Vons
Plan globally. Decide locally. Keep the host in control.
Compact local decision models for frontier-agent workflows.
Kwangseob Ahn · INLEVEL9 / SEJONG UNIV. · oswarld@inlevel9.com
Source preview
This repository contains research source and documentation. It does not contain
pretrained model weights, tokenizer assets or datasets. Hosted inference and
from_pretrained loading are not available from this source preview.
Vons studies Direct and Diffusion candidate scoring with abstention. The host owns execution, permission and consent. Model output never authorizes an action. English-first operation and a model asset budget under 64 MiB are design targets; they are not general quality or complete browser-package guarantees.
See the model card, the TypeScript SDK, the paper and Tech Report, and release preparation.
Read, try and contribute
This source preview is distributed through INLEVEL9/Vons. Source and contribution reports belong in inlevel9-com/Vons. The reviewed paper and Tech Report are included as PDF downloads below. The software remains a 0.1.0 research preview. No pretrained weights or working Space are announced.
Follow the community guide for the no-weight walkthrough, experience and reproduction reports, and the contribution guide for review expectations. The paper's arXiv submission record is available. User reports are not independent research validation.
Maintainers can use the Hub upload guide to prepare an organization card, source preview and later a separately validated Space.
Paper and Tech Report
| Publication | Download |
|---|---|
| Vons: A Compact, Host-Controlled Decision Component for Agent Workflows — Preprint v1.1 | PDF · 21 pages |
| Vons: Compact Decision Models for Frontier-Agent Workflows — Technical Report v1.1 | PDF · 29 pages |
Articles and original figures use CC BY 4.0. Their version, SHA-256 digests and verification scope are in the publication index. These preprints preserve negative results, missing measurements and the boundary between frozen v1 evidence and the separately labelled post-freeze v2 handoff. The paper uses arXiv submission number 8127259. The v1.1 PDF and metadata were processed successfully on 2026-09-25 and submitted on 2026-09-26. The arXiv account currently reports on hold for moderation. This tracking number is not a public arXiv article identifier. No public announcement or peer-review acceptance is claimed.
Chrome Web Store version 0.1.0 passed review and is publicly available as Vons — Local Decisions, verified on 2026-09-25. Store availability is not evidence of model quality, scientific validity or production readiness.
Local use
python3 -m venv .venv
. .venv/bin/activate
python -m pip install -e '.[dev]'
python -m vons.cli generate-smoke --output data/generated/smoke.jsonl
python -m vons.cli validate-data --input data/generated/smoke.jsonl
The source export includes the research tools and pinned pilot configurations. Obtain and review upstream assets separately before running model experiments. Raw restricted benchmark data and private evidence are not redistributed here.
Terms
Source is distributed under Vons Community and Commercial License 1.0: qualifying noncommercial use is free; enterprise and other commercial use require a separate written agreement. Contact oswarld@inlevel9.com for commercial terms. This is source-available software, not OSI-approved open source.
The paper and Tech Report are distributed under CC BY 4.0. This permits commercial article reuse with attribution; software rights are separate. Third-party assets retain their own terms. See licensing scope. This source release does not establish research validation or a commercial customer agreement.