Measured over all eleven saves with state_checksum, not derived:
* the reference pair posts TWO events (turn1->turn2) and THREE (turn2->turn3),
and only two players in the whole corpus ever hold an event -- the human and
the one AI empire that owns colonies. The dormant shadow empires pick research
targets every turn and still post nothing.
* order within a player is readable off the ids: the build pass posts before the
research pass. Ids are per player, so no cross-player order is observable.
* the event type is the EvImg string and it is composed at run time --
EVENT_ENEMY_INCOMING_Human carries a species suffix, so the type space is not
an enumeration.
Two corrections in place (rule 11):
* the turn PostEvent is handed is the FRAME, not ModCount. turn2-state.sav has
Frame 2 and ModCount 12, and every event it carries is in bucket EvTurn=2. It
is the post-increment turn: a turn run from a save at turn N posts into N+1.
* the EVENT_NO_RESEARCH gate: 0x00584e50 is TechTree::CollectResearchedTechs,
not a ListAvailableTechs, and the middle test is "nothing was researched on
this turn or later" -- not "no affordable tech". zuul-turn23 exercises it: the
human posts RESEARCH_COMPLETE on turn 22 with no no-research event that turn,
then NO_RESEARCH again on turn 23.
ghidra/addresses.d/lane-ev.json records the gate at 0x0089162a with the argument
order re-read from the instruction stream. Validated with tools/gen_addresses.py to
a scratch path (1121 entries, no duplicate); the shared header is NOT regenerated.
tools/standalone_report.py gains --engine-arg (repeatable), so a lane can feed the
standalone the operator inputs a save does not carry -- the data root, the AI
roster, which blocked phases may commit -- instead of hard-coding them. Every run
records what it was given, in the report header and in status.json.
The standalone now models the turn's dominant generator cost -- 16 of a measured 18-22
words -- and lands 4 and 2 short of the two calibrated pairs, which is exactly the
per-call-site ledger's split for those turns. The state block is byte-identical; only
left differs. The answer to 'does it match the oracle' is no, by a stated amount, and
tools/rng_oracle_check.py is the instrument that says so.
The tail's last phase is modelled for the six turn-record fields recoverable from the
wire and checked against the record the game itself archived: 480 fields over 80
player-records, 0 mismatches. It stays blocked; --commit-blocked shows exactly which
five fields are missing and what they cost.
By-product, and probably worth more than the phase: the stored bankruptcy elimination
limit is injective in the maximum-income sum it is built from, so every save states the
per-system output term that blocks ComputeBudget. tools/max_income_oracle.py inverts it
-- 25 player-records over the corpus -- and recovers the protection factor as 3.3 from
the saves rather than from the data files. It also shows the engine's -0.15 divisor
disagrees with the game on 6 of those 25.
divergence unchanged: 209->204 and 108->103, 5 closed / 0 regressed on both pairs.
tools/standalone_report.py drives sots-engine's sots_turn over each
consecutive-turn save pair and diffs the result against the game's own
post-turn save with state_checksum.py, which localises to named leaves and
proves its own coverage by re-serialisation.
turn1-state -> turn2-state baseline 209 diverging, after 204, closed 5
turn2-state -> turn3-state baseline 108 diverging, after 103, closed 5
regressed 0 on both
`regressed` is reported next to `closed` and never netted off. It earned its
place immediately: committing the phase-31 player-status restore turned two
agreeing leaves into disagreeing ones, because the phase writes 1 and the file
carries 4.
The stable-system stand-in feeding the colony pass is a labelled hypothesis and
it survived a changed workload -- the same 3 ntdev leaves closed on both pairs,
six agreements, zero disagreements.
Two things deliberately NOT implemented: the TShn/ltis counters (18 leaves, a
`+1` would close them, but "+1 across one observed turn" is a hypothesis, not a
reading), and the RNG state write-back (an advanced-but-incomplete generator is
wrong in a different way from an untouched one).
dashboard.py gains section 6, reading verify/results/standalone/status.json:
phases modelled/committed per driver, baseline vs after, closed vs regressed,
the subsystem breakdown of what still differs, and the RNG gap. Sections 6-8
renumbered to 7-9; the delta footer tracks the two new counts.
DASHBOARD_README.md documents every number.
findings/control-flow/standalone-scaffold.md has the ranked blocker list.
