> For the complete documentation index, see [llms.txt](https://aro-1.gitbook.io/aro/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aro-1.gitbook.io/aro/aros-tech/data-flow-and-multimodel-reasoning.md).
# Data Flow & Multimodel Reasoning
**User Request**
* Example: “What is the long-term analysis for Bitcoin $BTC”
* Sent via **chat** , **UI dashboard, API call.**
**AI Research Orchestrator Receives the Task**
* Classifies the query (market sentiment, price prediction, or code interpretation).
**Multimodel Reasoning Layer**
* **Model Selection**: Chooses o1/deepseek r1 for textual reasoning, or Grok for advanced sentiment analysis, etc.
* **Collaboration**: Multiple LLMs can be used concurrently for complex, multi-part questions.
**Tool Interactions**
* The orchestrator invokes relevant tools in the **Orchestration Intelligence** (e.g., on-chain, sentiment, or financial analytics) for data gathering or specialized computations.
**Aggregation & Finalization**
* Partial outputs (from LLMs + Orchestration Intelligence) are **merged** into a cohesive final answer.
* Logic checks by AI Research Orchestrator ensure data consistency and integrity.
**Dataset Logging**
* Key outputs are **archived** for historical reference and to refine future model performance.
**Output Delivery**
* Final insights appear in web app chat reply, an interactive dashboard, PDF/HTML report, or as a structured **API** response.
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# Agent Instructions
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## Querying This Documentation
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