Von

# Von

**An Open-Source, Non-Autoregressive System One Decision Model.**  
*Calibrated discrete, probabilistic, and ordinal inference in sub-25ms.*

[](https://huggingface.co/wfzyx/von-1.0)
[](https://opensource.org/licenses/Apache-2.0)
[](https://www.python.org/)
[](https://www.typescriptlang.org/)

---

## Overview

Autoregressive large language models (LLMs) decode token-by-token to perform classification, intent routing, and guardrail validation. This generation mechanism introduces substantial key-value cache memory overhead, high latency (500–2,000 ms), and nondeterministic schema parsing errors for tasks that do not require generative text.

**Von** implements the **System One** computational paradigm: reflexive, parallel, deterministic, and statistically calibrated decision-making. Operating entirely in-process or via an HTTP server, Von evaluates arbitrary discrete and continuous criteria directly over input state in a single forward pass without autoregressive text generation.

### Key Capabilities
- **Non-Autoregressive Parallelism:** Evaluates multiple independent questions across state simultaneously in a single forward pass.
- **SOTA Empirical Accuracy:** **91.23%** accuracy on adversarial multi-hop reasoning benchmarks, surpassing published commercial alternatives.
- **Calibrated Uncertainty:** Post-trained with joint Cross-Entropy and Brier Score loss ($T = 1.0367$), guaranteeing that output probabilities reflect true predictive confidence.
- **Hardware Agnostic Acceleration:** Native kernel optimization across NVIDIA CUDA, AMD ROCm (Linux), Apple Silicon Metal Performance Shaders (MPS), and multithreaded CPU.
- **Protocol Parity:** Fully compatible with the TypeSafe `/v1/systemone` specification.

---

## Empirical Benchmark

Von is evaluated across two