Card: add the fine-tune findings and known limits (from the approved article)

README.md

@@ -75,6 +75,23 @@ A field the posting does not state should be left empty. Empty is a valid answer
 
 The full table is `data/scoreboard.csv`; per-field scores for every model are in `data/per_field.csv`.
 
+## What the arena showed: small fine-tuned models
+
+The arena's leader before fine-tuning, Qwen/Qwen3.5-27B, became a trainer: it extracted the same fields from a large set of JOA listings, and a refinement step kept only answers grounded in the posting text (a title or a salary that actually appears in it). None of the 250 arena listings was in the training data, and the Claude-built reference labels were used only to evaluate, never to train. LoRA adapters on three open base models became JOA Small, JOA Advance and JOA Deep (status pages, no weights yet: https://huggingface.co/JobOpportunitiesAPI/joa-small, https://huggingface.co/JobOpportunitiesAPI/joa-advance, https://huggingface.co/JobOpportunitiesAPI/joa-deep).
+
+- **The smallest model ranks first.** Fine-tuning lifted granite-4.2-3b from rank 71 to rank 1. JOA Small and JOA Advance are statistically tied; JOA Deep is just behind. Once the task was learned, size stopped mattering on this test.
+- **The students beat their teacher.** Partly because the trainer sometimes reasoned until it ran out of tokens, and partly because the training answers were tidied into one consistent form. On the listings the trainer did answer, JOA Advance was still 1.9 points ahead (95% range +0.5 to +3.5).
+- **They answer in about 110–120 tokens.** The trainer needs about 2,900, almost all of it reasoning.
+- **A stricter second test** used 250 different, recent listings, each verified twice by Claude Opus against the live page and the application flow. On exact match, all three fine-tunes scored about 85%, the trainer about 86% and the stock base models 71–82% (2 October 2026). Here the teacher is still slightly ahead.
+
+## Known limits
+
+- **The models read what JOA stores, not the live page.** The stored text is usually the description body; the header, where the title, location and posted date often sit, can be missing.
+- **They cannot tell that a job has closed.** From stored text alone, all three called a few dead listings open. The fix is better input, not a bigger model.
+- **JOA Deep is weak on posted date** (72, against 89 for JOA Small).
+- **JOA Small cannot drive a tool-using agent.** It works when given the input, not when asked to fetch it.
+- **The references and the judges are models.** Two judges, checked references and confidence ranges reduce that risk; they do not remove it.
+
 ## Files
 
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@@ -112,4 +129,4 @@ Employers or individuals who want a listing removed: hello@jobopportunitiesapi.o
 - General: hello@jobopportunitiesapi.org
 - Legal matters: luca@tzekos.eu
 - Phone: +30 2311 113 603
-- Job Opportunities API (JOA) is a sole proprietorship of Loukas Tzekos, Didaskalisis Papathanasiou Vas. 79, 54629 Thessaloniki, Greece. VAT EL117613696.
+- Job Opportunities API (JOA) is a sole proprietorship of Loukas Tzekos, Didaskalisis Papathanasiou Vas. 79, 54629 Thessaloniki, Greece. VAT EL117613696.