A Deterministic Runtime for Grok-1: Static Plan Compiler + 100× Reproducibility Test

[yuer-dsl]

Hi everyone 👋

I’ve been experimenting with ways to make multi-step Grok-1 reasoning fully deterministic,
especially for cases where reproducibility, auditing, or long-term replayability matter.

To explore this, I built a small open-source project:

➡ deterministic-grok-runtime
https://github.com/yuer-dsl/deterministic-grok-runtime

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🔒 What it does

This project compiles a YAML reasoning chain into a static, deterministic execution plan:

• temperature = 0
• top_p = 1.0
• penalties normalized
• stable prompt expansion
• signed with SHA256

The goal is simple:

Same input → same reasoning route → same output hash.
Every time. 100×.

It’s a minimal demonstration of how probabilistic LLM behavior can be wrapped inside a deterministic layer.

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🧩 Features

• Static reasoning-chain compiler (.yaml → .compiled.yaml)
• Automatically normalizes sampling params
• Adds metadata + SHA256 signature
• Minimal deterministic runtime (backend-agnostic)
• 100× reproducibility stress test
• Fully open source, no proprietary components

Everything is framework-neutral; the runtime currently uses a small stub but can be connected to:

• xAI Grok API
• local Grok execution
• or any LLM backend

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🧪 What the reproducibility test does

The script runs the same compiled plan 100 times:

python tests/reproducibility_test.py

Expected:

Run 001: abc123...
Run 002: abc123...
...
Run 100: abc123...

✓ 100% reproducible

If any hash differs, it flags nondeterministic behavior.

📦 Example included

A five-step hypothetical scenario chain is provided as a demo (not financial advice).
It shows how llm → tool → llm → llm chains can be compiled into a static plan.

🎯 Purpose of this project

This is not a critique of Grok-1.
Instead, it’s an engineering exploration of:

reproducibility

deterministic evaluation

reliable multi-step pipelines

debugging & auditing chains

research validation

I built it to better understand how static plans can complement powerful probabilistic models.

If anyone has ideas, feedback, or wants to experiment together, I’d love to hear your thoughts!

Thanks for reading 🙏