---
title: VQuant: An Agentic Large Language Model Architecture for Automated Quantitative Financial Analysis and Document Generation: Reasoning-Augmented Tool Orchestration, In-Loop Numerical Computation, and Multi-Format Reporting
url: https://www.ml-quant.com/papers/ssrn/7650999/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-10-23
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 7650999
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7650999
featured: 2026-10-23
citations: unknown
topic: ML & AI Methods
---


# VQuant: An Agentic Large Language Model Architecture for Automated Quantitative Financial Analysis and Document Generation: Reasoning-Augmented Tool Orchestration, In-Loop Numerical Computation, and Multi-Format Reporting

A platform couples a language model with deterministic execution to turn natural-language requests into auditable reports where every figure is from live data or reproducible code, not model hallucination.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7650999
- Identifier: SSRN 7650999
- Released: 2026-10-22
- First featured: Quant Letter No. 136 (2026-10-23): https://www.ml-quant.com/issues/2026-10-23/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods
- Authors: Simon-Pierre Boucher

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