example: If SQL retrieval fails due to restrictive filters, the system identifies the issue at the query task.

Replanning

Only the affected tasks are updated:

• SQL query parameters might be adjusted to broaden the results.
• Dependencies in the DAG are recalibrated to reflect these changes.
• Completed tasks remain untouched, ensuring minimal disruption.

LLM-Assisted Reasoning

LLMs play a critical role in diagnosing failures and generating alternative workflows. Them mimicing reasoning capabilities allow the system to adjust dynamically while maintaining logical consistency.

Modular Expert Models and Tools

XMODE integrates a range of specialized tools, each designed for specific subtasks:

1. Text-to-SQL Models: Convert natural language into SQL for structured data retrieval.

2. Image Analysis Frameworks: Extract features or classify patterns in visual data.

3. Visualization Libraries: Create intuitive visual outputs such as trend charts or heatmaps.

4. Aggregation Tools: Summarize or process intermediate results for downstream tasks.

These tools operate independently but interact seamlessly within the shared state. The modular design ensures scalability and extensibility, enabling the integration of new tools without disrupting existing workflows.

Experimental Insights

XMODE was evaluated on two datasets to measure its effectiveness in multi-modal reasoning:

1. Artwork Dataset: Combined metadata and image data to test multi-modal query capabilities.

2. EHRXQA Dataset: Integrated electronic health records with X-ray images for medical analysis.

Performance Metrics

• Accuracy: XMODE outperformed baseline systems like CAESURA and NeuralSQL, demonstrating superior task decomposition and reasoning.
• Efficiency: Parallel execution and optimized workflows reduced latency and computational overhead.
• Explainability: Intermediate outputs and task reasoning provided actionable insights, enhancing trust.

Implications for Multi-Agent Copilots

XMODE’s architectural principles extend seamlessly to multi-agent copilot systems, enabling efficient and transparent query execution across domains.

1. DAG-Based Orchestration

DAGs provide a structured framework for managing complex workflows:

Task Decomposition: Break queries into modular tasks and assign them to specialized agents. In a research copilot, “Summarize the evolution of AI methods for image recognition over the last decade” could translate into:

• Node 1: Retrieve academic papers or key publications (retrieval agent).
• Node 2: Extract insights and trends (summarization agent).
• Node 3: Generate a comparative analysis or timeline visualization (visualization agent).

2. Explainability as a Core Feature: Copilots should provide intermediate outputs and reasoning, enabling users to inspect and refine workflows iteratively.

3. Resilient Error Handling: Dynamic replanning ensures workflows adapt to unexpected conditions without restarting. If one data source fails, the copilot queries alternative sources while preserving progress.

4. Scalability: Modular design allows the integration of new agents and tools without disruption.

Key Takeaways

1. DAGs as a Foundation: Provide structured task orchestration, enabling efficient execution and clear reasoning.

2. Dynamic Replanning Ensures Robustness: Localized error handling minimizes disruptions and optimizes recovery.

3. Explainability Builds Trust: Transparent workflows and intermediate outputs enhance user understanding and system accountability.

4. Modular Tools Enable Scalability: Specialized agents and tools ensure the system can grow and adapt seamlessly.

Wrapping up

XMODE sets a high standard for multi-modal data exploration by integrating DAG-based task orchestration, dynamic planning, and modular toolsets. Its ability to handle complex workflows efficiently and transparently makes it a blueprint for next-generation AI systems. Whether applied to data exploration or multi-agent copilots, XMODE’s approach provides the tools needed to manage complexity while maintaining trust and adaptability. For AI engineers and researchers, this framework offers a practical path to building scalable, explainable, and efficient AI systems.