Editing Openai/691c1dba-9228-800f-8463-13b3a9006306 (section)

==== I’ll outline an exact test you or I can run (copy/paste–ready): ====
# Pick one representative set: the 5–7 LaTeX files (call them D1..D6).
# Canonicalize originals (strip non-critical whitespace, normalize macros).
# Compress them via HPLm → produce ingot(s).
# Reconstruct via your pipeline (same decoder choice and temperature=0).
# Compute: - token counts (orig/recon/ingot) - token-identical percentage - SHA256 on canonicalized docs (orig vs recon) - embed similarity using all-MiniLM-L6-v2 or similar (mean cosine) - QA test with 200 autogenerated factual Qs (use your main LLM to ask Qs)
# Report: compression ratio, %token-preserved, mean cosine, QA accuracy, and any divergent sections (manual inspect).

If you want I’ll draft the exact code for this pipeline (Python) that you can run locally (embedding, tokenization, checksums, QA generation).