- ON 47823 / OFF 47824 with own PXPIPE_LOG files: operator session can
never pollute bench measurement (separation by construction)
- resume = skip existing patch files; clean stop on quota errors
- Docker grading documented as proxy-free (no model calls in containers)
- README: mention Pro pilot alongside Lite
Does the model actually read pixelpipe's render, or coast on memorized answers?
- novel random-number problems (un-memorizable): text 100% vs image 93% (-7pp real reading tax)
- GSM8K (in training data): 97% vs 96% -- inflated ~3pp by recall
- verbatim recall from a dense render: 0/15
README 'Benchmarks' section leads with the clean 93% and reports the failure
rows too (no cherry-pick). Harness in eval/gsm8k/: gen_novel.py, render_cfg.mjs
(pixelpipe's real renderTextToPngs), bench.py -- claude -p, exact-match grading.
The prior commit captured only the rename (a stale pathspec aborted the
content staging). This applies the actual edits: retitle to FINDINGS,
'## Verdict' + superseded TL;DR, and the two cross-reference updates.
Widen the model gate to /^claude-opus-4-(?:[7-9]|[1-9]\d)(?:-|$)/ (Opus 4.7
and newer; previously 4.6/4.7). Wire isPixelpipeSupportedModel into the proxy
boundary in proxy.ts — the proxy previously gated on economics only and
ignored the model, so it compressed 4.8 traffic despite the docs claiming a
4.6/4.7 scope. Unsupported models now pass through with reason
'unsupported_model'.
Update public-api tests to the 4.7+ spec (320/320 pass).
Correct the docs to live measurement: reverse the POSTMORTEM "dead" verdict
(pixelpipe is a lossy gist-compressor saving ~68% on real dense Claude Code
traffic; the verbatim 0/15 needle finding stands as a caveat, not the
verdict), rewrite README Status/Limitations, and add a correction pointer to
the eval README. Original POSTMORTEM body preserved below the correction.
BREAKING CHANGE: Opus 4.6 and older are no longer supported.
Verbatim-risk guard (skip imaging unique IDs/hashes/exact values) still pending.
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