RVL-CDIP-Classifier: appendix

Slice table · prompt registry · cost

Supporting tables for the RVL-CDIP case study. All accuracy figures use prompt v11.8 on qwen/qwen3.7-flash unless stated.

Accuracy by slice

Slice size Tuning set? Exact-match accuracy
160 Yes, tuned against 99.4% (157/158)
320 No; informed later rules 87.2% (279/320)
480 No; superset of the 160 89.1% (424/476)
800 Not the tuning slice 83.1% (665/800)
1,120 balanced Not the tuning slice; shares at least 320 images with the 800 82.6% (925/1,120)

The tuning number is an upper bound, not a generalisation result. The 1,120 slice overlaps the 800 slice, so those two results are not independent.

Prompt history

Version Change Result (tuning slice unless stated)
v0 Short baseline 66.1% on the 800-image control slice (529 of 800), not the tuning slice
v10 Rules written for every v9 miss +6.4 points over v9
v11.8 Form-versus-budget and memo-versus-letter fixes 99.4% (157/158)
v16 Two worked examples 96.2%
v17.2 Current code default —
v19 Vote preamble 96.9% (155/160)

The registry holds 29 versions including subversions such as v11.8 and v17.2; “v0 to v19” names the endpoints.

Cost

Measure Value
1,120-image run, billed $0.4937
List-price projection $0.6815
Per image About $0.00044
v19 run, per image $0.000377

About 61–63% of prompt tokens were cache hits, which explains the gap between billed and projected cost.

What the simulations did not reproduce

Voting, stronger-model routing and worked examples looked promising in simulation. On measured slices, routing did not improve accuracy and cost 7.5–7.8 times more; exemplars moved accuracy down. Treat the simulation table in the report as predictions with a separate measured column.