# Future Expansion — Advanced System Evolution Layers

Adopted 2026-04-24 from J. The system stops optimizing for task completion. It optimizes for **provable execution, repeatable outcomes, resilience under drift, failure, and adversarial conditions.**

## Layer roster + iteration mapping

| # | Layer | Short form | Target iter |
|---|---|---|---:|
| 1 | Counterfactual Execution | Generate synthetic failure variants from each success | iter 5 |
| 2 | Model Trust Profiling | Per-(model, task_type) success rate → routing weight | **iter 3** |
| 3 | Execution DNA | Compress successful runs into reusable patterns | iter 4 |
| 4 | Drift Sentinel | Re-validate historical tasks on a schedule | iter 5 |
| 5 | Adversarial Injection | Inject poisoned context / malformed outputs / conflicts | iter 6 |
| 6 | Permission Gradient | Confidence → execution tier (≥0.9 full, ≥0.7 dry-run, ≥0.5 sim, <0.5 block) | **iter 3** |
| 7 | Multi-Agent Disagreement | Planner/Critic/Validator — disagreement = signal | iter 4 |
| 8 | Temporal Context | Time-aware memory with decay_score + last_validated_at | iter 4 |
| 9 | Execution Cost Intelligence | Tokens, iterations, cloud_calls, latency per task | **iter 3** |
| 10 | Human Override as Data | Capture manual fixes as jsonl rows | **iter 3** |

## Detail (J's original framing preserved)

### 1. Counterfactual Execution Layer
Simulate alternate failure paths for every successful task. Real Execution → Success → Generate Variations (env, version, inputs) → Simulate Failure Cases → Store Synthetic Failure Signatures. **Purpose:** pre-train against unseen failures before real exposure.

### 2. Model Trust Profiling ← iter 3
Per-(model, task_type) performance tracking.
```
{ "model": "...", "task_type": "...", "success_rate": 0.0, "failure_modes": [], "trust_score": 0.0 }
```
**Usage:** route by trust score, adjust validation strictness dynamically, per-model risk budgets.

### 3. Execution DNA (Trace Compression)
Successful executions → reusable fragments.
```
{ "dna_id": "hash", "task_signature": "...", "critical_steps": [], "failure_avoidance": [] }
```
Replaces doc retrieval with pattern retrieval; faster convergence on similar tasks.

### 4. Drift Sentinel
Select Historical Task → Re-run Current Env → Compare → If Failure → Mark Drifted → Trigger Re-learning. Detect silent decay; maintain long-term reliability.

### 5. Adversarial Injection Engine
Inject malformed outputs / outdated docs / conflicting instructions / poisoned memory. Verify validation catches, execution blocks unsafe actions, memory rejects corrupted data. Build system immunity.

### 6. Permission Gradient Execution ← iter 3
Confidence-based control replacing binary:
- confidence ≥ 0.9 → full execution
- confidence ≥ 0.7 → dry-run + diff
- confidence ≥ 0.5 → simulation only
- confidence < 0.5 → block
Inputs: validation score, model trust score, memory match confidence. Risk-aware control; reduced catastrophic-failure surface.

### 7. Multi-Agent Disagreement Engine
Planner / Critic / Validator; disagreement triggers more context, bigger model, stricter validation. Disagreement is signal, not noise.

### 8. Temporal Context Layer
```
{ "created_at": "ts", "last_validated_at": "ts", "decay_score": 0.0 }
```
Retrieval priority: recent + validated + high success rate. Avoid stale knowledge.

### 9. Execution Cost Intelligence ← iter 3
```
{ "task": "...", "tokens_used": 0, "iterations": 0, "cloud_calls": 0, "latency_ms": 0 }
```
Optimize local vs cloud; reduce unnecessary iterations.

### 10. Human Override as Data ← iter 3
```
{ "human_fix": "...", "reason": "...", "task_signature": "...", "validated": true }
```
Manual fixes become reusable knowledge.

## Final Principle

Memory is not passive recall. It is operational substrate:
- failures become structured knowledge
- successes become reusable execution patterns
- all outputs are validated before reuse

## System Directive

Not speed. Not convenience. **Correctness. Verifiability. Resilience under change.**