README.md

---
tags:
- smart-manufacturing
- sft
- industrial
- vision
license: other
extra_gated_fields:
  Name: text
  Affiliation: text
  Intended use: text
extra_gated_prompt: >-
  This dataset is released for **research use**. Access is reviewed and granted
  **manually** by the maintainers. Please state your name, affiliation, and intended use.
pretty_name: D23-mcq
dataset_info:
  features:
  - name: query
    dtype: string
  - name: image
    dtype: image
  - name: annot
    dtype: string
  - name: reasoning
    dtype: 'null'
  - name: cate
    dtype: string
  - name: task
    dtype: string
  - name: metadata
    dtype: string
  splits:
  - name: train
    num_examples: 71
  - name: validation
    num_examples: 69
---

# D23-mcq

Marked-instance multiple choice on VISION industrial imagery — **140 records**
(one balanced-picked marked instance per eligible record, drawn from an eligible
pool of 384 marked instances), derived **deterministically** (no LLM/teacher)
from the human gold boxes of
[`AI4Manufacturing/D23-annotated`](https://huggingface.co/datasets/AI4Manufacturing/D23-annotated).
Exact-match gradable (single letter) → SFT and RLVR-ready.

> The repository name is an internal task code. See **Provenance** below.

> **Smallness is deliberate.** 140 records over 2 subsets is what survives the
> eligibility and kill battery below. The rung ships anyway for **format parity**:
> the corpus PoC found the MCQ shape own-task-critical and probe-protective, and
> the letter protocol here matches the D15/181 MCQ rungs so per-shape findings
> transfer.

> **Query diversity.** `query` is drawn from a pool of **32 surface variants** (paraphrases preserving task and answer format; the options clause and answer-format directive are held verbatim), selected by an independent salted per-item hash — the corpus query-template-diversity standard.

## Task

One defect instance is marked on the full-frame image by a **red rectangular
outline** (same drawing + seeded per-side [10%, 40%] padding
convention as [`AI4Manufacturing/D23-region`](https://huggingface.co/datasets/AI4Manufacturing/D23-region));
the query lists the subset's class menu as lettered options and asks for the letter
only. **Position-fair seeded letters:** the class→letter assignment is an
independent salted-hash permutation per record, so no letter position correlates
with the answer. Gold letter counts: Lens A 15, B 18, C 19, D 11, E 21; PCB_1 A 7, B 14, C 8, D 8, E 9, F 10.

**No `no defect` option** — every record's marked instance is a real gold, so the
option would be never-correct and is excised (corpus-wide surgery; it exists only in
D23-region's PCB_1 menu, where decoys make it correct).

**Balanced instance pick.** Each record contributes ONE marked instance, chosen by
the gate battery's balanced per-record pick (least-picked class first, deterministic
order) over its 384-instance eligible pool — this caps the answer-class priors
at the shares below (shipping the full pool would breach the prior cap).

## Records

**140 records** (train=71 ·
validation=69), split preserved verbatim from the parent.

| subset | records (train/val) | eligible instance pool | options | max answer-class share | max gold-letter share |
|---|---|---|---|---|---|
| Lens | 84 (41/43) | 218 (100/118) | 5 | 26.19% | 25.00% (uniform 20.00%) |
| PCB_1 | 56 (30/26) | 166 (84/82) | 6 | 42.86% | 25.00% (uniform 16.67%) |

- Every menu class is gold ≥1× within its subset. **PCB_1 `spur` note:** `spur` has
  exactly 1 gold pick (train-side only) — on the
  validation side `spur` is an eval-side **distractor-only** option.
- Blind-guess ceiling = the max answer-class share above (letter-marginal guessing
  is capped by the letter fairness column).

| field | type | meaning |
|---|---|---|
| `query` | str | names the product, mentions the red outline, lists lettered options; "answer with the letter only" |
| `image` | Image | full-frame photo (never cropped) with the padded red outline burned in |
| `annot` | str | gold letter (exact match) |
| `reasoning` | null | none — deterministic derivation |
| `cate` / `task` | str | `B` / `T-B2` (unified schema; rungs keep the parent token) |
| `metadata` | str (JSON) | subset, split, `image_sha256`, `source_record_id`, `instance_index` (**deduplicated** objects list), `gold_class`, `gold_letter`, `letter_map`, raw `bbox_xywh` + `padded_box_xywh` + `drawn_box_xywh`, menu, gates provenance |

## Eligibility & kills (the decision trail)

Pools were frozen by a pre-build gate battery under an adversarial convergence
review; the numbers below are the battery's own measurements.

**Legibility floor.** An instance is eligible only if min(w,h) ≥ 16px at the
2.36MP-equivalent training resolution. Unlearnable fraction of each shipped subset's
parent instances by reference input (long side / area cap):

| subset | 448 | 768 | 1024 | 1568 | 2.36MP |
|---|---|---|---|---|---|
| Lens | 93.4% | 87.8% | 83.5% | 68.1% | 68.8% |
| PCB_1 | 100.0% | 97.5% | 85.6% | 69.7% | 58.1% |

**Geometry-decoder kills** (rule: hard decoder iff probe >= majority + 25pts AND >= 75% absolute, computed on padded
outline geometry — no retained subset's outline geometry decodes its answer):

- **PCB_2 killed** — full-geometry GBM 98.1% vs
  30.5% majority (repeated-panel layout).
- **Cable killed (region/MCQ)** — size-only GBM 82.4%
  vs 50.4% majority: box size alone decodes the class.
- **Casting killed** — full-geometry GBM 83.8%
  vs 56.6% majority; the kill is position-driven
  (position channel adds +10.0pts over size-only
  73.8%).
- **Cylinder killed (round-3)** — cross-split geometry twins: 21.7%
  of val instances have a train instance within 10px (raw geometry) and
  51.3% within 30px; train→val padded 1-NN
  80.3% vs 33.6% val-majority —
  repeated rig positions leak labels across the split.

**Retained-subset certifications** (same probes, below the bar):

- **Hemisphere (BCD)** — cross-split padded 1-NN 71.9%
  (+16.7pts over val-majority) with twin fractions
  0.0%/8.1% —
  the signal is class-geometry, not rig repetition; in-pool padded LOO 1-NN on the
  **shipped** pool = **0.773** vs kill bar
  **0.780** (passes by
  0.7pts; both digits disclosed —
  this is the highest retained value in the corpus).
- **Lens (post-Fiber-trim)** — cross-split padded 1-NN 50.0%
  (-8.0pts), twins
  0.9%/3.6%.
- **PCB_1** — cross-split padded 1-NN 37.8%
  (-26.8pts), twins
  3.7%/7.3%.

**Opaque-code discriminability (size-normalized audit).** Hemisphere's anonymized
`Defect-A..D` codes were additionally required to show appearance-borne signal on a
fixed 224px canvas (removes
absolute size by construction; judge-side instrument, upscaling allowed): Defect-B
3.2× chance, Defect-C 2.0×,
Defect-D 1.73× pass the ≥1.5× bar; **Defect-A
0.8× fails and is excluded as a gold everywhere** (menu
excised too — never-correct options are removed corpus-wide).
The verdicts are **judge-robust** (judge-sensitivity rerun, three control-passing
judges: claude-sonnet-4-5, claude-sonnet-4-6, gpt-5.6): Defect-A stays under the ≥1.5× bar with every judge
(accuracy 0.20/0.37/0.30 vs the 0.375 bar), while Defect-B/C/D and every PCB_2
class pass under all three.
*Instrument-ceiling caveat:* the positive control (Cylinder, semantically named
classes) passes the control gate under every judge (macro 0.65/0.74/0.75), but its
Chip class scores 0.15 under the original judge and rises to
≈0.45 under the stronger judges — per-class collapses are **partly
judge-limited, not a hard pixel ceiling**; treat per-class passes as a lower bound
on discriminability. Residual cue disclosed by the protocol: **aspect ratio
survives the canvas normalization**.

Additionally for MCQ: Hemisphere is excluded from this rung (choosing among opaque `Defect-*` codes from a menu is closer to memorization than perception; audit scope), independent of the region-rung retention above.

*No D23-counting rung exists:* counting was dropped at plan review (degenerate ~all-one count priors on this parent + the counting shape lacks PoC validation; the shape is kept corpus-wide via other artifacts).

## Roles

**Roles:** this is an answer-only tier — there is no reasoning content (`reasoning` is null on every record); `annot` is both the machine-parseable gold AND the direct-answer SFT target ('SFT-ready' here means direct imitation of `annot` in the query-specified format); it is also the exact-match/IoU reward key for RLVR.

## Provenance

Derived read-only from [`AI4Manufacturing/D23-annotated`](https://huggingface.co/datasets/AI4Manufacturing/D23-annotated) (revision pinned: `fd728cc34406cd87512bd22fd8c31df78dace149`), itself derived from raw VISION (Bai et al., arXiv:2306.07890; upstream CC BY-NC 4.0 — respect upstream terms; this card is `license: other`). Answers are **pure functions of the human gold annotations** — no LLM/teacher anywhere in this rung. Generator: `annotate/D23/rungs/build_rungs.py` in [`AI4Manufacturing/forge_model`](https://github.com/AI4Manufacturing/forge_model) (builder sha256 `8652395b…`), built against the pre-build gate battery (gates v5 report, script sha256 `0b9d0e43…`, build source of truth = its section 16). Every independent choice is salted-hash seeded; a rerun reproduces the artifact exactly (tuple-hash verified).

Parent golds were deduplicated first: 5 records carried 8 exact-duplicate (class,bbox) phantom instances, collapsed before any pool math. `metadata.instance_index` refers to this **deduplicated** objects list, not the raw parent array. The parent's official train/validation split is preserved verbatim on every item (uniform-split policy; carve train/eval downstream).

## Training-mixture notes

- **One-lineage rule:** the parent excludes the 641 VISION images byte-shared with the DefectSpectrum/D15 family (materially different label policies); the 8 subsets here have **zero** image overlap with the D15 family. Details + machine-readable keys: base [`AI4Manufacturing/D23`](https://huggingface.co/datasets/AI4Manufacturing/D23) card §8.
- **Same-evidence rungs:** 137 of 140 D23-mcq marked instances also ship as D23-region records with **identical padded outlines** — region and mcq are format variants over the same evidence. Treat them jointly in any mixture/eval carve; never split the two rungs across train/eval.
- **Image-wise carving:** one photo can appear in the parent and in up to three rungs; carve on `metadata.image_sha256` across the whole D23 family simultaneously.
- **All-defective world prior:** every parent record is defective; rung items never assert a defect-free image (no 'no defect' option in this rung).

## Overlap / de-duplication (§8)

Base photos are the SAME images as [`AI4Manufacturing/D23-annotated`](https://huggingface.co/datasets/AI4Manufacturing/D23-annotated) and base [`AI4Manufacturing/D23`](https://huggingface.co/datasets/AI4Manufacturing/D23) — this rung inherits their overlap situation: the shipped 8-subset lineage shares no imagery with the D15 family (sha-verified sidecar on the base card), and D23-validation imagery appears in the other D23 rungs and the parent. **Do not evaluate on any D23-family repo's validation split if you train on this set**, and reconstruct exact overlaps via `metadata.image_sha256`.

## Training notes

- `annot` is a single letter → loss on completions; exact-match reward for RLVR.
- 137/140 of these marked
  instances also ship as
  [`AI4Manufacturing/D23-region`](https://huggingface.co/datasets/AI4Manufacturing/D23-region) records with
  identical outlines — same-evidence format variants; never split the two rungs
  across train/eval.
- Companions: [`AI4Manufacturing/D23-annotated`](https://huggingface.co/datasets/AI4Manufacturing/D23-annotated),
  [`AI4Manufacturing/D23-region`](https://huggingface.co/datasets/AI4Manufacturing/D23-region),
  [`AI4Manufacturing/D23-grounding`](https://huggingface.co/datasets/AI4Manufacturing/D23-grounding).