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A domain-specialized multimodal model trained on a new mechanical-drawing benchmark beats strong closed-source systems by 7.57 points on dense engineering drawings.

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2026-07-12 15:35 UTC pith:4QMGOBLT

load-bearing objection Real first unified mechanical-drawing VQA suite plus a solid 4B specialized baseline; headline +7.57 rests on LLM-judge scores that also shaped RL, with no human IAA. the 3 major comments →

arxiv 2605.30794 v2 pith:4QMGOBLT submitted 2026-05-29 cs.CV cs.AI

MechVQA: Benchmarking and Enhancing Multimodal LLMs on Comprehensive Mechanical Drawing Understanding

Qian Kou , Xiaofeng Shi , Yulin Li , Xiaosong Qiu , Xinyang Wang , Hua Zhou , Cao Dongxing This is my paper
classification cs.CV cs.AI
keywords mechanical drawing understandingmultimodal large language modelsvisual question answeringMechVQAdomain specializationreinforcement learningmulti-view reasoningengineering drawings
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

General multimodal models still break on mechanical engineering drawings: dense annotations are easy to miss, domain symbols are poorly known, and multi-view spatial reasoning under projection rules is unreliable. This paper introduces MechVQA, a benchmark of about 3,300 real part and assembly drawings with roughly 21,000 question–answer pairs spanning ten subtasks under Recognition, Reasoning, and Judging. On that testbed the authors train MechVL with supervised fine-tuning followed by DAPO reinforcement learning that rewards answer correctness, output format, and explanation quality. The resulting 4B model reaches 84.85 total score and outperforms the strongest closed-source baseline by 7.57 points, with the largest gains on medium/hard items and on projection-consistent reasoning plus standards-aware judging. The package is offered as a reusable foundation for drawing-centric design and inspection tools.

Core claim

The paper claims that multi-stage post-training on MechVQA—full supervised fine-tuning of the language module, then DAPO-based self-play reinforcement learning with a taxonomy-aligned composite reward—turns a general 4B vision–language model into a domain baseline that substantially outperforms both large open-source and closed-source general MLLMs on comprehensive mechanical drawing understanding.

What carries the argument

MechVQA’s three-capability, ten-subtask taxonomy, paired with a DAPO reward that combines semantic accuracy (LLM-judged equivalence), binary schema format compliance, and quality scores for logic, professionalism, and conciseness; that stack is what carries the reported MechVL gains.

Load-bearing premise

Automatic multi-LLM semantic judging of free-form answers is treated as a faithful proxy for engineering correctness even though no human-expert upper bound or inter-annotator agreement is reported.

What would settle it

Have independent mechanical engineers re-label the same held-out answers under a fixed rubric; if MechVL’s advantage over the strongest closed- and open-source baselines disappears or reverses under human majority scores, the central performance claim fails.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Mechanical design and inspection workflows can treat specialized MLLMs as decision-support for dimension reading, multi-view correspondence, and anomaly checks rather than only generic VQA.
  • Future engineering-drawing work can evaluate against the same Recognition / Reasoning / Judging split instead of isolated symbol or floor-plan tasks.
  • Targeted self-play RL that upweights weak subtasks can reduce capability imbalance on dense, constraint-sensitive drawings.
  • A public-source drawing benchmark with drawing-level splits becomes a shared testbed for domain adaptation of multimodal models.
  • Standards-aware judging becomes a trainable signal, not only a human review step.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same SFT-plus-group-RL recipe may transfer to other dense technical-document domains (electrical schematics, AEC drawings) where symbol standards and projection rules dominate.
  • Freezing the vision encoder while fully updating only the language module implies that much of the remaining failure is in domain grounding and multi-step constraint use, not pure low-level perception alone.
  • If judge models systematically reward fluent majority-model phrasing, reported gains partly measure style alignment; a human expert ceiling would separate drafting truth from judge preference.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 0 minor

Summary. The paper introduces MechVQA, a mechanical-drawing VQA benchmark with 3,281 drawings and 20,778 QA pairs spanning 10 subtasks under Recognition, Reasoning, and Judging, built via a semi-automated pipeline with expert metadata checks, multi-model generation, cross-model validation, and majority voting. It then presents MechVL, a Qwen3-VL-4B-based baseline trained with full-parameter SFT followed by DAPO reinforcement learning using accuracy, format, and quality rewards. On the held-out test split, MechVL-4B-RL reports 84.85 total accuracy, exceeding the strongest open-source baseline (GLM-4.6V, 78.91) by 5.94 points and the strongest closed-source baseline (Gemini-3-Pro-Preview, 77.28) by 7.57 points, with larger gains on medium/hard items and on projection- and standards-sensitive subtasks. Ablations compare SFT vs. full/targeted RL, GRPO/GSPO/DAPO, and reward variants.

Significance. If the reported gains hold under expert-calibrated evaluation, this is a useful domain contribution: mechanical drawings are a high-value, under-served setting for MLLMs, and a unified Recognition/Reasoning/Judging taxonomy with real part/assembly sheets fills a clear gap relative to general VQA and narrower CAD/blueprint suites. Strengths include drawing-level 8:1:1 splits with CLIP-based near-duplicate control, multi-stage quality gates, explicit difficulty stratification, and systematic ablations of training stage, RL algorithm, and reward design (Tables 2–3; Figs. 2–4). The work is practically oriented toward design/inspection assistance and is careful about scope (public educational/professional drawings, not proprietary factory archives).

major comments (3)
  1. The central quantitative claim (abstract; Table 2: MechVL-4B-RL 84.85 vs. Gemini-3-Pro-Preview 77.28 / GLM-4.6V 78.91) rests on automatic multi-LLM semantic judging of free-form answers (Appendix C.4: GPT-OSS-120B, DeepSeek-V3.2, Kimi-k2 majority vote, without the image). Ground-truth answers are themselves largely produced by multi-model generation plus LLM majority voting (§3.3), and RL accuracy/quality rewards also use LLM-as-a-Judge (§4.2). Limitations explicitly note missing human-expert upper bounds and inter-annotator agreement. Without a calibrated human audit on a stratified test subset (especially GC, PM, AD, CJ), it is not established that the leaderboard measures drafting-grounded engineering correctness rather than preference for schema-compliant, professionally phrased, majority-model answers. This is load-bearing for the headline +7.57 claim.
  2. §5.1 and Appendix C.4 treat binary semantic match to model-voted ground truth as the primary metric, but do not report judge–judge agreement, human–judge agreement, or sensitivity of rankings to judge choice. Given that the same LLM-judge paradigm shapes both training rewards and final scores, a modest expert re-label or re-score study on the test set (or at least on Reasoning/Judging hard items) is needed to support the claim that domain post-training “significantly enhances mechanical drawing understanding” rather than optimizing for the automatic evaluator.
  3. Table 2 shows very large relative gains on sparse subtasks (e.g., AR 40→84, IL 61.15→82.01, PM 44→64) where sample counts are small (Fig. 2b: AR 1.9%, PM 3.6%, IC 1.5%, TT 1.3%). The paper should report per-subtask test sizes and uncertainty (bootstrap or binomial CIs) so that the capability-wise averages and “best on DA/IL/SU/AR/PM/AD” claims are not over-interpreted from few items.

Circularity Check

1 steps flagged

Empirical train/test benchmark paper with no derivation that equals its inputs by construction; only a mild shared LLM-judge loop between RL rewards and automatic scoring.

specific steps
  1. other [§4.2 Reward design; §5.1 Evaluation; Appendix C.4]
    "Remarkably, instead of strict string matching, we also employ LLM-as-a-Judge for Accuracy Reward to assess semantic equivalence... Quality reward... Logic, Professionalism, and Conciseness... We employ multiple strong LLMs as automatic judges, including GPT-OSS-120B, DeepSeekV3.2, and Kimi-k2 for each test question."

    Not a by-construction identity: train/test are split and baselines are external. Mild loop only—RL optimizes accuracy/quality under LLM judges, and the headline total is again majority-voted LLM semantic match to GT without the image. This can favor judge-preferred phrasing rather than force the numerical claim from the training labels alone; it is a soft evaluation circularity risk, not Eq.X≡Eq.Y.

full rationale

MechVQA/MechVL is an empirical systems paper, not a first-principles derivation. The load-bearing claim is a held-out test score (MechVL-4B-RL 84.85 vs Gemini-3-Pro-Preview 77.28 and GLM-4.6V 78.91 in Table 2), obtained after SFT+DAPO on a drawing-level train split with external general-purpose MLLM baselines under a fixed protocol. That is not a fitted parameter renamed as a prediction, nor a self-definitional identity, nor a uniqueness theorem imported from the same authors. Dataset construction uses multi-model generation and majority voting for QA pairs, and both RL accuracy/quality terms and final evaluation use LLM-as-a-Judge semantic scoring; that is a methodological validity risk (judge style may correlate with training rewards), but it does not make the reported total score equal to the training inputs by construction—the model is still scored against held-out ground-truth answers and compared to independent baselines. No self-citation uniqueness chain or ansatz-smuggling step is load-bearing. Score 1 only for the mild shared automatic-judge pathway; central empirical comparison remains independent of a circular reduction.

Axiom & Free-Parameter Ledger

3 free parameters · 4 axioms · 0 invented entities

The central empirical claim rests on standard MLLM post-training practice plus domain assumptions that the constructed QA labels and LLM judges track mechanical-drawing truth well enough for ranking. Free parameters are training/reward choices, not physics constants. No new physical entities are postulated; MechVQA/MechVL are engineered artifacts.

free parameters (3)
  • reward weights λ_acc / λ_qual / λ_fmt = 0.60 / 0.30 / 0.10
    Targeted RL uses a hand-chosen 0.60/0.30/0.10 mix (Appendix C.3); final total score depends on this weighting.
  • DAPO clip ranges and group size = clip [0.20, 0.28], G=10
    ε_low/ε_high (0.20/0.28), group size 10, LR 1e-6, batch 128, etc., are chosen hyperparameters that affect reported RL gains (Appendix C.2).
  • LLM-judge majority aggregation = 3 judges, majority of rounded scores
    Final scores round and majority-vote three judges; this aggregation rule is a free evaluation design choice that can change rankings (Appendix C.4).
axioms (4)
  • domain assumption Semantic majority vote among strong MLLMs yields unique, drawing-faithful ground-truth answers for retained QA pairs.
    Core of dataset construction in §3.3; discarded only when no clear majority.
  • domain assumption LLM-as-a-Judge binary/semantic scores are adequate substitutes for expert mechanical engineers when ranking models.
    Used in RL rewards (§4.2) and primary evaluation (§5.1); Limitations admit missing human upper bounds/IAA.
  • domain assumption Public textbook/handbook/design-platform drawings under orthodox drafting conventions are a valid testbed for real-world mechanical drawing understanding.
    Stated scope in §3.1 and Limitations; industrial legacy practices are out of scope.
  • standard math Standard causal LM SFT and DAPO group-relative policy optimization improve instruction following and long-form reasoning under the stated rewards.
    Training objectives Eqs. 1–6 follow established SFT/RL literature (Ouyang et al.; Yu et al. DAPO).

pith-pipeline@v1.1.0-grok45 · 28386 in / 3227 out tokens · 34304 ms · 2026-07-12T15:35:48.749517+00:00 · methodology

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read the original abstract

Multimodal Large Language Models (MLLMs) have demonstrated significant achievements in general visual question answering (VQA) tasks. However, they remain brittle on mechanical engineering drawings, where high annotation density and weak domain knowledge, compounded by unreliable spatial relation reasoning under strict projection rules and geometric constraints, make decisive cues easy to miss and frequently lead to wrong answers. To bridge this gap, we introduce the first comprehensive mechanical drawing understanding dataset, MechVQA, created through a semi-automated construction and quality-control pipeline. MechVQA contains 3.3k high-density pictures with 21K question-answer pairs, spanning 10 different fine-grained tasks across three capability levels: Recognition, Reasoning, and Judging, providing a testbed to evaluate and improve MLLM understanding on real-world mechanical drawings. On top of MechVQA, we then develop the MechVL model through a multi-stage training paradigm, building a strong domain-specialized baseline. Extensive experimental results demonstrate that MechVL outperforms the strongest closed-source baseline by 7.57 percentage points on the MechVQA total score, significantly enhancing mechanical drawing understanding ability and providing a reusable foundation for deploying MLLMs in mechanical design and inspection scenarios.

Figures

Figures reproduced from arXiv: 2605.30794 by Cao Dongxing, Hua Zhou, Qian Kou, Xiaofeng Shi, Xiaosong Qiu, Xinyang Wang, Yulin Li.

Figure 1
Figure 1. Figure 1: Overview of Mechanical Drawing Understanding and MechVQA. (a) Representative challenges, including high-density annotation recognition, projection-consistent spatial correspondence across views, and standards-aware interpretation of domain-specific symbols, specifications, and tables. (b) MechVQA task taxonomy organized into three capabilities (Recognition, Reasoning, Judging) with fine-grained subtasks, a… view at source ↗
Figure 2
Figure 2. Figure 2: MechVQA dataset construction and analysis. (a) Source drawings and mechanical textbooks are filtered and annotated to obtain reliable metadata. Multiple strong MLLMs generate and self-refine QA pairs, which are further screened by voting-based quality checks and manual review, yielding the final MechVQA dataset used for evaluation and post-training. (b) MechVQA contains 10 subtasks with varying quantities … view at source ↗
Figure 3
Figure 3. Figure 3: Evaluation results on difficulty levels Capability-wise comparison. Beyond the overall score, MechVL-4B-RL shows consistent gains across all three capability axes. Averaging the corresponding subtasks in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Response length dynamics under different reward designs during RL training. Acc(F1) rapidly shortens responses, indicating that token-overlap feedback encourages terse outputs; w/o Qual produces the longest traces, reflecting verbosity without a matching accuracy gain. The full reward maintains controlled response lengths while achieving the best final performance, suggesting better-calibrated reasoning tr… view at source ↗
Figure 5
Figure 5. Figure 5: shows an example mechanical drawing. The corresponding metadata schema is summarized in [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Example of metadata extracted from a mechanical drawing B.2. Effect of Expert Verification To quantify the role of expert verification, we compare model-extracted metadata before human checking with the final expert-corrected metadata on the audited typical and newstandard groups. We exclude bookkeeping-style schema migration fields from this analysis and focus on mechanically meaningful corrections. As su… view at source ↗
Figure 7
Figure 7. Figure 7: Task taxonomy of MechVQA 17 [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Difficulty Level definition for MechVQA 18 [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Question generation prompt for MechVQA without ground truth 19 [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Question generation prompt for MechVQA with ground truth: Dimension 20 [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Question generation prompt for MechVQA with ground truth: Annotation 21 [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Question generation prompt for MechVQA with ground truth: Location 22 [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Question generation prompt for MechVQA with ground truth: Calculation 23 [PITH_FULL_IMAGE:figures/full_fig_p023_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Prompt for question validation and fixing 24 [PITH_FULL_IMAGE:figures/full_fig_p024_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Prompt for answer voting and merging with language consistency 25 [PITH_FULL_IMAGE:figures/full_fig_p025_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: t-SNE visualization of the feature distributions for the train/validation/test splits. Different colors indicate samples from each split. C. Training Details All experiments were conducted on a computing cluster equipped with eight NVIDIA H800 GPUs (80GB memory each). Our training pipeline consists of multiple stages: supervised fine-tuning (SFT) followed by reinforcement learning (RL). C.1. Supervised Fi… view at source ↗
Figure 17
Figure 17. Figure 17: summarizes the validation accuracy and response length trends under different RL algorithms. Category Hyperparameter SFT RL GRPO GSPO DAPO Data Max Pixels 262,144 262,144 262,144 262,144 Max Sequence Length 4,096 1,024 / 2,048 1,024 / 2,048 1,024 / 2,048 Optimization Optimizer AdamW AdamW AdamW AdamW Learning Rate 1.0 × 10−5 1.0 × 10−6 1.0 × 10−6 1.0 × 10−6 LR Scheduler Cosine Linear Linear Linear Warmup … view at source ↗
Figure 18
Figure 18. Figure 18: Prompt template for automatic VQA answer evaluation, translated from the implementation. The returned JSON is parsed by extracting the first JSON object in the judge response; the score is clipped to [0, 1]. If JSON parsing fails, the evaluator falls back to a conservative string-level parse for explicit 0/1 outputs, and otherwise assigns 0. For each model answer, we aggregate the valid judge scores by ro… view at source ↗

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Reference graph

Works this paper leans on

32 extracted references

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    PMLR, 2019. Hu, E. J., Shen, Y ., Wallis, P., Allen-Zhu, Z., Li, Y ., Wang, S., Wang, L., Chen, W., et al. Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022. Khan, M. T., Chen, L., Yong, Z., Tan, J. M., Feng, W., and Moon, S. K. From drawings to decisions: A hybrid vision- language framework for parsing 2d engineering drawings into str...

    arXiv 2019

  2. [2]

    The tip head is carburized and quenched, hardness 40–45 HRC

  3. [3]

    Unspecified chamfer: C1

  4. [4]

    Unspecified dimension tolerance according to GB/T1804–2000–m

    2000

  5. [5]

    top-left drawing

    Unspecified geometric tolerance according to GB/T1184–1996–K. Figure 6.Example of metadata extracted from a mechanical drawing B.2. Effect of Expert Verification To quantify the role of expert verification, we compare model-extracted metadata before human checking with the final expert-corrected metadata on the audited typical and newstandard groups. We e...

    1996

  6. [6]

    Do not hallucinate or infer features that are not present

    Generate questionsonly based on what is explicitly shown in the drawing. Do not hallucinate or infer features that are not present

  7. [7]

    Focus exclusively on the graphical content of the drawing

    Ignore all surrounding textual descriptions(e.g., title blocks, notes, tables). Focus exclusively on the graphical content of the drawing

  8. [8]

    4.Prioritize question quality over quantity

    If the drawing containsmultiple sub-figures, you must clearly specify which sub-figure each question refers to. 4.Prioritize question quality over quantity. Do not generate vague, trivial, or unanswerable questions. Given Drawing Metadata (Provided as Annotations): •Combination Type:Single drawing or Multiple sub-figures •Drawing Type:{drawing type}(provi...

  9. [9]

    Inspect the drawing and identify up tofive distinct locations or featuresthat are explicitly annotated with dimensions

  10. [10]

    right end of the shaft

    For each identified location, determine: • Thedimension value(e.g.,Φ30,50,R5,15 ◦). • Theannotated location or feature(e.g., “right end of the shaft”, “bottom hole”, “top fillet”). • Theviewin which the dimension appears (e.g., front view, top view, side view, section A–A, detail view B)

  11. [11]

    Prefer diversity in bothlocationsanddimension types(e.g., diameter, length, radius, chamfer, angle)

  12. [12]

    dimensions

    If fewer than five dimension annotations are present, report as many as can be reliably identified. Output Format:Return the result strictly in the following JSON format, without any additional explanation or commentary: { "dimensions": [ { "dimension_value": "e.g., Phi30", "location": "annotated feature or position", "view": "corresponding view name" } ]...

  13. [13]

    Do not hallucinate missing symbols or values

    Generate questionsonly based on annotations that explicitly appear in the drawing. Do not hallucinate missing symbols or values

  14. [14]

    Focus exclusively on graphical annotations within the drawing views

    Ignore all surrounding textual descriptions(e.g., title blocks, technical notes, tables). Focus exclusively on graphical annotations within the drawing views. 3.Prioritize correctness and clarity. Do not generate vague, redundant, or unanswerable questions. Given Drawing Metadata (Provided as Annotations): •Combination Type:Single drawing or Multiple sub-...

  15. [15]

    Inspect the drawing and identify existing annotations from the categories above

  16. [16]

    For each identified annotation, generate one clear and professional question

  17. [17]

    Each question must explicitly include: • Theannotation type(datum symbol, datum feature, GD&T, limit dimension, roughness, etc.); Theannotation content(specific symbol, value, or notation).Thelocation(which view and which feature of the part)

  18. [18]

    annotations

    If a specific annotation type doesnotappear in the drawing, do not generate a question for that type. Output Format:Return the result strictly in the following JSON format, without any additional explanation or commentary: { "annotations": [ { "type": "annotation type (symbol/feature/roughness/other)", "content": "specific symbol or value", "location": "c...

  19. [19]

    Do not infer nonexistent views, annotations, or parts

    Generate questionsonly based on elements that explicitly appear in the drawing. Do not infer nonexistent views, annotations, or parts

  20. [20]

    Focus exclusively on graphical views, symbols, and part depictions

    Ignore surrounding textual descriptions(e.g., title blocks, notes). Focus exclusively on graphical views, symbols, and part depictions

  21. [21]

    If the drawing containsmultiple sub-figures, clearly specify which sub-figure each question refers to

  22. [22]

    Avoid vague or ambiguous spatial descriptions

    Prioritizeclarity, correctness, and answerability. Avoid vague or ambiguous spatial descriptions. Given Drawing Metadata (Provided): •Combination Type:Single drawing or Multiple sub-figures •Drawing Type:{drawing type}(provided metadata, used as reference) •View Composition:{view info}(provided metadata, used as reference) Objective:Identify different vie...

  23. [23]

    Inspect the drawing and identify spatial or positional information from the categories above

  24. [24]

    Generatefiveprofessional questions covering different location categories when possible

  25. [25]

    Each question must explicitly include: • thelocation type(view / annotation / part / cross-view), • thetarget object(specific view, symbol, part, or feature) and a clearlocation description

  26. [26]

    locations

    If a certain category is not present in the drawing, generate questions from other applicable categories. Output Format: { "locations": [ { "type": "location type (view / annotation / part / cross-view)", "object": "target object", "location_desc": "spatial description", "question": "generated professional question" } ] } Figure 12.Question generation pro...

  27. [27]

    Generate questionsonly for dimensions that are not directly labeledin the drawing

  28. [28]

    3.Do not generate questionsthat require complex geometric reasoning (e.g., trigonometry, Pythagorean theorem)

    The target dimension must be computable usinglinear operations only(addition or subtraction) from explicitly annotated dimensions. 3.Do not generate questionsthat require complex geometric reasoning (e.g., trigonometry, Pythagorean theorem)

  29. [29]

    Ignore surrounding textual descriptions and focus only on graphical content and dimension annotations

    Clearly specify thestart and end locationsof the dimension to be calculated. Ignore surrounding textual descriptions and focus only on graphical content and dimension annotations. Given Drawing Metadata (Provided): •Combination Type:Single drawing or Multiple sub-figures •Drawing Type:{drawing type}(provided metadata, used as reference) •View Composition:...

  30. [30]

    Identify up tofive locations/featuresthat satisfy all of the following: • the dimension isnot explicitly annotated; • the value can be obtained throughlinear calculation(addition or subtraction); • the required referenced dimensions are clearly annotated in the drawing

  31. [31]

    For each identified location, generate a professional question that includes: • thecalculation target(explicit start and end points); • theviewin which the calculation is performed (e.g., front view, top view, section view);thecalculation basis(which annotated dimensions are used)

  32. [32]

    In the front view, what is the distance from the left end face to the center of the middle hole?

    If fewer than five valid locations exist, generate questions for as many as are reasonably supported. Question Examples: • “In the front view, what is the distance from the left end face to the center of the middle hole?” • “In the top view, what is the spacing between the two bosses?” Output Format: { "calculations": [ { "location": "description of the s...