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D23-mcq / README.md
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weipang142857
card: judge-sensitivity robustness (discriminability v4) + softened instrument-ceiling caveat; README-only update
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metadata 
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. 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); 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 (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 (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 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 and base 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