README.md

---
license: cc-by-nc-4.0
task_categories:
  - visual-question-answering
language:
  - en
tags:
  - benchmark
  - biology
  - diagram-understanding
  - vision-language
  - no-image-control
  - grounding
pretty_name: BLINDSPOT
dataset_info:
  features:
    - name: item_id
      dtype: string
    - name: model
      dtype: string
    - name: condition
      dtype: string
    - name: f1
      dtype: float64
    - name: precision
      dtype: float64
    - name: recall
      dtype: float64
    - name: n_pred
      dtype: int64
    - name: n_key
      dtype: int64
    - name: predicted_names
      sequence: string
    - name: key_names
      sequence: string
    - name: subject
      dtype: string
    - name: tier
      dtype: string
    - name: unsolved_core
      dtype: bool
    - name: recitable
      dtype: bool
    - name: parse_ok
      dtype: bool
  splits:
    - name: train
      num_bytes: 3500000
      num_examples: 6335
  download_size: 3500000
  dataset_size: 3500000
configs:
  - config_name: default
    data_files:
      - split: train
        path: results.jsonl
---

# BLINDSPOT — a no-image control for figure-understanding benchmarks

**Byline: Nalandadata**

A model is shown an **unlabelled** scientific diagram and must name every part a leader line points to. The labelled original is the answer key. The headline result is not the leaderboard — it is the **no-image control**: the same prompt repeated with the picture removed. Whatever the model still scores came from memory alone. Eight of nine models keep 70–100% of their score that way.

## Dataset structure

This dataset contains **6,335 scored responses** across 9 models, 4 conditions, and 181 benchmark items. Source images are not distributable (copyrighted textbook figures and a proprietary question bank); only scores, predicted names, and answer keys are released.

### Files

| File | Description |
|---|---|
| `results.jsonl` | One row per (item, model, condition) — 6,335 rows |
| `items.jsonl` | One row per benchmark item — 181 rows with aggregate metadata |
| `sample/results.jsonl` | 10-row public preview |

### Schema — `results.jsonl`

| Field | Type | Description |
|---|---|---|
| `item_id` | string | Public item code (`NALANDA-N`) |
| `model` | string | Model identifier, e.g. `google/gemini-3.6-flash` |
| `condition` | string | `blind`, `told`, `desc`, or `no-image` |
| `f1` | float | Synonym-aware name-set F1 (0–1) |
| `precision` | float | Precision component |
| `recall` | float | Recall component |
| `n_pred` | int | Number of names the model predicted |
| `n_key` | int | Number of ground-truth names |
| `predicted_names` | list[str] | The model's label list |
| `key_names` | list[str] | Ground-truth anatomical names |
| `subject` | string | Short factual description of the diagram |
| `tier` | string | Item tier (`core` or `extended`) |
| `unsolved_core` | bool | True if no baseline model solved this item |
| `recitable` | bool | True if ≥1 model scored F1 ≥ 0.5 with no image |
| `parse_ok` | bool | Whether the model's output parsed correctly |

### The four conditions

| Condition | Model receives | Isolates |
|---|---|---|
| `blind` | Unlabelled diagram only | Visual recognition |
| `told` | Diagram + subject name | Naming, given recognition |
| `desc` | Diagram + long subject description | Does verbosity help? |
| `no-image` | Subject name, **no picture** | **Prior knowledge alone — the control** |

```
grounded contribution = told − no-image   (percentage points)
recitation share      = no-image ÷ told   (%)
```

### Prompts — verbatim, as sent

The `condition` column is only reproducible with the exact prompt text. All four prompts share the same JSON output schema; only the opening instruction differs.

**`blind`** — image attached, subject unknown:
```
You are an expert biology educator labeling a scientific diagram.
You are shown one biology diagram. Its parts are indicated by leader lines / arrows
(the printed labels have been erased). First work out what the diagram depicts, then
identify and name EVERY part that a leader line or arrow points to.

Rules:
- Use the single most specific, standard biology term for each part.
- Base your answer only on the image; do not invent parts.
- Output ONLY valid JSON (no markdown, no prose) in exactly this shape:
{
  "diagram_is": "<what the whole diagram depicts>",
  "labels": [ {"name": "<term>", "location_hint": "<where in image>"} ]
}
```

**`told`** — image attached, subject disclosed (`{subject}` = per-item string):
```
You are an expert biology educator labeling a scientific diagram.
You are shown a diagram of: {subject}. Its parts are indicated by leader lines / arrows
(the printed labels have been erased). Identify and name EVERY part that a leader line
or arrow points to.

Rules:
- Use the single most specific, standard biology term for each part.
- Base your answer only on the image; do not invent parts.
- Output ONLY valid JSON (no markdown, no prose) in exactly this shape:
{
  "diagram_is": "<what the whole diagram depicts>",
  "labels": [ {"name": "<term>", "location_hint": "<where in image>"} ]
}
```

**`desc`** — image attached, verbose subject description:
```
You are an expert biology educator labeling a scientific diagram.
You are shown a detailed, magnified educational diagram of {subject} — a biology
illustration drawn in a schematic / sectional style typical of a biology textbook.
Several of its distinct component structures, regions and parts are individually picked
out by leader lines or arrows (the printed labels have been erased). Study the whole
diagram carefully, then identify and name EVERY part that a leader line or arrow
points to.

Rules:
- Use the single most specific, standard biology term for each part.
- Base your answer only on the image; do not invent parts.
- Output ONLY valid JSON (no markdown, no prose) in exactly this shape:
{
  "diagram_is": "<what the whole diagram depicts>",
  "labels": [ {"name": "<term>", "location_hint": "<where in image>"} ]
}
```

**`no-image`** — **no image attached**, subject disclosed:

> ⚠️ The `no-image` prompt is **byte-identical to `told`** — it still says "You are shown a diagram of" and "Base your answer only on the image." No image is attached. This is intentional, not a transcription error: the goal was to hold the prompt constant and vary only the presence of the image, to isolate what the model could score from prior knowledge alone. A model that notices the contradiction and declines gets the same F1 as one that bluffs — the metric cannot distinguish them (see Finding 6).

```
You are an expert biology educator labeling a scientific diagram.
You are shown a diagram of: {subject}. Its parts are indicated by leader lines / arrows
(the printed labels have been erased). Identify and name EVERY part that a leader line
or arrow points to.

Rules:
- Use the single most specific, standard biology term for each part.
- Base your answer only on the image; do not invent parts.
- Output ONLY valid JSON (no markdown, no prose) in exactly this shape:
{
  "diagram_is": "<what the whole diagram depicts>",
  "labels": [ {"name": "<term>", "location_hint": "<where in image>"} ]
}
```

### Decode settings

| Provider | Settings |
|---|---|
| Google (Gemini) | `temperature=0`, `thinkingBudget=1024` tokens, `maxOutputTokens=8000` |
| OpenAI (gpt-4o, gpt-4o-mini) | `temperature=0`, `max_tokens=8000` |
| OpenAI (gpt-5.6-luna) | `reasoning_effort=low`, `max_completion_tokens=8000` |
| Anthropic (claude-opus-5) | `output_config.effort=high`, `max_tokens=24000`† |
| Anthropic (claude-sonnet-4) | `temperature=0`, `max_tokens=8000` |

† `max_tokens` was raised from 8,000 to 24,000 after 14 items truncated mid-JSON at the lower limit; those items were re-run.

## Terminology

Every term this dataset uses. The column values in `results.jsonl` are only meaningful with these definitions.

### The task

| Term | Meaning |
|---|---|
| **leader line** | The thin line or arrow drawn from a printed label to the part of the figure it names. Erasing the label leaves the line, which makes the question answerable: the line says *"name this"* without saying what it is. |
| **answer key** | The list of part names printed on the original figure. |
| **marker** | A letter (A, B, C…) printed on the figure itself, used instead of leader lines by some items. |

### The four conditions

| Condition | Image? | Subject named? | Question it answers |
|---|---|---|---|
| **blind** | ✅ | ✗ | Can it work out *what* the diagram is, unaided? |
| **told** | ✅ | ✅ | Given the subject, can it read the leader lines? |
| **desc** | ✅ | ✅ described at length | Does verbose framing add anything? |
| **no-image** | ✗ | ✅ | How much can it answer from memory alone? |

### The metrics

| Term | Meaning |
|---|---|
| **name-set F1** | The primary score. Compares the *set* of names the model produced against the set in the answer key — synonym-aware, so `loop of Henle` matches `Henle's loop` and `alveoli` matches `alveolus`. Position is ignored; only which names were produced. Reported as a percentage. |
| **marker accuracy** | The harder secondary score, for items with printed markers: was the right name attached to the right letter? Much lower than name-set F1 for every model — producing the vocabulary is easy, placing it is not. |
| **grounded contribution** | `told − no-image`, in **percentage points**. How much of the score actually required the picture. This is the headline metric. |
| **recitation share** | `no-image ÷ told`, as a **percentage of the score**. How much of the score came from prior knowledge instead. |
| **pts vs %** | A gap is written `+17.7 pts`; a share is written `71%`. Reading one as the other inverts the finding. |

### The item sets

| Term | Meaning |
|---|---|
| **BLINDSPOT-181** | The benchmark: 181 items kept because no baseline model scored 100% on them under `told`. A **failure-only set** — see Caveat 7.1. |
| **BLINDSPOT-core** | The 161 items no model ever solved, in any condition. |
| **unsolved_core** | Per-item flag — `True` if no model ever solved this item. |
| **recitable** | Per-item flag — `True` if at least one model scored F1 ≥ 0.5 with no image. 120 of 181 qualify. |
| **tier** | Which harvest an item came from: `core` (question/solution image pairs), `extended` (textbook figures), `patternc` (marker-keyed). Provenance, not difficulty. |
| **Control A / Control B** | The two selection-bias controls (Caveat 7.1). A = items excluded *because* a model solved them; B = pool figures never run at all. |

### Identifiers

| Term | Meaning |
|---|---|
| **NALANDA-1 … NALANDA-329** | Public item IDs. Deliberately opaque: the internal IDs encode source book and page, which cannot be published. Ordered by a salted hash, not alphabetically, so the numbering does not cluster by source. |


## Results

| Model | blind | told | desc | no-image | grounded | recited |
|---|---|---|---|---|---|---|
| google/gemini-3.1-pro-preview | 55.8% | 60.2% | 60.2% | 42.5% | +17.7 | 71% |
| google/gemini-3.6-flash | 53.8% | 59.5% | 59.3% | 41.4% | +18.1 | 70% |
| anthropic/claude-opus-5 | 42.2% | 55.1% | 53.5% | 39.9% | +15.2 | 72% |
| google/gemini-2.5-pro | 43.7% | 52.6% | 52.5% | 41.9% | +10.7 | 80% |
| openai/gpt-5.6-luna | 33.7% | 50.7% | 51.5% | 4.5% | **+46.2** | **9%** |
| google/gemini-2.5-flash | 39.4% | 50.0% | 49.5% | 39.7% | +10.3 | 79% |
| anthropic/claude-sonnet-4 | 24.3% | 45.2% | 44.0% | — | — | — |
| openai/gpt-4o | 31.4% | 43.1% | 44.3% | 34.8% | +8.3 | 81% |
| openai/gpt-4o-mini | 21.4% | 41.3% | 39.7% | 41.1% | **+0.2** | **100%** |

`anthropic/claude-sonnet-4` has no no-image row: the model is retired from Anthropic's API and cannot be re-run. `desc` is complete for all nine.

## Key findings

| # | Finding |
|---|---|
| 1 | Recognition is the bottleneck, not naming — every model gains from being told the subject |
| 2 | Verbose context adds nothing — 9 models, every difference within ±1.6 pts |
| 3 | The recognition gap is closing, unevenly — Anthropic −8.0 pts, Google −5.0, OpenAI −2.9 |
| 4 | 8 of 9 models keep 70–100% of their score with no image |
| 4b | Recitation is *older*, not newer — 2.5-era models recite 80–100%, current ones ~70% |
| 4c | For `gpt-4o-mini` the image contributes **nothing** (+0.2 pts, CI contains zero) |
| 5 | 120 of 181 items (66%) are answerable from memory by at least one model |
| 6 | Bluffing and abstention land at the same F1 — gpt-5.6-luna declines 81%, claude-opus-5 never declines |
| 7 | Inter-model agreement is not correctness — 31% of consensus names are absent from the key |
| 8 | Selection bias tested with two controls — it does **not** explain the finding (69–73%) |
| 9 | The reversal is statistically solid: +28.6 pts, 95% CI +23.2 to +33.6 |

## Caveats

| # | Caveat |
|---|---|
| 7.1 | **Failure-only set** — items were kept *because* baseline models failed them. Tested with two controls; the finding holds. |
| 7.2 | **Mixed API routes** — baseline models ran via OpenRouter, controls via native APIs. For the four baseline models the grounded contribution subtracts a native no-image score from an OpenRouter with-image score. |
| 7.3 | 8 items reproduce the figure at small scale. Excluding them changes nothing. |
| 7.5 | **Item-sampling variance** measured by bootstrap: ±4 pts. The top two models are a statistical tie. |
| 7.6 | **Biology only.** Untested on other domains. |
| 7.7 | **Contamination not separated from general knowledge** — these are published textbook figures. |

## Why no images?

All 181 benchmark items come from copyrighted textbooks and a proprietary question bank. There is no subset whose figures are releasable. Item IDs are anonymised public codes (`NALANDA-N`); the mapping to source books is private and will not be released.

## Scoring

Responses are scored with a synonym-aware name-set F1. The scorer handles:
- Latin/Greek plural forms (alveolus/alveoli, atrium/atria)
- Synonyms (mitral valve = bicuspid valve = left AV valve)
- Fuzzy matching for morphological variants (tight guards: ≥7 chars, first 3 identical, ≤3 length difference, ≥0.86 similarity)

Scoring scripts (`score_ext.py`, `score_grounding.py`) are included in the full dataset repository.

## Item provenance — three patterns

| Pattern | Key source | How the question was made | n |
|---|---|---|---|
| A | Solution image already labelled | Nothing — a true pair | 20 |
| B | Born-digital textbook figure | Label text erased from PDF; leader lines kept | 144 |
| C | Markers printed on question image | Key parsed from solution prose | 17 |

## Citation

```
@dataset{nalandadata2026blindspot,
  title   = {{BLINDSPOT}: a no-image control for figure-understanding benchmarks},
  author  = {Nalandadata},
  year    = {2026},
  url     = {https://huggingface.co/datasets/Nalandadata/BLINDSPOT}
}
```