cad2bf51b2
Second review pass, three findings. Pending cancellations were a set, so a node that proposed twice before its next cover emission forfeited only one and then over-emitted relative to its quota -- the precise uniformity cover traffic exists to preserve. Now a multiset. The timeline drew the block proposer uniformly while quota.py used a stake- weighted lottery, so the two halves of the cover-traffic model disagreed. The timeline now takes the stake array. Concentration is visible in the bookkeeping: a dominant proposer wins most proposals but rarely draws a cover slot to forfeit, so cancellations redeemed fall from 107 to 28 -- the unredeemed backlog being exactly the over-emission the stake ceiling describes. data/report_numbers.py claimed to print every quoted value but covered only sections 3.1-3.5 and 3.8. Extended to 3.9 correlated churn, 3.10 blending, mixing and the quota ceiling, 3.11 the release designs, and the 3.4 attribution bracket; the claim in data/README is corrected to say what it actually does. Neither model fix moves a published number: the proposer identity does not enter blending, mixing or timing, and repeat proposals are rare at the reported rates. Two regression tests pin both. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Evidence of record
The sweep outputs behind every number in the report. The simulator does not commit
its own runs/ directory — these are the copies of record, kept so that any figure or table can be
re-derived, or challenged, without re-running hours of compute.
Each run directory holds the three tables the simulator writes: propagation.parquet,
adversary.parquet and deanon.parquet.
| directory | config | sampling | backs |
|---|---|---|---|
default/ |
configs/default.yaml |
1 000 rounds × 8 seeds = 8 000/cell | §3.1–§3.5 — delay, observation, eclipse, deanonymization, delivery, coverage |
redundancy/ |
configs/redundancy.yaml |
1 200 × 8 = 9 600/cell | §3.8 — messaging redundancy R = 1…4 |
percolation/ |
configs/percolation.yaml |
800 × 8 = 6 400/cell | §3.5 — the churn threshold u_c = 1 − 1/(degree − 1) |
correlated-churn/ |
configs/correlated-churn.yaml |
800 × 8 = 6 400/cell | §3.9 — correlated AS/region outages vs uniform churn |
fullscale/ |
configs/fullscale.yaml |
64 × 3 = 192/cell | §5 — the 10⁶ scaling check (deliberately lighter; not a source of headline numbers) |
cover-traffic/ |
configs/cover-traffic.yaml |
900 s timeline × 4 seeds | §3.10 — blending, mixing, and the emission-quota stake ceiling. Carries a fourth table, traffic.parquet |
timing/ |
configs/timing.yaml |
120 s timeline × 3 seeds | §3.11 — the two release designs under a timing attack, and the minimum-interval control |
The linkability results (§3.6–§3.7) and both deanonymization rates are closed forms over these
tables rather than separate measurements, so they have no run of their own — blend.linkability
derives them and make verify checks them against Monte-Carlo.
Regenerating the report's numbers
python report_numbers.py
prints every quoted value straight from the parquets here -- the §3.1–§3.5 and §3.8 tables with
their across-topology standard errors, and the §3.9–§3.11 tables and the §3.4 attribution bracket
from their own runs.
That is the fastest way to check a table in the report against its evidence. It takes optional
paths (report_numbers.py <default> <redundancy> <percolation>) if you want to point it at fresh
runs instead.
Regenerating the data itself
From tools/simulators/blend: make sweep,
make redundancy, make percolation, make correlated-churn, make sweep-fullscale. Results
land in that simulator's runs/<timestamp>_<label>/. Note that the seed streams depend on the
configuration, so re-running reproduces the statistics, not bit-identical numbers, unless the
config is unchanged — in which case it does reproduce exactly.
Two runs from the same session are deliberately not kept: the smoke runs (throwaway, far too
noisy to interpret) and an earlier 144-rounds/cell redundancy grid that was superseded because its
sampling error produced a non-monotonic delivery curve — the reason redundancy/ samples 9 600.