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Move issues (01–29) and PRD from .scratch/distributed-inference-network/ into docs/issues/ and docs/. Update ralph_progress.py DEFAULT_PRD path and rewrite docs/agents/issue-tracker.md to reflect the new layout. The distributed_inference_network.egg-info/docs/ mirror is a build artifact already covered by *.egg-info/ in .gitignore — not committed. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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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]+1.0floor: models with no traffic still compete by coverage+0.01floor: 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_assigncomputes score per model and returns the highest- Demand uses combined stats (local + peer slices)
price_per_token: 0.0present in response- Test: high-demand low-coverage model beats low-demand high-coverage model
python -m pytestpasses