# US-026 — Smart model assignment via demand×coverage scoring

Status: done
Priority: Medium
Stage: Implemented

## Context

`/v1/network/assign` currently picks the model with the largest uncovered shard gap,
ignoring traffic. A model serving 1000 RPM at 60% coverage is far more valuable to fill
than a zero-traffic model at 50% coverage.

## Scoring formula

```
score = (demand_rpm + 1.0) × (coverage_deficit + 0.01)
```

- `demand_rpm`: combined RPM from `_StatsCollector.get_combined_stats()`
- `coverage_deficit`: fraction of model layers with zero node coverage, in [0.0, 1.0]
- `+1.0` floor: models with no traffic still compete by coverage
- `+0.01` floor: fully-covered models still have a non-zero score if they have traffic

`price_per_token: 0.0` reserved in the response for future billing integration.

## Acceptance criteria

- [ ] `_handle_network_assign` computes score per model and returns the highest
- [ ] Demand uses combined stats (local + peer slices)
- [ ] `price_per_token: 0.0` present in response
- [ ] Test: high-demand low-coverage model beats low-demand high-coverage model
- [ ] `python -m pytest` passes
