A plan has to work before it can be good.
11 models, each run 5 times on all 31 tasks. Even the top model scores below the expert solutions that clients accepted.
The higher α, the more low-scoring tasks pull the score down. At α = 1 it is the usual DAQ.
Full results Paper, Table 1
CSV| # | Model | Reasoning effort | ||
|---|---|---|---|---|
| 1 | high | 88.39± 10.28 | 86.55± 12.97 | |
| 2 | xhigh | 84.52± 17.02 | 85.02± 17.78 | |
| 3 | high | 82.58± 17.49 | 82.94± 19.06 | |
| 4 | xhigh | 78.71± 23.11 | 77.97± 23.71 | |
| 5 | xhigh | 77.42± 4.98 | 77.44± 7.26 | |
| 6 | high | 74.19± 12.04 | 72.86± 12.34 | |
| 7 | high | 69.68± 14.60 | 69.28± 15.93 | |
| 8 | high | 66.45± 9.95 | 66.85± 10.38 | |
| 9 | high | 65.16± 17.81 | 64.82± 18.47 | |
| 10 | max | 62.58± 25.35 | 63.21± 25.68 | |
| 11 | high | 45.16± 19.58 | 43.77± 19.50 |
Each model ran 5 times on each of the 31 tasks; ± shows how much results on the same task varied from run to run (standard deviation). A task’s plan is feasible only if it passes every test case. The experts score 100%, and a model can score above 100% on a task if its plan beats the expert’s.
How the scores work
The share of tasks where the submitted plan passes all of the hidden checks, such as capacity, timing and demand rules.
Each task is scored 0 if the plan breaks a rule. Otherwise its result is compared with the expert’s, where 1 means just as good. The overall score is the average over all 31 tasks.
qi compares the result on task i with the expert’s.
An average lets good tasks make up for bad ones. DAQα weighs low-scoring tasks more as α rises from 1 to 3. At α = 1 it equals DAQ; the experts stay at 1 throughout.
The coding tool matters too: in a separate run with Codex instead of Claude Code (paper, Appendix D.7), GPT-5.6 Sol scored 93.59% ± 7.31. Those runs are not part of this ranking.