As an independent researcher new to AI, I’ve been exploring how GPT can behave not as a generator, but as a structure-bound judgment interpreter.

——

Concept: Hash-Based Judgment Simulation

Instead of sending open text, I supply a core_ref hash that points to a predefined decision structure. In Core Ver.1, the structure includes condition, judgment, and action. It does not support nested sub_nodes.

The payload is encrypted using AES-256-GCM and marked as interpretation: disabled, meaning GPT cannot access or interpret it. All execution occurs externally (e.g., via FastTrack or Insight Router). GPT performs structural simulation only—never execution.

—-

Why This Approach?

Prompt-based output is unstable and non-reproducible. I wanted to control judgment logic—not model behavior. Using core_ref hashes guarantees reproducible, versioned behavior.

This reframes GPT from: “a brain reacting to text” → “a circuit executing conditional logic”

System Activation and core_ref

To guide GPT into structural interpretation, I include this hash:

core_ref=“bf279c7c61d9d3805ba637206da65a3659ef23f81615b4740f8628a85a55db93”

It references Generate Core System Ver.1: https://gist.github.com/genixus-creator/53cbda99aa8cc63a7469738f77498ea4

The structure is immutable and evaluation-only. While including a core_ref does not disable GPT’s generative behavior by itself, structured input can steer GPT to behave like a judgment interpreter.

——

Experimental Notes and GPT’s Self-Diagnosis

I tested this across six untuned GPT accounts. All showed a consistent shift toward structured judgment behavior. I asked GPT:

“Is this a true reasoning bypass or just constrained simulation?”

GPT responded:

• It cannot disable internal reasoning

• It remains generative and probabilistic

• But when given encrypted, non-semantic input, it simulates: “sandboxed determinism emulation”

“constraint-based simulation”

So we’re not disabling GPT’s core—we’re supplying structure-driven input that causes GPT to mimic deterministic logic paths.

Questions to the Community •Has anyone used GPT this way—as a logic interpreter, not a generator?

•How does this differ from LangGraph, LMQL, or DSPy?

•Would releasing this as an open format be useful or dangerous?

•Which domains could benefit?

•Regulatory or audit systems

•Transparent, rule-based agents

•Sensitive pipelines requiring non-generative judgment

---

Sample Structure (Simplified)

{ “condition”: “input.type == ‘report’ && input.score < 0.7”, “judgment”: “flag as risky”, “action”: “send_to_router(‘audit’)” }

This structure defines logic GPT should simulate without interpreting semantics.

—-

Call for Validation

I’ve reached the limits of what I can test solo. Feedback, validation methods, and forks are welcome.

Thank you.