Co-render the OCR instruction into the same PNG as the content,
delimited by '===…===' bands, with the API system field dropped.
L1 OCR fidelity, Opus 4.7, 20 production blocks, 7×10 cell:
- baseline (text-only): 97.91% mean / 96.25% min
- reflow (separate system): 91.99% mean / 82.59% min (-5.93pp)
- reflow-inimage: 98.95% mean / 96.42% min (+1.04pp)
reflow-inimage wins on all 20/20 blocks vs reflow, and beats the
text-only baseline on 17/20 blocks. Three blocks hit 100%. The
-5.93pp reflow regression that the cell-pitch sweep partially
recovered disappears entirely when the instruction is co-rendered.
Mechanism: when system carries the instruction and the image
carries the content, the model does cross-modal binding to figure
out what the image is for. Co-rendering reduces it to a
single-modal task with an unambiguous parse rule.
Files:
- eval/eval-l1-ocr.mjs: add reflow-inimage variant + prompt
- README.md: new section before history compression
- eval/EXPERIMENT_LOG.md: attempt #2 writeup
- eval/results/{l1-report.md, l1-results.json}: regenerated
Pack text into a continuous sentinel-delimited stream (↵ = U+21B5) so
wrapLines fills every row to `cols` instead of leaving dead right-margin.
Adds reflow/dereflow, renderTextToPngsReflow{,MultiCol} variants, a full
test suite, and an A/B eval harness with L1 OCR + L2 session results.
teamchong