safety and availability don't reduce to physics formulas the way
power_density/range_fuel/cost_efficiency do -- they're judgment calls
(risk assessment, infrastructure prevalence), and running them through the
same log-normalize() built for physical quantities produced incoherent
results: safety's raw value is already a "0-1" score, and normalizing it
again turned 0.6 into an unexplainable 0.678 that even the LLM reviewing
it could only cite, never justify (see combo 1540). Removed both from
domain_metric_weights (safety from 4 domains, availability from
urban_commuting) and renormalized the remaining weights to sum to 1.0.
Pass 4 now produces one holistic RATING (LOW/MEDIUM/HIGH) alongside the
existing VERDICT, with safety and accessibility folded in as qualitative
considerations feeding that single judgment rather than scored
separately -- not a checklist of independent numbers. New
qualitative_rating column, filterable in the results UI. Also added
domain name/description to the review prompt so the LLM judges a metric
like range against what the domain actually needs (urban_commuting:
1-50km) instead of generic real-world expectations for the platform
category -- confirmed live on a combo where phi4 had called a 396km range
"limited" by comparing to typical aircraft rather than a domain that
needs 1-50km.
Pass 2 is estimator-only now -- self.llm is never consulted there,
reserved entirely for pass 4. Restructured Pipeline.run() from combo-first
to phase-parallel: each pass now runs to completion across every combo
before the next pass starts, rather than walking each combo through all
four passes before the next combo. This surfaced a real bug: domain-
blocked combos (status stays "valid" by design, not "_fail") were
slipping past a naive status-based skip guard and getting silently
re-processed by pass 2. Fixed with a shared dead-combo check that catches
both generic failures and domain blocks correctly.
Also fixes a results-page display bug found while reviewing a live combo:
the per-metric "position" bar showed raw distance from norm_min without
inverting for lower_is_better metrics, so an excellent cost score (near
the good end) rendered as a ~0%, near-empty bar -- looked bad next to its
own 0.99 normalized score.
Validated live against phi4 (real Ollama calls, not mocked): full-domain
phase-parallel run (2,970 combos, estimator-only p2, 1.6s) followed by a
real pass-4 run (111 reviewed, 11m, 0 crashes, 0 null ratings). Two tests
that relied on the old LLM-driven pass 2 to force deterministic outcomes
were updated to test pass 4's verdict-wiring directly instead. All 100
tests pass.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Constraint resolver: aggregate mass/footprint across a combo instead of
pairwise-only checks, treat medium/atmosphere as agreement not supply/demand,
reduce multi-provider checks by best/sum instead of AND-ing every provider,
fail closed on unrecognized mutex values, add a propulsion-viability
(thrust-to-weight) rule. Seed data updated to match (nuclear/solar-sail
footprint floors, water-medium exclusions, explicit ground/gravity providers).
Domain metric units were stored globally per metric name instead of
per-domain, silently corrupting cost_efficiency for every domain but the
first one seeded — fixed with a schema migration.
Stub estimator's cost_efficiency/safety/availability/reliability were a
backwards formula and flat constants; replaced with heuristics grounded in
each entity's thrust_profile/energy_form/infrastructure.
LLM estimate_physics() now receives each metric's unit and expected range
instead of a bare name, fixing wildly miscalibrated estimates traced back to
the prompt's own hardcoded example anchoring the model to the wrong order of
magnitude. Sharpened the safety-estimation and plausibility-review prompts.
Deduped provider parsing logic into llm/parsing.py.
Web pipeline form can now pick an LLM provider per run instead of only via
server env var.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
