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Issue files (.scratch/issues/20-29): retrospective specs for all work done in the current sprint — hardening, route-timeout, start-layer protocol, heartbeat stats, availability map, rolling RPM, smart assignment, throughput routing, routing tests, relay outbound client. ADRs (docs/adr/0011-0014): 0011 — Auto-shard from memory budget and tracker network assignment 0012 — X-Meshnet-Start-Layer overlapping shard execution protocol 0013 — Rolling RPM statistics, smart assignment scoring, throughput routing 0014 — Relay outbound client for NAT/internet pipeline hops prd.json: US-020 through US-029 added, all marked done. ralph_progress.py now shows 29/29 complete (100%). 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