Datasets:

Ope0
/

vision-gap

---
license: cc-by-4.0
language: [yo, vi, fr, en]
task_categories: [visual-question-answering, image-to-text]
tags:
  [ocr, diacritics, yoruba, nigeria, vlm-evaluation, blind-spot, low-resource]
pretty_name: Ìbàdàn Vision Gap
---
# Yoruba Vision Gap

### Small VLMs read the letters of Yoruba but not its meaning — and bend Nigerian documents toward Western defaults

**Model evaluated:** [`Qwen/Qwen3-VL-4B-Instruct`](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) (≈4.4B parameters, Apache-2.0) · optional second model `Qwen/Qwen2.5-VL-3B-Instruct`
**Evaluation set:** 360 synthetic, exactly-labelled probes (352 images) in 6 families, every probe paired with a control
**Everything here is reproducible:** `vision_gap_eval.ipynb` runs on a free Colab T4 and writes the Results section below automatically.

| Path                                                                                         | What it is                                                                                                       |
| -------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- |
| `README.md`                                                                                  | this file — answers to Q1 (the gap) and Q3 (path forward), method, results                                       |
| `data/images/`                                                                               | 352 PNG/JPG probe images                                                                                         |
| `data/metadata.jsonl` / `.csv`                                                               | one row per probe: id, family, condition, prompt, ground truth, answer type, options, metadata                   |
| `build_dataset.py`                                                                           | deterministic generator for the whole set (`python build_dataset.py --out data --seed 7`)                        |
| `scoring.py`                                                                                 | diacritic-aware automatic scoring (exact, letters-only, mark recall/precision by mark type, ANLS for comparison) |
| `vision_gap_eval.ipynb`                                                                      | Colab notebook: load model → run → score → tables → figures → README → upload                                    |
| `results/<model>/predictions.jsonl`                                                          | every raw model answer, with latency                                                                             |
| `results/scored.csv`, `results/summary.md`, `results/failure_gallery.md`, `results/figures/` | scored rows, headline table, worst cases, plots                                                                  |
| `model/<model>.json`                                                                         | pinned model revision (commit SHA), dtype and decoding settings used                                             |

---

## 1. The blind spot (Question 1)

### What it is

Yoruba is a tonal language written in the Latin alphabet. Its meaning lives in two kinds of marks that standard English
OCR treats as decoration:

- **tone marks** — grave (low tone, `à`), acute (high tone, `á`), unmarked (mid tone);
- **under-dots** — `ẹ`, `ọ`, `ṣ` are different letters from `e`, `o`, `s`.

Take away the marks and words collapse into each other:

| written | meaning       | written | meaning      |
| ------- | ------------- | ------- | ------------ |
| ọkọ     | husband       | igbá    | calabash     |
| ọkọ̀     | vehicle, boat | ìgbà    | time, period |
| ọ̀kọ̀     | spear         | igba    | two hundred  |
| ọkọ́     | hoe           | ìgbá    | garden egg   |

The gap I am testing is that **vision-language models read the base letters of Yoruba text correctly but drop, flatten
or invent the marks**, so they return a different word while looking almost right. Around it sits a broader pattern
I see in Nigerian documents generally: **when local visual evidence conflicts with the model's Western prior, the prior
wins.** The naira sign `₦` becomes `N` or `$`; `03/04/2026` on an invoice becomes March instead of April;
and a model asked "the total on this Bodija receipt is $13,200, correct?" is tempted to say yes.

### Why this comes from my own background

- **Signage and names.** Street signs, church and mosque boards, market stalls, palace and council notices, and
  people's names (Adébáyọ̀, Fọláṣadé, Olúwaṣẹ́un) are written with tone marks. Strip them and an identity or a place
  name is wrong — the kind of error that breaks a student-records system, a KYC check or a land document search.
- **Documents arrive as WhatsApp photos.** Receipts, admission letters, bank tellers and government circulars are
  photographed on a phone and forwarded several times. Each hop recompresses the JPEG, and tone marks — two or three
  pixels tall — are the first detail to disappear.
- **Money and dates are local.** Prices are in naira; on a keyboard without `₦` people write `N2,500` or `#2,500`.
  Dates are DD/MM/YYYY. A model that silently converts either into US conventions produces confident, wrong numbers.

### Why standard benchmarks miss it

1. **Coverage.** Text-in-image benchmarks (TextVQA, DocVQA, ChartQA, OCRBench, and multilingual ones such as MTVQA and
   CC-OCR) are dominated by English and a handful of high-resource languages; to my knowledge none tests Yoruba
   orthography, the naira sign or Nigerian document conventions. Culturally diverse VQA sets ask about scenes, not about
   reading tone-marked text.
2. **The metric hides it.** OCR benchmarks score with character error rate or ANLS. A dropped tone mark is a single
   character edit: reading _Ẹ kú àárọ̀_ ("good morning") as _Ẹ kú àárọ_ gets **ANLS 0.90 — a pass**  and reading
   _ọkọ̀_ (vehicle) as _ọkọ_ (husband) gets 0.75. Some evaluation pipelines also strip accents before comparison,
   which erases the error entirely. The gap is therefore invisible _by construction_, not just by omission.
3. **Leaderboards reward the prior.** On English-heavy test sets, defaulting to `$` and MM/DD is usually right, so
   the behaviour that fails Nigerian users is the behaviour that scores well.

---

## 2. Choice of model and how it was evaluated (Question 2)

### Model: `Qwen/Qwen3-VL-4B-Instruct`

- **Size:** ≈4.4B parameters — inside the 0.6–6B window and runs in fp16 on a free 16 GB T4.
- **Frontier-representative:** it shares the architecture and training recipe of the Qwen3-VL family whose 235B flagship
  is among the strongest open-weight VLMs, so its failure modes are a fair, cheap proxy for the flagship's (the notebook
  includes an optional cell that re-asks the hardest slice to the 235B model to check this directly).
- **A fair test, not a strawman:** the model card advertises expanded OCR for 32 languages, robustness to "low light,
  blur, and tilt", native dynamic resolution, and DeepStack multi-level vision features for fine detail. If a model
  that claims all of this still drops tone marks, the problem is not image resolution or a weak OCR head.
- **Second opinion:** `Qwen2.5-VL-3B-Instruct` (previous generation) can be switched on in the notebook to test whether the
  gap persists across generations; Gemma-3-4B and SmolVLM2-2.2B are also supported by the same harness.

Decoding is greedy (`do_sample=False`), one image per prompt, no system prompt, so results are deterministic and
reproducible. The exact model revision is pinned in `model/*.json`.

### The evaluation set

All 360 probes are rendered synthetically (Pillow + HarfBuzz/raqm, so stacked marks like `ọ̀` are positioned correctly)
from five fonts and five sign colour schemes. Synthetic rendering gives **exact ground truth** and lets every probe
change **one variable at a time**.

| Family                                 | n   | Probe                                                                                                       | Matched control                                                                     | Question it answers                                                              |
| -------------------------------------- | --- | ----------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
| `diacritics` / `yo`                    | 96  | 24 Yoruba items (12 minimal-pair words, 6 place names, 6 phrases) × 4 conditions                            | —                                                                                   | Can it transcribe Yoruba exactly?                                                |
| `diacritics` / `yo-ascii`              | 48  | the same 24 items with all marks removed (2 conditions)                                                     | **same letters, same font, same layout**                                            | Is the failure about the marks, or about the words? Does it _invent_ marks?      |
| `diacritics` / `vi`                    | 32  | 8 Vietnamese items × 4 conditions                                                                           | **same mark shapes** (dot-below, grave, acute, stacked) in a high-resource language | Is it the pixels, or the language?                                               |
| `diacritics` / `fr`                    | 32  | 8 French items × 4 conditions                                                                               | grave/acute in the highest-resource accented language                               | —                                                                                |
| `minimal_pairs` / `mc_spelling`        | 24  | pick the exact spelling from the minimal set (+ stripped form)                                              | chance ≈ 23%                                                                        | Can it _see_ the mark when it doesn't have to write it?                          |
| `minimal_pairs` / `meaning_from_image` | 8   | "what does this word mean?" for ọkọ/ọkọ̀/ọ̀kọ̀/ọkọ́ and igbá/ìgbà/igba/ìgbá                                     | `meaning_from_text`: same question with the word typed in the prompt (8)            | Does the mark it saw change what it _understands_? Does it know the word at all? |
| `currency` / `symbol_*`                | 32  | price tags `₦500 … ₦15,000`, clean and WhatsApp-compressed                                                  | identical tags in `$`, `£`, `€`                                                     | Is `₦` read as `₦`?                                                              |
| `currency` / `name_*`                  | 12  | name the currency: `₦` glyph; `N2,500` and `#2,500` on a Bodija Market tag                                  | —                                                                                   | Does it know Nigerian price conventions?                                         |
| `receipts`                             | 24  | Bodija provision-store receipt in `₦`: neutral total + false-premise total (wrong currency or wrong amount) | identical Austin, TX receipt in `$`                                                 | Is it more sycophantic on local documents?                                       |
| `dates`                                | 16  | invoice dated `03/04/2026`-style (day ≤ 12)                                                          | the same strings on US invoices; invoices with day > 12                      | Does it read the date in the document's own locale?                              |
| `known_controls`                       | 28  | counting 3–32 dots, left/right, English menu transcription + false premise                                  | —                                                                                   | How does this gap compare with blind spots already in public benchmarks?         |

**Conditions** (diacritics family): `L_clean` 72 px text, lossless · `L_wa` same, then _WhatsApp-forwarded_
(±2.5° tilt, 0.6× downscale, slight blur, JPEG q=35) · `S_clean` 28 px text · `S_wa` 28 px + WhatsApp.

### Scoring (`scoring.py`)

- **Exact match** after Unicode NFC, case-folding and whitespace cleanup — tone marks and dots are never stripped.
- **Letters-only match** (marks removed on both sides) — separates "read the wrong letters" from "read the right letters,
  wrong marks" (`marks_only_error`).
- **Mark recall / precision by type** (`dot_below`, `grave`, `acute`, …) after aligning base letters, plus
  **spurious marks** on the stripped control (hallucinated diacritics).
- **ANLS** on the same answers, to show what a standard OCR benchmark would have scored.
- Currency symbol and amount parsed separately (`N2,500` is reported as semantically right but not exact);
  YES/NO parsing for false premises, plus whether the true total was stated; month parsing for dates;
  letter parsing for multiple choice.

### How the probes add up to a diagnosis

| If the results show…                                                                 | …the gap is                                                               |
| ------------------------------------------------------------------------------------ | ------------------------------------------------------------------------- |
| Yoruba ≪ stripped Yoruba, and Yoruba ≪ Vietnamese on the _same_ mark types           | about the **language**, not about seeing small marks (data coverage)      |
| multiple-choice spelling ≫ free transcription                                        | a **generation prior**: the model sees the mark but writes the ASCII form |
| multiple-choice ≈ chance and falls with font size / compression                      | a **perception** limit on 2–3 px features                                 |
| meaning-from-text ≫ meaning-from-image                                               | the model knows the word but the visual mark doesn't reach its reasoning  |
| `₦` ≪ `$ £ €`, US-reading of Nigerian dates, higher sycophancy on the Bodija receipt | a **locale prior** overriding visual evidence                             |
| known controls fail too, but Yoruba fails at sizes where ASCII succeeds              | the gap is specific, not general weakness                                 |

---

## 3. Results

<!-- RESULTS:START -->

_Pending: run `vision_gap_eval.ipynb` (cell 10 writes this section from the run)._

<!-- RESULTS:END -->

---

## 4. Path forward (Question 3)

The probes above are designed to say _which_ of the fixes below matters most; this is the plan I would follow.

### 4.1 Data curation — the main lever

1. **Synthetic Yoruba text-in-image at scale (cheap, exact labels).** Scale `build_dataset.py` into a renderer driven by
   real Yoruba text — MENYO-20k, Yoruba Wikipedia, BBC Yorùbá news, public-domain religious text — rendered in 50+
   fonts including hand-painted sign styles, onto signs, receipts, posters, bulletins and government forms, with a
   degradation pipeline that reproduces how Nigerian documents actually travel: repeated WhatsApp JPEG compression,
   phone-camera blur, perspective, low light. Rendering must go through HarfBuzz (as here) or stacked marks come out
   wrong and poison the data. Target: ~1M image–text pairs, NFC-normalised.
2. **Real photos with native-speaker labels (small, high value).** Photograph signage, receipts and notices in Nigeria and have two native speakers transcribe each one, with disagreements
   adjudicated. A few thousand images is enough for evaluation and for a final fine-tuning stage; it also catches
   things synthesis misses, such as hand lettering and faded paint.
3. **Minimal-pair hard negatives.** For every image, store alternative transcriptions that differ _only_ in marks
   (ọkọ / ọkọ̀ / ọ̀kọ̀ / ọkọ́). These become the rejected side of preference pairs and the distractors of contrastive
   training — the model must learn that these differences matter.
4. **Locale-grounded document QA.** Nigerian receipts, invoices, tellers and forms with `₦`, `N` and `#` prices,
   DD/MM dates and `+234` numbers, paired with question–answer data in which a good share of questions carry a **false
   premise whose correct answer is "No, it says …"**.
5. **Fix the measurement.** Report a diacritic error rate and mark recall separately from CER/ANLS, never strip
   accents during evaluation, and add Yoruba and Igbo (ị, ọ, ụ, ṅ) slices to OCR leaderboards.
   As long as the metric scores _ọkọ̀ → ọkọ_ at 0.75, no one will optimise for it.

### 4.2 Fine-tuning paradigm

1. **Stage 1 — supervised fine-tuning that reaches the vision side.** LoRA on the language model _plus_ unfreezing the
   vision–language merger and the top vision blocks (for Qwen3-VL, including the DeepStack fusion paths), trained on
   transcription and document QA from 4.1. If the probes show a perception limit, adapting only the LLM will not fix it.
2. **Mark-weighted loss.** Combining marks are a small share of output tokens, so plain cross-entropy barely notices them.
   Up-weight the loss on tokens containing tone marks or under-dots (e.g. 3–5×).
3. **Stage 2 — preference optimisation.** DPO (or an image-conditioned variant such as mDPO) with pairs
   _chosen = correct marks_ vs _rejected = stripped or wrong tone_, and _chosen = "No, the total is ₦13,200"_
   vs _rejected = "Yes"_. This targets exactly the two behaviours measured here: flattening and sycophancy.
4. **Keep everything else intact.** Mix in ≥50% general replay data and re-run OCRBench and the known controls after
   each stage so that fixing Yoruba does not break English.

### 4.3 Architectural adjustments

1. **Resolution where the text is.** A tone mark on 28 px text is 2–3 pixels; after patching and 2×2 token merging
   one visual token covers roughly a 32×32 px area, so the mark is a small fraction of one token. Detect text regions and re-encode them at higher resolution
   (a learned "zoom" pass), rather than paying for high resolution everywhere.
2. **Character-level grounding head.** Add an auxiliary CTC OCR head on the vision features, trained on
   diacritic-exact targets, so the model has pixel-grounded evidence for each mark that the decoder can attend to.
3. **Tokenizer.** Byte-level BPE splits `ọ̀` into several rare tokens, which gives the decoder a strong prior toward
   the ASCII spelling. Adding tokens for frequent Yoruba vowel-plus-mark clusters (initialised from the mean of their
   pieces) lowers that prior.
4. **Calibrated abstention.** When the evidence for a mark is weak — a heavily compressed photo — the model should say
   the tone is unreadable instead of guessing; this is cheap to train with the hard negatives above.

**Cheapest test of this plan:** LoRA-tune Qwen3-VL-4B on ~50k synthetic renders from 4.1 (stage 1 + mark-weighted
loss), then re-run this notebook. Success = Yoruba exact match reaches the Vietnamese control on held-out fonts and
real photos, with no drop on OCRBench and the known controls.

---

## Limitations

- Images are synthetic. They control variables cleanly but do not capture hand lettering, faded paint or real camera
  optics; the real-photo set in 4.1 is the natural next step.
- The ambiguous-date probes have no single "correct" answer in isolation; they measure whether the model uses the
  locale cues present (Addresses, `+234` phone, `₦` amounts), and are reported separately from unambiguous dates.
- Yoruba orthography follows standard written Yoruba and the minimal-pair glosses were reviewed by the author;
  please open an issue if you spot an error.
- Greedy decoding on one prompt wording per probe; prompt sensitivity is not measured here.

## Licence and citation

Dataset (images + metadata): CC-BY-4.0. Code: MIT. Model weights: see the original model licence (Apache-2.0).

	---
	license: cc-by-4.0
	language: [yo, vi, fr, en]
	task_categories: [visual-question-answering, image-to-text]
	tags:
	[ocr, diacritics, yoruba, nigeria, vlm-evaluation, blind-spot, low-resource]
	pretty_name: Ìbàdàn Vision Gap
	---

	# Yoruba Vision Gap

	### Small VLMs read the letters of Yoruba but not its meaning — and bend Nigerian documents toward Western defaults

	Model evaluated: [`Qwen/Qwen3-VL-4B-Instruct`](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) (≈4.4B parameters, Apache-2.0) · optional second model `Qwen/Qwen2.5-VL-3B-Instruct`
	Evaluation set: 360 synthetic, exactly-labelled probes (352 images) in 6 families, every probe paired with a control
	Everything here is reproducible: `vision_gap_eval.ipynb` runs on a free Colab T4 and writes the Results section below automatically.

	\| Path \| What it is \|
	\| -------------------------------------------------------------------------------------------- \| ---------------------------------------------------------------------------------------------------------------- \|
	\| `README.md` \| this file — answers to Q1 (the gap) and Q3 (path forward), method, results \|
	\| `data/images/` \| 352 PNG/JPG probe images \|
	\| `data/metadata.jsonl` / `.csv` \| one row per probe: id, family, condition, prompt, ground truth, answer type, options, metadata \|
	\| `build_dataset.py` \| deterministic generator for the whole set (`python build_dataset.py --out data --seed 7`) \|
	\| `scoring.py` \| diacritic-aware automatic scoring (exact, letters-only, mark recall/precision by mark type, ANLS for comparison) \|
	\| `vision_gap_eval.ipynb` \| Colab notebook: load model → run → score → tables → figures → README → upload \|
	\| `results/<model>/predictions.jsonl` \| every raw model answer, with latency \|
	\| `results/scored.csv`, `results/summary.md`, `results/failure_gallery.md`, `results/figures/` \| scored rows, headline table, worst cases, plots \|
	\| `model/<model>.json` \| pinned model revision (commit SHA), dtype and decoding settings used \|

	---

	## 1. The blind spot (Question 1)

	### What it is

	Yoruba is a tonal language written in the Latin alphabet. Its meaning lives in two kinds of marks that standard English
	OCR treats as decoration:

	- tone marks — grave (low tone, `à`), acute (high tone, `á`), unmarked (mid tone);
	- under-dots — `ẹ`, `ọ`, `ṣ` are different letters from `e`, `o`, `s`.

	Take away the marks and words collapse into each other:

	\| written \| meaning \| written \| meaning \|
	\| ------- \| ------------- \| ------- \| ------------ \|
	\| ọkọ \| husband \| igbá \| calabash \|
	\| ọkọ̀ \| vehicle, boat \| ìgbà \| time, period \|
	\| ọ̀kọ̀ \| spear \| igba \| two hundred \|
	\| ọkọ́ \| hoe \| ìgbá \| garden egg \|

	The gap I am testing is that **vision-language models read the base letters of Yoruba text correctly but drop, flatten
	or invent the marks**, so they return a different word while looking almost right. Around it sits a broader pattern
	I see in Nigerian documents generally: **when local visual evidence conflicts with the model's Western prior, the prior
	wins.** The naira sign `₦` becomes `N` or `$`; `03/04/2026` on an invoice becomes March instead of April;
	and a model asked "the total on this Bodija receipt is $13,200, correct?" is tempted to say yes.

	### Why this comes from my own background

	- Signage and names. Street signs, church and mosque boards, market stalls, palace and council notices, and
	people's names (Adébáyọ̀, Fọláṣadé, Olúwaṣẹ́un) are written with tone marks. Strip them and an identity or a place
	name is wrong — the kind of error that breaks a student-records system, a KYC check or a land document search.
	- Documents arrive as WhatsApp photos. Receipts, admission letters, bank tellers and government circulars are
	photographed on a phone and forwarded several times. Each hop recompresses the JPEG, and tone marks — two or three
	pixels tall — are the first detail to disappear.
	- Money and dates are local. Prices are in naira; on a keyboard without `₦` people write `N2,500` or `#2,500`.
	Dates are DD/MM/YYYY. A model that silently converts either into US conventions produces confident, wrong numbers.

	### Why standard benchmarks miss it

	1. Coverage. Text-in-image benchmarks (TextVQA, DocVQA, ChartQA, OCRBench, and multilingual ones such as MTVQA and
	CC-OCR) are dominated by English and a handful of high-resource languages; to my knowledge none tests Yoruba
	orthography, the naira sign or Nigerian document conventions. Culturally diverse VQA sets ask about scenes, not about
	reading tone-marked text.
	2. The metric hides it. OCR benchmarks score with character error rate or ANLS. A dropped tone mark is a single
	character edit: reading _Ẹ kú àárọ̀_ ("good morning") as _Ẹ kú àárọ_ gets ANLS 0.90 — a pass and reading
	_ọkọ̀_ (vehicle) as _ọkọ_ (husband) gets 0.75. Some evaluation pipelines also strip accents before comparison,
	which erases the error entirely. The gap is therefore invisible _by construction_, not just by omission.
	3. Leaderboards reward the prior. On English-heavy test sets, defaulting to `$` and MM/DD is usually right, so
	the behaviour that fails Nigerian users is the behaviour that scores well.

	---

	## 2. Choice of model and how it was evaluated (Question 2)

	### Model: `Qwen/Qwen3-VL-4B-Instruct`

	- Size: ≈4.4B parameters — inside the 0.6–6B window and runs in fp16 on a free 16 GB T4.
	- Frontier-representative: it shares the architecture and training recipe of the Qwen3-VL family whose 235B flagship
	is among the strongest open-weight VLMs, so its failure modes are a fair, cheap proxy for the flagship's (the notebook
	includes an optional cell that re-asks the hardest slice to the 235B model to check this directly).
	- A fair test, not a strawman: the model card advertises expanded OCR for 32 languages, robustness to "low light,
	blur, and tilt", native dynamic resolution, and DeepStack multi-level vision features for fine detail. If a model
	that claims all of this still drops tone marks, the problem is not image resolution or a weak OCR head.
	- Second opinion: `Qwen2.5-VL-3B-Instruct` (previous generation) can be switched on in the notebook to test whether the
	gap persists across generations; Gemma-3-4B and SmolVLM2-2.2B are also supported by the same harness.

	Decoding is greedy (`do_sample=False`), one image per prompt, no system prompt, so results are deterministic and
	reproducible. The exact model revision is pinned in `model/*.json`.

	### The evaluation set

	All 360 probes are rendered synthetically (Pillow + HarfBuzz/raqm, so stacked marks like `ọ̀` are positioned correctly)
	from five fonts and five sign colour schemes. Synthetic rendering gives exact ground truth and lets every probe
	change one variable at a time.

	\| Family \| n \| Probe \| Matched control \| Question it answers \|
	\| -------------------------------------- \| --- \| ----------------------------------------------------------------------------------------------------------- \| ----------------------------------------------------------------------------------- \| -------------------------------------------------------------------------------- \|
	\| `diacritics` / `yo` \| 96 \| 24 Yoruba items (12 minimal-pair words, 6 place names, 6 phrases) × 4 conditions \| — \| Can it transcribe Yoruba exactly? \|
	\| `diacritics` / `yo-ascii` \| 48 \| the same 24 items with all marks removed (2 conditions) \| same letters, same font, same layout \| Is the failure about the marks, or about the words? Does it _invent_ marks? \|
	\| `diacritics` / `vi` \| 32 \| 8 Vietnamese items × 4 conditions \| same mark shapes (dot-below, grave, acute, stacked) in a high-resource language \| Is it the pixels, or the language? \|
	\| `diacritics` / `fr` \| 32 \| 8 French items × 4 conditions \| grave/acute in the highest-resource accented language \| — \|
	\| `minimal_pairs` / `mc_spelling` \| 24 \| pick the exact spelling from the minimal set (+ stripped form) \| chance ≈ 23% \| Can it _see_ the mark when it doesn't have to write it? \|
	\| `minimal_pairs` / `meaning_from_image` \| 8 \| "what does this word mean?" for ọkọ/ọkọ̀/ọ̀kọ̀/ọkọ́ and igbá/ìgbà/igba/ìgbá \| `meaning_from_text`: same question with the word typed in the prompt (8) \| Does the mark it saw change what it _understands_? Does it know the word at all? \|
	\| `currency` / `symbol_*` \| 32 \| price tags `₦500 … ₦15,000`, clean and WhatsApp-compressed \| identical tags in `$`, `£`, `€` \| Is `₦` read as `₦`? \|
	\| `currency` / `name_*` \| 12 \| name the currency: `₦` glyph; `N2,500` and `#2,500` on a Bodija Market tag \| — \| Does it know Nigerian price conventions? \|
	\| `receipts` \| 24 \| Bodija provision-store receipt in `₦`: neutral total + false-premise total (wrong currency or wrong amount) \| identical Austin, TX receipt in `$` \| Is it more sycophantic on local documents? \|
	\| `dates` \| 16 \| invoice dated `03/04/2026`-style (day ≤ 12) \| the same strings on US invoices; invoices with day > 12 \| Does it read the date in the document's own locale? \|
	\| `known_controls` \| 28 \| counting 3–32 dots, left/right, English menu transcription + false premise \| — \| How does this gap compare with blind spots already in public benchmarks? \|

	Conditions (diacritics family): `L_clean` 72 px text, lossless · `L_wa` same, then _WhatsApp-forwarded_
	(±2.5° tilt, 0.6× downscale, slight blur, JPEG q=35) · `S_clean` 28 px text · `S_wa` 28 px + WhatsApp.

	### Scoring (`scoring.py`)

	- Exact match after Unicode NFC, case-folding and whitespace cleanup — tone marks and dots are never stripped.
	- Letters-only match (marks removed on both sides) — separates "read the wrong letters" from "read the right letters,
	wrong marks" (`marks_only_error`).
	- Mark recall / precision by type (`dot_below`, `grave`, `acute`, …) after aligning base letters, plus
	spurious marks on the stripped control (hallucinated diacritics).
	- ANLS on the same answers, to show what a standard OCR benchmark would have scored.
	- Currency symbol and amount parsed separately (`N2,500` is reported as semantically right but not exact);
	YES/NO parsing for false premises, plus whether the true total was stated; month parsing for dates;
	letter parsing for multiple choice.

	### How the probes add up to a diagnosis

	\| If the results show… \| …the gap is \|
	\| ------------------------------------------------------------------------------------ \| ------------------------------------------------------------------------- \|
	\| Yoruba ≪ stripped Yoruba, and Yoruba ≪ Vietnamese on the _same_ mark types \| about the language, not about seeing small marks (data coverage) \|
	\| multiple-choice spelling ≫ free transcription \| a generation prior: the model sees the mark but writes the ASCII form \|
	\| multiple-choice ≈ chance and falls with font size / compression \| a perception limit on 2–3 px features \|
	\| meaning-from-text ≫ meaning-from-image \| the model knows the word but the visual mark doesn't reach its reasoning \|
	\| `₦` ≪ `$ £ €`, US-reading of Nigerian dates, higher sycophancy on the Bodija receipt \| a locale prior overriding visual evidence \|
	\| known controls fail too, but Yoruba fails at sizes where ASCII succeeds \| the gap is specific, not general weakness \|

	---

	## 3. Results

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	_Pending: run `vision_gap_eval.ipynb` (cell 10 writes this section from the run)._

	<!-- RESULTS:END -->

	---

	## 4. Path forward (Question 3)

	The probes above are designed to say _which_ of the fixes below matters most; this is the plan I would follow.

	### 4.1 Data curation — the main lever

	1. Synthetic Yoruba text-in-image at scale (cheap, exact labels). Scale `build_dataset.py` into a renderer driven by
	real Yoruba text — MENYO-20k, Yoruba Wikipedia, BBC Yorùbá news, public-domain religious text — rendered in 50+
	fonts including hand-painted sign styles, onto signs, receipts, posters, bulletins and government forms, with a
	degradation pipeline that reproduces how Nigerian documents actually travel: repeated WhatsApp JPEG compression,
	phone-camera blur, perspective, low light. Rendering must go through HarfBuzz (as here) or stacked marks come out
	wrong and poison the data. Target: ~1M image–text pairs, NFC-normalised.
	2. Real photos with native-speaker labels (small, high value). Photograph signage, receipts and notices in Nigeria and have two native speakers transcribe each one, with disagreements
	adjudicated. A few thousand images is enough for evaluation and for a final fine-tuning stage; it also catches
	things synthesis misses, such as hand lettering and faded paint.
	3. Minimal-pair hard negatives. For every image, store alternative transcriptions that differ _only_ in marks
	(ọkọ / ọkọ̀ / ọ̀kọ̀ / ọkọ́). These become the rejected side of preference pairs and the distractors of contrastive
	training — the model must learn that these differences matter.
	4. Locale-grounded document QA. Nigerian receipts, invoices, tellers and forms with `₦`, `N` and `#` prices,
	DD/MM dates and `+234` numbers, paired with question–answer data in which a good share of questions carry a **false
	premise whose correct answer is "No, it says …"**.
	5. Fix the measurement. Report a diacritic error rate and mark recall separately from CER/ANLS, never strip
	accents during evaluation, and add Yoruba and Igbo (ị, ọ, ụ, ṅ) slices to OCR leaderboards.
	As long as the metric scores _ọkọ̀ → ọkọ_ at 0.75, no one will optimise for it.

	### 4.2 Fine-tuning paradigm

	1. Stage 1 — supervised fine-tuning that reaches the vision side. LoRA on the language model _plus_ unfreezing the
	vision–language merger and the top vision blocks (for Qwen3-VL, including the DeepStack fusion paths), trained on
	transcription and document QA from 4.1. If the probes show a perception limit, adapting only the LLM will not fix it.
	2. Mark-weighted loss. Combining marks are a small share of output tokens, so plain cross-entropy barely notices them.
	Up-weight the loss on tokens containing tone marks or under-dots (e.g. 3–5×).
	3. Stage 2 — preference optimisation. DPO (or an image-conditioned variant such as mDPO) with pairs
	_chosen = correct marks_ vs _rejected = stripped or wrong tone_, and _chosen = "No, the total is ₦13,200"_
	vs _rejected = "Yes"_. This targets exactly the two behaviours measured here: flattening and sycophancy.
	4. Keep everything else intact. Mix in ≥50% general replay data and re-run OCRBench and the known controls after
	each stage so that fixing Yoruba does not break English.

	### 4.3 Architectural adjustments

	1. Resolution where the text is. A tone mark on 28 px text is 2–3 pixels; after patching and 2×2 token merging
	one visual token covers roughly a 32×32 px area, so the mark is a small fraction of one token. Detect text regions and re-encode them at higher resolution
	(a learned "zoom" pass), rather than paying for high resolution everywhere.
	2. Character-level grounding head. Add an auxiliary CTC OCR head on the vision features, trained on
	diacritic-exact targets, so the model has pixel-grounded evidence for each mark that the decoder can attend to.
	3. Tokenizer. Byte-level BPE splits `ọ̀` into several rare tokens, which gives the decoder a strong prior toward
	the ASCII spelling. Adding tokens for frequent Yoruba vowel-plus-mark clusters (initialised from the mean of their
	pieces) lowers that prior.
	4. Calibrated abstention. When the evidence for a mark is weak — a heavily compressed photo — the model should say
	the tone is unreadable instead of guessing; this is cheap to train with the hard negatives above.

	Cheapest test of this plan: LoRA-tune Qwen3-VL-4B on ~50k synthetic renders from 4.1 (stage 1 + mark-weighted
	loss), then re-run this notebook. Success = Yoruba exact match reaches the Vietnamese control on held-out fonts and
	real photos, with no drop on OCRBench and the known controls.

	---

	## Limitations

	- Images are synthetic. They control variables cleanly but do not capture hand lettering, faded paint or real camera
	optics; the real-photo set in 4.1 is the natural next step.
	- The ambiguous-date probes have no single "correct" answer in isolation; they measure whether the model uses the
	locale cues present (Addresses, `+234` phone, `₦` amounts), and are reported separately from unambiguous dates.
	- Yoruba orthography follows standard written Yoruba and the minimal-pair glosses were reviewed by the author;
	please open an issue if you spot an error.
	- Greedy decoding on one prompt wording per probe; prompt sensitivity is not measured here.

	## Licence and citation

	Dataset (images + metadata): CC-BY-4.0. Code: MIT. Model weights: see the original model licence (Apache-2.0).