README.md

# Vibecheck Deep Dive — Confidence-Gated Cascade

## Architecture

This is the **meta configuration repo** for the Vibecheck Deep Dive cascade.
It contains no model weights — weights are stored in the component repos below.

## Component Repos

| Component | Repo | Task |
|-----------|------|------|
| Quick Vibe (Tier 1) | `itsLu/mentalbert-v5-source-aware` | Cooperative source-aware 8-class MentalBERT |
| Pathway A (Tier 2) | `itsLu/mentalbert-v5-specialist-a` | CSSRS-pure 6-class specialist |
| Pathway B (Tier 2) | `itsLu/longformer-v5-crisis-evidence` | Binary crisis-evidence Longformer |

## Routing Logic

```python
ROUTE_TO_DEEP_DIVE = (
    top1_prob < 0.65
    or (top1_class in {Depression, Suicidal} and margin < 0.2)
)
# Pass-through: Normal and Directed Aggression always use Quick Vibe
```

## Fusion Gate

```python
crisis_boost = alpha * pathway_b_crisis_prob  # alpha=1.5
fused_logits[Suicidal]   += crisis_boost
fused_logits[Depression] -= 0.5 * crisis_boost  # mass conservation
final = softmax(fused_logits)
```

## Load Pattern

```python
import json
from huggingface_hub import hf_hub_download

cascade_cfg = json.load(open(hf_hub_download('itsLu/vibecheck-deepdive-cascade', 'cascade_config.json')))
pathway_a   = load_specialist_a(cascade_cfg['pathway_a_repo'])
pathway_b   = load_specialist_b(cascade_cfg['pathway_b_repo'])
result = cascade_predict(text, quick_vibe, pathway_a, pathway_b, cascade_cfg)
```

## Test Metrics

| Metric | Value |
|--------|-------|
| Accuracy | 84.82% |
| F1 Macro | 0.8558 |
| Total bleed | 1179 |
| Dep→Sui | 482 |
| Sui→Dep (critical) | 697 |
| % routed to Deep Dive | 12.2% |