vlm: lock DocTags→Markdown parity for picture_classification (#153)

picture_classification scored 1.3% similarity in the live VLM corpus run.
Root-causing it deterministically: our DocTags parser reproduces docling's
groundtruth Markdown for that fixture byte-for-byte (pictures with captions
before the image placeholder, multi-page <page_break>, reading order), so
the divergence is live-model / render variance — which the harness itself
flags as expected ("triage as render, not parser") — not a defect on our
side. Add an end-to-end regression test through convert_vlm that feeds the
model's exact DocTags for the document and asserts the Markdown matches the
groundtruth, so our side of the comparison stays provably correct. Image
leg, so it needs no pdfium and runs in CI everywhere.

Refs #153

Signed-off-by: artiz <artem.kustikov@gmail.com>
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EY5KAiquN4YpVf2PXEQkVT

Author: artiz

crates/docling/tests/vlm.rs

@@ -169,6 +169,38 @@ fn vlm_parses_doctags_answers() {
     assert!(md.contains("| H"), "md: {md:?}");
 }
 
+/// Conformance lock for #153: the `picture_classification` corpus fixture
+/// scored 1.3% similarity in the live VLM run — a divergence that (per the
+/// harness's own note) is render/model variance, *not* a parser defect. Feed
+/// the model's exact DocTags for that document through `convert_vlm` and assert
+/// the Markdown matches docling's groundtruth byte-for-byte, so our side of the
+/// comparison stays provably correct (pictures + captions before the image
+/// placeholder, multi-page `<page_break>`, reading order). Image leg → no
+/// pdfium, runs everywhere.
+#[test]
+fn vlm_doctags_matches_groundtruth_picture_classification() {
+    let doctags = std::fs::read_to_string(
+        repo_root().join("tests/data/pdf/groundtruth/picture_classification.doctags.txt"),
+    )
+    .expect("doctags fixture");
+    let want = std::fs::read_to_string(
+        repo_root().join("tests/data/pdf/groundtruth/picture_classification.md"),
+    )
+    .expect("markdown groundtruth");
+    // The model emits one DocTags blob per document (root wrapper + internal
+    // <page_break>); the image leg is a single request → single answer.
+    let (endpoint, served, handle) = mock_openai(vec![doctags]);
+    let source = SourceDocument::from_bytes("page.png", InputFormat::Image, image_bytes());
+    let doc = convert_vlm(&source, &opts(endpoint)).expect("vlm conversion");
+    handle.join().expect("mock server");
+    assert_eq!(served.load(Ordering::SeqCst), 1);
+    assert_eq!(
+        doc.export_to_markdown().trim(),
+        want.trim(),
+        "VLM DocTags → Markdown must match docling's groundtruth for picture_classification"
+    );
+}
+
 #[test]
 fn vlm_rejects_non_visual_formats() {
     let source = SourceDocument::from_bytes("x.md", InputFormat::Md, b"# hi".to_vec());