# 🤖 VQuant: An Agentic Large Language Model Architecture for Automated Quantitative Financial Analysis and Document Generation

UQO Working Paper No. 13 — Reasoning-Augmented Tool Orchestration, In-Loop Numerical Computation, and Multi-Format Reporting

TL;DR — VQuant is a deployed web platform that turns a natural-language request into a quantitative financial report in which every number comes from live data retrieval or auditable code rather than from the model. This is a design-science system description, audited line by line against the platform's source code (spbgit vquant.git, commit e2a148a, 2026-10-02): a recursive, batched, streaming tool-calling loop over 245 schema-defined tools (two market-data providers, 249 typed wrappers over 233 endpoint paths; three web-research services; a Python sandbox pre-loading 29 packages), a three-stage context-management procedure (3,000/15,000-char truncation at execution, 5,000-char re-send cap, adaptive summarisation at 10^5 tokens keeping the last 5 messages), a single-call computation convention, and a document pipeline (PDF via Puppeteer+KaTeX, DOCX via Pandoc, Beamer via a second model call + two pdflatex passes, live-formula XLSX). No empirical evaluation is reported (no accuracy, hallucination, latency or cost measurements); a pre-registered evaluation protocol is specified as future work.

# 📌 Status

# 🎯 What this paper does

Large language models are fluent in financial discourse, but fluency does not guarantee numerically rigorous, reproducible or citable analysis. This paper describes the architecture of VQuant, a platform that couples a reasoning-capable LLM with a deterministic execution substrate, under the principle the model plans and synthesises, auditable code computes. It is written as a design-science contribution (Hevner et al. 2004; Gregor & Hevner 2013): a situated implementation plus a demonstration, with the evaluation step specified but not performed.

Four mechanisms are documented at implementation level, with their actual constants, and audited against the code (Appendix E maps every claim to a source file):

1. an agentic orchestration loop over 245 tools (recursive tool-calling, batches of 5 with 500 ms spacing, tool-use/tool-result pairing, prompt-level routing by request complexity);
2. a three-stage context-management procedure (per-result truncation at execution, re-send cap, adaptive summarisation under a 10^5-token budget) and a token-accounting correction (max of provider count and a local BPE count — an approximation, not an exact count);
3. a single-call computation convention (data → estimation → figures in one sandboxed Python execution; process-isolated child, 90 s timeout, figure sweep at 150 dpi);
4. a document pipeline (PDF, DOCX, Beamer, live-formula XLSX from one Markdown artifact).

The revision corrected several statements of v1.0 that the code audit contradicted: tool count, tokenizer, reachability of the pre-built numerical routines (only the option pricer is in the catalogue), the model layer (now multi-provider), and three features (SSO, daily caps, bring-your-own-keys) that do not exist in any committed version.

# 🏆 Headline results

There are no empirical results. The table lists verified design facts (commit e2a148a).

# 🗂 Repository layout

vquant-llm-quant-platform/
├── paper/                    # main.tex + sections/ + figures/ (TikZ diagrams) + tables/ + appendix/ → main.pdf
│   ├── figures/              # architecture, loop, context, sandbox, streaming, docpipeline (TikZ)
│   └── tables/               # tools (generated by docs/verify/audit_vquant_code.py), models, events, robustness (= design rationale)
└── docs/                     # REVIEW-REPORT.md, CHANGELOG-REVISION.md, main_pre-revision-20261017.pdf, verify/ (audit script + JSON)

No src/, scripts/ or data/: the paper has no empirical pipeline. docs/verify/audit_vquant_code.py <clone of vquant.git> regenerates paper/tables/tools.tex and the code facts used in the text.

# 🚀 Quick start

cd paper && make      # latexmk → main.pdf (pdflatex + bibtex, TikZ diagrams compiled inline)

# 🗃 Data

Not applicable. The paper describes a software system; the platform itself (TypeScript/Python) is maintained in the spbgit repository vquant.git and deployed on the MacLustr cluster.

# 🔬 Methodology

1. Architecture description — components, data layer, model layer, as implemented at commit e2a148a (§3).
2. Mechanism specification — transport, recursive loop (Algorithm 1), batching, routing, three-stage context management (Algorithm 2), token correction, single-call convention (§4).
3. Execution substrate, document pipeline, illustrative walk-through — no numbers reported (§5).
4. Design rationale, threats to validity, reproducibility, proposed evaluation protocol (not executed) — task suite, metrics (incl. unsupported-number rate), ablations, baselines, controls (§6).
5. Appendix — prompt structure, persistence schema, numerical closed forms, event types, and the claim-by-claim audit table against the code.

# 🖼 Figures & tables inventory

Tables (paper/tables/*.tex): tool catalogue (generated), model layer, streaming events, design rationale; appendix tables: persisted entities, code audit (longtable).

# ⚠️ Limitations

• Design-science description only: no evaluation (accuracy, hallucination rate, latency, cost, ablations, baselines). The protocol of §6.6 is pre-registered in the text but has not been run.
• Describes one commit (e2a148a, 2026-10-02) of a system that keeps changing; counts and constants are facts about that commit.
• Outputs are not strictly reproducible (temperature 1, no seeds, model-authored code); the design is.
• Known defects surfaced by the audit and not yet fixed in the code: no recursion-depth bound; single $3/$15 price schedule for all models; six numerical handlers unreachable (not in the catalogue) while the prompt still advertises two of them; the sandbox is process isolation, not a security boundary.
• Reproducing the platform requires the private vquant.git code base and API keys.

# 📝 Citation

@techreport{boucher2026vquant,
  author      = {Boucher, Simon-Pierre},
  title       = {VQuant: An Agentic Large Language Model Architecture for Automated Quantitative Financial Analysis and Document Generation},
  institution = {Universit\'e du Qu\'ebec en Outaouais, D\'epartement des sciences administratives},
  type        = {Working Paper},
  number      = {13},
  year        = {2026},
  month       = {October},
  note        = {Version 1.1}
}

# 👤 Author & contact

Simon-Pierre Boucher — Département des sciences administratives, Université du Québec en Outaouais (UQO), Gatineau, QC. 📧 simon-pierre.boucher@uqo.ca

# 🗒 Notes

• Revision 2026-10-17 (v1.1): full editorial-board review — docs/REVIEW-REPORT.md (diagnosis, methodology verdicts, literature, maturity) and docs/CHANGELOG-REVISION.md (every corrected statement with its evidence in the code, references added, items not executed). Pre-revision state: git tag pre-revision-20261017, docs/main_pre-revision-20261017.pdf. Bibliography 20 → 46 entries, all verified (Crossref, arXiv, conference proceedings; one entry flagged as withdrawn by its authors). Sections 5 and 6 were renamed from "Results"/"Performance, Reliability, and Security" to "Execution Substrate … and an Illustrative Walk-Through" and "Design Rationale, Threats to Validity, and a Proposed Evaluation".
• Numbering: first circulated as “Working Paper No. 10” (June 2026); renumbered 13 on 2026-10-17 because No. 10 is The Assessment Gap in Quebec (see Recherche-UQO/HARMONISATION.md).
• Cover date normalised from “June 13, 2026” to “June 2026” (harmonisation), then “October 2026” (revision).
• The VQuant platform code is not in this repository (spbgit vquant.git); docs/verify/ contains the audit script and its JSON outputs, not the code.