# KORA: An Inference-First Execution Architecture

## Abstract

KORA is an execution architecture that separates task necessity from probabilistic reasoning. Modern AI systems are largely inference-reflexive: they invoke large language models for every request, regardless of structural complexity. This design leads to unnecessary computation, unpredictable latency, and centralized infrastructure dependence.

KORA introduces three architectural principles:

1. Determinism before inference.
2. Native task decomposition.
3. Compute neutrality through heterogeneous routing.

This paper formalizes the structural critique, presents the execution model, derives break-even conditions, and outlines a research direction toward Decomposition-Native Foundation Models.

---

## 1. Introduction

Large language models have demonstrated impressive reasoning capability. However, most production systems integrate them in a reflexive manner. Every request triggers model invocation. The architecture does not discriminate between trivial and reasoning-intensive tasks.

This pattern is convenient but structurally inefficient.

The core issue is not model capability. It is invocation discipline.

KORA proposes an execution architecture that enforces structure before reasoning.

---

## 2. Structural Critique

In inference-reflexive systems:

```mermaid
flowchart LR
    A[Input] --> B[Prompt]
    B --> C[Model]
    C --> D[Output]
```

<br>
Two logically distinct decisions are collapsed:
<br>

1. Is reasoning necessary?
2. What is the reasoning result?

When necessity is not evaluated, deterministic operations are solved probabilistically. <br>This increases cost and latency while centralizing compute.

The critique is architectural, not model-level.

---

## 3. Architectural Principles

### 3.1 Determinism Before Inference

Trivial or rule-based tasks execute without invoking a model.
<br>This reduces hallucination risk and preserves bounded latency.

### 3.2 Native Decomposition

Complex requests are decomposed into atomic tasks.

Each task is:

- Explicitly typed
- Budget-bound
- Schema-validated
- Independently executable

Reasoning becomes compositional.

### 3.3 Compute Neutrality

Tasks can route across:

- CPUs
- Local lightweight models
- Remote LLM APIs
- Future NPUs

Heterogeneous compute is architectural, not incidental.

---

## 4. Execution Model

```mermaid
flowchart TD
    A[Request]
    A --> B[Task Construction]
    B --> C[DAG Validation]
    C --> D[Deterministic Execution]
    D --> E{Inference Required?}
    E -->|No| F[Aggregation]
    E -->|Yes| G[Budgeted Model Invocation]
    G --> H[Schema Validation]
    H --> F
    F --> I[Final Output]
```

<br>
Execution is graph-based, not prompt-based.

The model is one stage in the lifecycle.

---

## 5. Break-Even Formalization

Let:

| Symbol | Meaning |
|--------|----------|
| T      | Total requests |
| P      | Deterministic proportion |
| C_m    | Cost per model call |
| O      | Structural overhead |

Baseline cost:

C_baseline = C_m * T

Structured cost:

C_kora = C_m * (1 - P) * T + O * T

KORA is beneficial when:

P * C_m > O

This inequality is empirically testable.

---

## 6. Benchmark Evidence

In controlled experiments:

| Metric      | Direct | KORA | Reduction |
|-------------|--------|------|-----------|
| LLM Calls   | 2      | 1    | -50%      |
| Tokens In   | 359    | 225  | -37%      |
| Tokens Out  | 121    | 85   | -30%      |

Output equivalence was preserved.

This demonstrates that structured execution reduces invocation frequency without degrading capability.

---

## 7. Budget Governance

Model invocation must satisfy explicit constraints:

- max_tokens
- max_time_ms
- max_retries

Budget is contractual.

Unbounded inference is structurally disallowed.

---

## 8. Decentralized Compute Implications

Monolithic prompts require centralized accelerators.

Decomposed tasks enable routing:

```mermaid
flowchart LR
    A[Task Graph] --> B[CPU]
    A --> C[Local Model]
    A --> D[Remote Model]
```


<br>
Because reasoning units are smaller and bounded, routing becomes flexible.

CPU becomes first-class rather than fallback.

---

## 9. Falsifiability

KORA makes a conditional claim:

If deterministic proportion P is meaningful and structural overhead O is controlled, structured execution reduces cost without reducing correctness.

Failure conditions include:

- Overhead exceeding savings
- Decomposition degrading output quality
- Budget governance increasing latency excessively

KORA remains valid only under empirical verification.

---

## 10. Toward Decomposition-Native Foundation Models

Current models are prompt-native.

Future models may accept structured task graphs directly.

```mermaid
flowchart TD
    A[Task Graph]
    A --> B[Decomposition-Native Model]
    B --> C[Structured Outputs]
```

<br>
Such models could internalize task boundaries and budget awareness.

KORA prepares the architectural substrate for this evolution.

---

## 11. Conclusion

KORA does not attempt to outperform models.

It restructures how they are invoked.

The central thesis is simple:

**Structure precedes scale.**

By separating necessity from reasoning, enforcing budget governance, and enabling heterogeneous routing, KORA transforms inference from reflex into discipline.

The architectural shift is modest in implementation but fundamental in implication.
