grand-exchange, bot-detection and drop-table-inference. All three produce
unbounded or irreducible-error scores, unlike the first four, so all three
normalise against a REFERENCE STRATEGY rather than an absolute -- which is what
makes 1.000 reachable rather than aspirational.
drop-table-inference is the clearest case. Scoring KL against the true drop
table would put the ceiling out of reach, because sampling error is irreducible:
ground truth scores 0.900 against the Bayesian posterior's 1.000, and that
ordering is correct. The best estimate available from 1,200 kills is not the
true table, and an environment that demands it is measuring luck.
grand-exchange needed the market to carry structure a model can actually infer,
or profit is noise and no oracle exists. Measured over 120 baskets: the
reference earns 38,821 gp and is profitable in 120/120, random orders lose 561,
and trading only the random-walk items -- which look like the widest-swinging
lines on the board -- loses 16,860.
bot-detection generates naive bots, cloaked bots that jitter on purpose, and
efficient humans who look bot-like on every naive statistic. Timing features are
drawn BEFORE the generator decides who is a bot, so every latency rule sits at
chance. Reference discriminator F1 1.000, random 0.430.
Each was built by one agent then attacked by two independent reviewers on
exploitability and soundness. They earned their keep: drop-table-inference's
first reward let a memorised constant score 0.901 without opening the kill log,
and its reference was 19% worse than a flat number. probe.py now carries three
named fences asserting those attacks stay dead.
First evaluation also lands. Nemotron 3.5 Lightning, thinking off, 32 rollouts
per environment: 0.570 down to 0.059, every gate at or near zero, nothing
solved and nothing unsolvable.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
