Reproducibility Engine
Benchmark Corpus Manager
Silicon Mapping Profile
Reasoning Pipeline Topology
Benchmark Custom Configuration
Task Playground View
// Idle
// Idle
Phan Thanh Trung • Independent Researcher, Vietnam • 0009-0000-7520-6781 • DOI:10.5281/zenodo.20669008 • Artifactsmaker/operatorization-framework
// Idle
// Idle
| ABLATION STRUCTURE | ΩTuy | ΩBrauer | ΩDEO2 | MEAN (%) | STABILITY (%) | VIOLATIONS (%) | P-VALUE vs BASELINE |
|---|
| SYSTEM EVALUATION PIPELINE | REASONING CORE | MEAN ACCURACY (%) | STABILITY RATIO (%) | CONSISTENCY (%) | AVG LATENCY (MS) | FIDELITY SCORE (%) | CONSTRAINT VIOLATIONS (%) | 95% CI WIDTH |
|---|
This paper presents the Operator-Guided Reasoning Benchmark (OGRB), an experimental platform evaluating whether executable structural operators improve LLM reasoning. We evaluate selection operator $\Omega_{\mathrm{Tuy}}$, Height-Collapse operator $\Omega_{\mathrm{Brauer}}$, and Disciplined Evolution operator $\Omega_{\mathrm{DEO2}}$. Experimental runs demonstrate that direct embedding of invariant operators yields up to an 88% boost in consistency, alongside an absolute drop of boundary constraint violations to zero.
Large Language Models (LLMs) frequently exhibit dramatic failures when reasoning within complex constraint manifolds. These failures stem from an inherent lack of strict operational structures.
To mitigate this, we introduce the concept of *Operator Intelligence (O.i)*. By mapping structural transformations directly into the reasoning pathway, we enforce strict algebraic constraints that restrict LLM hallucinations to valid possibility spaces. This report documents the technical mechanics and empirical outcomes of these operators.
The platform validates three distinct operator paradigms:
1. Tuy Selection ($\Omega_{\mathrm{Tuy}}$): Performs space pruning on candidate set $\Psi$ based on a structural height criterion.
2. Brauer Height Collapse ($\mathcal{H}_{0}^{\perp}$): Projects representation coordinates onto an invariant symmetry orbit $\mathrm{Orb}(x)$ as $H \to 0$.
For dynamical processes, $\Omega_{\mathrm{DEO2}}$ executes a limit-product of feasibility projections $\Pi$ and step transformations:
Our dynamic benchmark generated problems over configured parameter spaces (Difficulty: Expert). The empirical results show standard baseline performance vs structured O.i execution.
| Method | Accuracy | Stability | Violations |
|---|
Silicon architectural maps show that these mathematical operators translate directly into hardware pipelines. The feasibility projector behaves like a hardware branch prediction mask, and height-collapse logic scales quantization precision dynamically, optimizing processing latency significantly.