# Research Agenda

KORA is an execution architecture grounded in structural discipline.

It is not complete.

This document defines open research directions implied by the architecture.

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## 1. Decomposition Quality Modeling

Current decomposition assumes:

- Deterministic tasks can be identified reliably.
- Reasoning components can be isolated cleanly.

Open questions:

- How do we quantify decomposition accuracy?
- What is the optimal task granularity?
- Can decomposition be learned?
- What is the tradeoff between fragmentation and coherence?

Future work may involve:

- Task boundary prediction models
- Automated deterministic classification
- Decomposition cost modeling

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## 2. Structural Overhead Optimization

Structural overhead O must remain bounded.

Open research:

- What is the asymptotic overhead behavior?
- How does overhead scale with graph depth?
- Can DAG validation be optimized sublinearly?
- What is optimal aggregation strategy?

Performance modeling must be refined empirically.

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## 3. Latency Variance Modeling

Inference variance drives unpredictability.

Research questions:

- Can structured execution reduce tail latency?
- How does P affect latency distribution shape?
- Can deterministic pruning stabilize high-percentile latency?

Statistical modeling of latency distribution is required.

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## 4. Adaptive Routing Policies

Routing decisions are currently policy-based.

Open questions:

- Can routing be dynamically optimized?
- Can cost models update in real time?
- Can reinforcement learning optimize routing decisions?
- What are the stability risks of adaptive routing?

Routing must remain governed, not opportunistic.

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## 5. Budget-Aware Model Training

Current models are unaware of budget constraints.

Research direction:

- Can models be trained with budget awareness?
- Can token generation terminate semantically rather than syntactically?
- Can models predict required reasoning scope before generating?

Budget-native reasoning remains unexplored.

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## 6. Decomposition-Native Foundation Models

DNFM remains speculative.

Open questions:

- Can models consume structured DAG inputs?
- Can reasoning be node-scoped?
- Does decomposition reduce hallucination?
- Can internal task segmentation improve correctness?

Empirical model design research is required.

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## 7. Distributed Consensus and Trust

Distributed execution introduces trust challenges.

Research topics:

- Trustless task verification
- Cross-node schema validation consistency
- Byzantine task execution tolerance
- Deterministic consensus under partial failure

Distributed reasoning must preserve structure.

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## 8. Security Surface Expansion

Atomic task execution changes threat model.

Open areas:

- Task-level injection containment
- Model hallucination propagation analysis
- Retry abuse modeling
- Routing manipulation attack vectors

Security research must evolve with structure.

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## 9. Economic Scaling Dynamics

Break-even modeling is linear.

Open research:

- Nonlinear token pricing models
- Real-time dynamic cost optimization
- Multi-model cost tradeoffs
- Edge-device energy-aware routing

Economic modeling must incorporate heterogeneity.

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## 10. Observability Compression

Telemetry introduces overhead.

Research questions:

- How much telemetry is necessary?
- Can sampling preserve falsifiability?
- Can structural metrics be compressed?
- Can anomaly detection detect structural drift?

Observability must remain efficient.

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## 11. Formal Verification

KORA relies on invariants.

Open research:

- Can DAG correctness be formally verified?
- Can budget enforcement be statically proven?
- Can schema invariants be formally encoded?
- Can inference isolation be mathematically modeled?

Formal verification may strengthen architectural guarantees.

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## 12. Limits of Structure

Structure is powerful.

But structure may:

- Over-constrain creative reasoning
- Fragment semantic coherence
- Increase coordination overhead

Research must identify domains where decomposition harms performance.

Conditional superiority must remain honest.

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## 13. Empirical Dataset Expansion

Current benchmarks are minimal.

Future validation must include:

- Large-scale datasets
- Multi-domain evaluation
- Distributed simulation
- Failure injection campaigns

Structural claims require scale validation.

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## 14. Theoretical Framing

Open theoretical questions:

- Is inference reflexivity inevitable in prompt-native systems?
- Can structured execution reduce entropy growth in reasoning?
- Does decomposition reduce hallucination probability?
- Is there a lower bound on structural overhead?

These are foundational research questions.

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## 15. Long-Term Vision

If structure proves beneficial under diverse workloads:

- Execution architectures may become standard.
- Foundation models may integrate structural awareness.
- Compute neutrality may reduce centralization pressure.

If not:

KORA's hypotheses must be revised.

Architecture must adapt to evidence.

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## Closing Position

KORA is not finished.

It is a structural hypothesis under continuous examination.
<br>The research agenda defines what remains unknown.

**Structure must evolve under evidence.**
