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REVIEW 5 major objections 9 minor 50 references

Circuit Diagram Retrieval Based on Hierarchical Circuit Graph Representation

T0 review · 5 major / 9 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read Circuit diagram retrieval should be treated as graph retrieval, not image retrieval.

desk verdict A coherent engineering pipeline for graph-based circuit diagram retrieval, with a real but narrow contribution and an evaluation metric that does not yet prove the topological claim. read the letter →

arxiv 2503.11658 v1 pith:6QUCFAXO submitted 2025-02-05 cs.AR cs.AI

Ming Gao , Ruichen Qiu , Zeng Hui Chang , Kanjian Zhang , Haikun Wei , Hong Cai Chen This is my paper · ORCID
classification cs.ARcs.AI
keywords circuitdiagramretrievalgraphrepresentationeditdistancehierarchicalanalogcircuitsrecognitionGAM-YOLOtopologicalsimilarity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that circuit diagrams are best retrieved by their topology rather than their pixels. It turns a query diagram into a circuit graph with a detection model plus a two-step connected-domain filter, then ranks a database of schematics by graph edit distance. On its 503-diagram database, the proposed hierarchical search reaches an average top-5 precision of 0.881, which matches the most detailed graph representation alone while cutting retrieval time from 252 seconds to about 58 seconds per query, and it outperforms the best image-based baseline at 0.627. If the claim holds, engineers could search legacy schematics and check design similarity by structure, catching circuits that look different but are the same and missing visual lookalikes that are electrically different.

What carries the argument

The load-bearing object is the circuit graph representation: five construction classes are catalogued, and the retrieval pipeline uses Class 1 (devices as nodes, nets as edges) for the coarse stage and Class 4 (devices and device pins as nodes, nets as edges) for the fine stage. Similarity is measured by normalized graph edit distance (GED), the minimum number of node and edge insertions, deletions, and relabelings needed to turn one graph into another, computed by an enhanced A* algorithm. Retrieval is hierarchical: rank the whole database by Class 1 graphs, keep the top 20, re-rank those with Class 4 graphs, and return the top 5. This two-stage design recovers nearly all the accuracy of the expensive Class 4 representation while paying only a fraction of its time cost.

What would settle it

Build a benchmark where ground truth is netlist-level electrical equivalence or exact circuit topology rather than a coarse type label, and compare the hierarchical graph method with image retrieval on it. A direct check is to take queries that are same-type but rewired and queries that are different-type but isomorphic, and see whether the ranking follows topology or the label; if precision collapses to the image baseline, the central claim fails.

Watch

Extended reading notes

Core claim

The central claim is that a hierarchical circuit graph representation—first a coarse Class 1 graph, then a detailed Class 4 graph—gives retrieval accuracy of 0.881 average precision on the constructed dataset, matching the most detailed representation alone while reducing average retrieval time from 252.15 seconds to 57.86 seconds per query. The same pipeline achieves 0.892 accuracy in converting circuit diagrams into graphs, which the paper presents as the enabling step that makes graph retrieval practical. The paper also claims this graph route surpasses three image-based retrieval methods, whose best average precision is 0.627, because pixel features ignore the topological structure that determines circuit function.

Load-bearing premise

The 0.881 accuracy assumes that two diagrams labeled the same circuit type is the right definition of a correct retrieval; if same-type circuits can be electrically different, or different-type circuits can share a topology, the score does not show that graph topology is what drives the results.

Editorial extensions

If this is right

  • Engineers could retrieve legacy schematics by structural similarity, so the same circuit drawn with different routing directions is still found.
  • The coarse-to-fine two-stage ranking is a transferable pattern for any retrieval task where the accurate similarity measure is too expensive to run on the whole database.
  • Since retrieval accuracy tracks graph detail from Class 1 to Class 4, improving component and wire extraction from diagrams should directly raise retrieval precision.
  • The large gap over image baselines suggests that pixel embeddings miss exactly the connectivity information that determines circuit function.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The evaluation defines a correct hit as same circuit type; a stricter benchmark based on electrical equivalence or exact topology could change the reported gap, so the practical gain for plagiarism detection is not yet measured by this experiment.
  • The hierarchy could scale beyond the 503-diagram database if the graph edit distance stage is replaced or accelerated by a learned graph embedder, which the paper itself lists as future work.
  • Because Class 5 representations exist for CMOS and bipolar circuits, a specialized third retrieval stage could sharpen results on transistor-level queries.
  • A reasonable reading is that the method is a high-recall structural pre-filter that still needs a finer equivalence check before it can support legal or design-integrity decisions.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 9 minor

Summary. This paper presents a complete pipeline for retrieving analog circuit diagrams from a database by converting each diagram into a graph and comparing graphs via graph edit distance (GED). The pipeline has two stages: (1) recognition, which uses a modified YOLOv8 (GAM-YOLO) to detect components and a two-step connected-domain filtering method to extract net-level topology from the image, and (2) retrieval, which first performs a coarse search with a simple device-as-node graph representation (Class 1) to obtain 20 candidates, then a fine search with a device-and-pin representation (Class 4) on those candidates to produce the final top-5. Experiments on a self-built 503-image database with 10 circuit types report an average retrieval precision of 0.881 for the hierarchical method, identical to Class 4 alone, with a much lower average time (57.86 s vs. 252.15 s). The proposed method is also reported to outperform three image-based retrieval baselines (best APretrieval=0.627). The paper also reports component detection mAP50=0.902 and topology recognition precision of 0.892.

Significance. The idea of using graph representations for circuit diagram retrieval is sensible and addresses a real need in analog design reuse and plagiarism detection. The hierarchical retrieval strategy is a reasonable engineering solution to the computational cost of exact GED. If the results were confirmed with a stronger evaluation, the paper would demonstrate a clear advantage of structure-aware retrieval over pixel-based retrieval for this domain. However, the current significance is limited by the evaluation design. The retrieval metric based on 'same type' is too coarse to validate the topological-fidelity claim, and the absence of a holdout protocol and statistical tests weakens the empirical comparison. The paper does not provide code or data, and the GAM-YOLO contribution is not ablated against its base model. These issues prevent the current version from fully supporting the paper's central claims.

major comments (5)
  1. [Section IV.B.2] The retrieval accuracy metric in Eq. (9) counts a hit when the retrieved diagram has the same circuit type as the query. With only 10 types in the database, this does not measure the topological fidelity the paper claims is the advantage of graph-based representation. Two circuits of the same type can have substantially different topologies (e.g., different compensation networks or device counts), and two different types can share similar component inventories. Thus the APretrieval=0.881 in Table VI does not demonstrate that graph topology drives the retrieval; it may reflect type-level confounds. The authors should re-evaluate with a ground-truth based on structural similarity (e.g., graph isomorphism classes, expert-rated similarity) or at least report per-type results and show that retrieved same-type circuits are topologically more similar to the query than non-retrieved ones.
  2. [Section IV.B.1/IV.B.3] The retrieval protocol does not state whether the query diagrams are held out from the 503-image database. If each query is also present in the database, the top-1 result will be the query itself, artificially inflating AP for every method. The paper must clarify the experimental split, and if queries are not excluded, the evaluation should be repeated using leave-one-out (or a fixed query/database split) to ensure the comparison between graph-based and image-based methods is fair.
  3. [Section IV.A.3, Table IV] GAM-YOLO is presented as the proposed detector, but Table IV reports only its performance. Without a baseline comparison to the standard YOLOv8 under identical training and testing conditions, the claim that adding the GAM attention module improves component detection is not supported. An ablation or baseline result is needed to justify the contribution of the detection component.
  4. [Section IV.A.2] The evaluation of topology recognition is described as a manual judgment using 'professional circuit knowledge,' but no details are given about the annotation protocol: how many experts, how disagreements were resolved, or whether any inter-rater reliability was measured. The reported precision of 0.892 is therefore not reproducible. The authors should either construct a formal ground-truth (e.g., netlists extracted from the source of the circuit images) or, at minimum, document a structured multi-annotator protocol.
  5. [Section IV.B.3, Table VI] The table reports a single APretrieval per representation without error bars, standard deviation, or any significance test. The claim that the hierarchical method achieves the same accuracy as Class 4 (0.881) while being faster is statistically unsupported; the identical number could be a coincidence. The authors should report the distribution of per-query precisions (e.g., mean ± std, or a box plot) and, ideally, perform a paired significance test between the hierarchical method and Class 4, and between the graph-based method and the best image-based baseline.
minor comments (9)
  1. [Abstract] The abstract contains grammatical errors such as 'retrieval similar images' (should be 'retrieve similar images') and 'across of different types' (should be 'across different types').
  2. [Section II.A] The text states 'G is the set of nodes and E, the set of edges'; it should read 'V is the set of nodes and E is the set of edges.'
  3. [Section III.B.2, Eq. (4)] The grayscale conversion formula is missing explicit multiplication symbols; it should be written as GRAY = 0.299R + 0.587G + 0.114B.
  4. [Table III] Several category labels are misspelled or unclear, e.g., 'Biodioe2' (likely 'Bidiode 2') and 'notCon'/'notCon2' (possibly 'Not Connected 1/2'). Please use consistent, descriptive names.
  5. [Table VII caption] The caption reads 'CIRCUIT DIAGRAM RECOGNITION RESULT' but the table reports retrieval AP; it should be 'CIRCUIT DIAGRAM RETRIEVAL RESULT.'
  6. [Figure 10] Figure 10 is duplicated: the same figure appears twice with slightly different captions. One copy should be removed.
  7. [Section IV.A.1] The dataset description contains a typo: 'totaling 2 275 images (including 2 3304 circuit components)' – the stray '2' before '3304' should be removed.
  8. [Table VI] In the Hierarchical row, the node/edge counts are given as '6.27(Class1)/13.21(Class4)' and '9.53/14.35', but the Class 4 column reports 13.47 and 14.60. Clarify whether these are averages over the top-20 subset or the full database, and why they differ.
  9. [Section IV.C] The image-based baselines are described only briefly; please provide implementation details such as whether they were trained/fine-tuned on the circuit image database, the feature dimensions, and any preprocessing. Without these, the comparison is difficult to reproduce.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the retrieval metric uses external type labels and GED is a standard graph distance with no fitted parameters.

full rationale

I walked the derivation chain from circuit diagram to graph representation to retrieval result. The graph construction maps devices, nets, and pins to nodes and edges following a stated taxonomy (Section II). Similarity is computed by a standard graph edit distance normalized by node counts (Eqs. 6-7), and no parameter of that similarity function is fitted to the retrieval labels. The retrieval accuracy is defined by an external criterion: a hit occurs when the retrieved diagram and the query diagram have the same circuit type (Section IV.B.2, Eq. 9). The GAM-YOLO detector is trained on component detection labels and is evaluated separately (Tables IV-V), so the retrieval AP is not statistically forced by a fitted parameter. The hierarchical method is an algorithmic cascade: Class 1 retrieves a top-20 candidate set and Class 4 reranks that set; the fact that its reported AP equals the full Class 4 AP is an empirical outcome, not an identity by construction. No load-bearing self-citation, imported uniqueness theorem, or ansatz smuggled in by citation appears. The only concern is the coarseness of the same-type proxy and the self-constructed dataset, which is an external evaluation-validity issue rather than circularity in the derivation. Therefore no specific circular step can be exhibited, and the score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim depends on these assumptions: the recognition pipeline must yield accurate graphs, graph similarity must correspond to circuit similarity, and the type-level evaluation metric must measure the claimed phenomenon. The first three axioms are plausible for simple schematics but are not validated in the paper.

free parameters (3)
  • First-stage candidate count = 20
    Number of diagrams kept after Class 1 retrieval and passed to Class 4 re-ranking (Section III.C.2). Chosen without sensitivity analysis; affects the accuracy-time trade-off.
  • Final top-K count = 5
    Number of final retrieval results, used in APretrieval calculation (equation (9)). Chosen without justification.
  • Connected-domain filter size threshold = 10% of image pixels
    First-step connected-domain filter removes components smaller than 10% of image pixels (Section III.B.2). Chosen without sensitivity analysis.
assumptions (4)
  • domain assumption Similar circuit graph structure implies similar circuit function
    The retrieval premise: circuits with similar graph topology are considered similar and should be retrieved (Section III.A). No functional equivalence is defined.
  • domain assumption Nets are recoverable as filtered connected components of the binarized image
    The 2-step connected-domain filtering treats remaining connected domains as nets (Section III.B.2). This assumes wires are contiguous pixel regions, ignoring crossings, broken lines, and text-like artifacts that survive filtering.
  • domain assumption Same circuit type is a valid proxy for correct retrieval
    Section IV.B.2 defines retrieval accuracy as whether retrieved diagram type matches query type. This assumption underlies all quantitative retrieval claims.
  • domain assumption Manual professional judgment of generated graphs is accurate
    Section IV.A.2 states topology recognition is evaluated by manual judgment using professional circuit knowledge, with no objective ground truth or inter-rater check.

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Pith. "Pith review of Circuit Diagram Retrieval Based on Hierarchical Circuit Graph Representation." pith.science (2026). https://pith.science/paper/6QUCFAXO

@misc{pith2026250311658,
  author       = {Pith},
  title        = {Pith review of: Circuit Diagram Retrieval Based on Hierarchical Circuit Graph Representation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6QUCFAXO}},
  note         = {Machine review of arXiv:2503.11658}
}
read the original abstract

In the domain of analog circuit design, the retrieval of circuit diagrams has drawn a great interest, primarily due to its vital role in the consultation of legacy designs and the detection of design plagiarism. Existing image retrieval techniques are adept at handling natural images, which converts images into feature vectors and retrieval similar images according to the closeness of these vectors. Nonetheless, these approaches exhibit limitations when applied to the more specialized and intricate domain of circuit diagrams. This paper presents a novel approach to circuit diagram retrieval by employing a graph representation of circuit diagrams, effectively reformulating the retrieval task as a graph retrieval problem. The proposed methodology consists of two principal components: a circuit diagram recognition algorithm designed to extract the circuit components and topological structure of the circuit using proposed GAM-YOLO model and a 2-step connected domain filtering algorithm, and a hierarchical retrieval strategy based on graph similarity and different graph representation methods for analog circuits. Our methodology pioneers the utilization of graph representation in the retrieval of circuit diagrams, incorporating topological features that are commonly overlooked by standard image retrieval methods. The results of our experiments substantiate the efficacy of our approach in retrieving circuit diagrams across of different types.

Figures

Figures reproduced from arXiv: 2503.11658 by the authors.

Figure 4
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Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.