Observation: Deterministic structured reasoning inside ChatGPT (FRR Runtime experiment) #198
Description
Hi Google DeepMind team,
This is an observation from a controlled experiment on LLM behavior under
strong structural constraints. I’m sharing it here because the phenomenon
seems relevant to structured, multi-stage reasoning systems such as Graphcast.
Summary
I tested whether a general-purpose LLM (ChatGPT) can be forced to operate as a
deterministic structured runtime without external tools, APIs, or model modifications.
To do this, I built a small “Flight Readiness Review (FRR) Runtime” consisting of:
- a fixed 8-stage pipeline
- strong format constraints
- strict schema-only output
- enforced subsystem arbitration
- counterfactual reasoning
- rejection of free-form output
Under these constraints, the model exhibited reproducible deterministic behavior:
same input → same structure → same decision, across multiple runs.
Why I think this may interest you
Graphcast and related DeepMind work explore structured prediction,
multi-stage computation, and controllable reasoning.
The FRR experiment suggests that LLMs can mimic deterministic,
multi-step computational graphs purely via structural constraints, without tools.
This includes:
- stable intermediate representations
- stable factor vectors (F1–F12)
- stable subsystem arbitration
- stable final decision
- measurable coupling between variables
- zero drift across executions
This emergent determinism may have implications for:
- agent architectures
- constrained reasoning
- LLM-as-runtime behavior
- multi-step pipelines inside a single forward pass
Demo (3 minutes)
GitHub (prompt-only, safe)
https://github.com/yuer-dsl/qtx-frr-runtime
Closing
Not a feature request — only an observation that may provide a useful test case
for understanding controllable reasoning under structural constraints.
If useful, I can share simplified prompts or reduced test cases for reproduction.
Thanks!