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@@ -41,42 +41,30 @@ families are not enabled.
 
 ## Benchmarks (reproducible)
 
-Does imaging preserve answers? We measured it three ways on `claude-opus-4-8`,
-and — unlike a marketing table — we report where it breaks.
+**One number: on short, readable content the model reads pixelpipe's render
+~93% of the time, at ~38% fewer tokens.** Measured clean — with novel
+random-number problems it cannot have memorized, on `claude-opus-4-8`:
 
-**1. Reading fidelity, clean — novel random-number problems (zero memorization possible):**
-
-| test | N | baseline (text) | pixelpipe (image) | delta |
-|---|---:|---:|---:|---:|
-| novel arithmetic, random numbers | 100 | 100% | **93%** | **−7pp** |
-
-This is the honest reading number. With the answer un-memorizable, the model
-reads pixelpipe's render correctly ~93% of the time on short content; the other
-~7% are misreads (`10200`→`9400`, `7873`→`7793`) or come back unreadable.
-
-**2. GSM8K — the standard suite, but it's in training data:**
-
-| benchmark | N | baseline (text) | pixelpipe (image) | tokens/problem |
+| test | N | text | pixelpipe (image) | tokens |
 |---|---:|---:|---:|---|
-| GSM8K (math) | 100 | 97% | 96% | 61 → 38 (**−38%**) |
+| novel arithmetic (un-memorizable) | 100 | 100% | **93%** | **−38%** |
 
-GSM8K *looks* near-lossless (−1pp), but that's inflated — the model recognizes
-memorized problems and recalls the answer even when it misreads the image. The
-novel test strips that out: the real reading cost is ~7pp, not ~1.
+The ~7% gap is real misreads (`10200`→`9400`, `7873`→`7793`), not noise — imaging
+is lossy even where it mostly works.
 
-**3. Where it fails completely — dense / verbatim:**
+**The boundary** — push to dense, exact-recall content and it collapses:
 
-| test | baseline (text) | pixelpipe (image) |
+| test | text | pixelpipe (image) |
 |---|---:|---:|
-| verbatim recall — exact 12-char hex from a *dense* render | 15/15 (100%) | **0/15 (0%)** |
+| verbatim recall — 12-char hex from a *dense* render | 15/15 | **0/15** |
 
-So pixelpipe reads **short, readable** content at ~93% (a real ~7% misread tax)
-and **silently fails** on dense walls and exact recall. The number you get depends
-entirely on what you feed it.
+~93% on short / readable, **0%** on dense / exact-recall. There is no free lunch —
+full analysis in [`FINDINGS.md`](FINDINGS.md).
 
-Reproduce: [`eval/gsm8k/`](eval/gsm8k/) (GSM8K + the novel reading test) and
-[`eval/needle-haystack/`](eval/needle-haystack/) (the verbatim failure). Full
-analysis: [`FINDINGS.md`](FINDINGS.md).
+<sub>We also ran the standard **GSM8K** suite: 96% imaged. But GSM8K is in training
+data, so the model recalls memorized answers through its own misreads — inflating
+the score ~3pp over the clean novel number above, which is why we don't lead with
+it. Reproduce: [`eval/gsm8k/`](eval/gsm8k/) · [`eval/needle-haystack/`](eval/needle-haystack/).</sub>
 
 ---