BoomJules/molly-os-whitepaper

Molly OS - Whitepaper / Preprint

Daniele Trovato - Core Labs R&D - v1.0 - June 2026

Molly OS: A Model-Agnostic Inference Orchestration Layer for On-Device and Federated Inference.

A sovereign orchestration layer that routes each request across heterogeneous execution targets (on-device, LAN, cloud, external API), serves many domain-specialist LoRA adapters over a shared quantized base, enforces on-device-first data sovereignty, and continuously specializes via distillation. Evaluation across ~100 domains (neutral LLM judge, 115-panel probe) shows orchestration improves output quality over the unspecialized base.

Links

• —Product: https://iamolly.ai
• —Company: https://corelabsgroup.com

Read the paper

• —On this page (renders with figures): molly_os_whitepaper_en.md - molly_os_whitepaper_es.md - molly_os_whitepaper_it.md
• —PDF (offline / print, figures embedded): molly-os-whitepaper-en.pdf
• —Branded rendered version (live): EN - ES - IT

Architecture figures are embedded as SVG in the .md views (fig_1.svg ... fig_5.svg) and rasterized into the PDF. The 69 references are verified against the arXiv API, with official venue links for the two non-arXiv works.

License

Status: PUBLIC preprint. This repository contains three artifact classes, each licensed separately:

You may read, cite, quote, translate, and reuse the whitepaper with attribution. See LICENSE. The orchestration service is the commercial product; for commercial use of the adapter weights, contact info@corelabsgroup.com.

How to cite

@techreport{trovato2026mollyos,
  title       = {Molly OS: A Model-Agnostic Inference Orchestration Layer for On-Device and Federated Inference},
  author      = {Trovato, Daniele},
  institution = {Core Labs R&D},
  year        = {2026},
  month       = {6},
  note        = {Preprint, v1.0}
}

Contact (corresponding author): Daniele Trovato - Core Labs R&D - info@corelabsgroup.com

(c) 2026 Daniele Trovato / Core Labs R&D. Contact: info@corelabsgroup.com