32 recorded rollouts, 8 seeds x 4 agents, every one replaying through the Python
engine to a delta of exactly 0.
arm solved economy consistency total
base-off 0/8 0.000 0.122 0.0244
base-on 4/8 0.469 0.479 0.4865
cautious 8/8 0.944 1.000 0.9831
solver 8/8 1.000 0.719 0.9437
Thinking-off solves none of eight. The same model on the same seeds, sampled
with thinking on, solves four. That is the headline, it is ours, and it needed
no training — which is also why the run is labelled an intervention and names
what was done to it, so a sampling change can never read as a training result.
`cautious` is new and it exists to make the reward editor honest. Until now the
page invited you to move a slider and watch the ranking change, and no slider
changed anything, because the solver dominated a model that solved nothing. A
candidate-only player — most informative guess among words that could still win
— takes consistency outright and pays for it in turns. Now:
only speed matters solver 0.9859 cautious 0.9606 -> solver
shipped weights solver 0.9437 cautious 0.9831 -> cautious
punish contradictions solver 0.8312 cautious 0.9972 -> cautious
The ranking really does flip, on recorded data, with one slider. The first
version of this arm picked the alphabetically-first candidate and opened on
'abaci', which made the policy the counterweight exists to reward look like a
straw man.
190 contract checks pass.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019mt6sHQHEnEYrJZvoMCJSB
karti-ai