{"id":"a4404955-2a51-4267-8fb6-eb220d96dcec","arxiv_id":"2503.11658","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"Graph-based hierarchical retrieval of circuit diagrams achieves 0.881 top-5 type-level accuracy, beating image-based baselines (best 0.627), by extracting components and nets from images and comparing graph structures.","lead":"This paper builds a system that turns images of analog circuit diagrams into graphs and retrieves similar circuits by comparing those graphs. It reports that this graph-based approach outperforms standard image-based retrieval on a custom dataset of 503 circuit diagrams.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Retrieval accuracy is scored as 'same type as query', so the headline AP=0.881 does not test the paper's core claim that topological graph structure drives retrieval, since same-type circuits can differ topologically and different-type circuits can share component counts.","rationale":"The reader's weakest assumption correctly identifies the same-type proxy as the load-bearing vulnerability in the central claim. My stress-test review confirms this: the paper's headline result would only demonstrate that the proposed graph representation outperforms image features for a coarse functional label, not that topological structure is what drives retrieval. The concrete test I propose directly replaces the coarse label with a fine-grained netlist-based ground truth, which would either substantiate the topology claim or show that the advantage is an artifact of component-count correlations. The query-in-database issue is a secondary confound that affects the absolute AP values but not necessarily the relative ordering; I mention it to strengthen the need for a clean held-out evaluation. The paper's pipeline is coherent and the recognition results are suggestive, but the retrieval evaluation as designed cannot distinguish the proposed method's claimed topological benefit from simpler type-level signals. Therefore the verdict stays CONDITIONAL, pending the fine-grained validation.","tokens_in":14820,"tokens_out":8166,"duration_ms":81604,"concrete_test":"Construct a gold-standard netlist graph for each of the 503 database diagrams (manually or from source netlists), and recompute retrieval AP using a fine-grained ground truth: a retrieved diagram is correct if its gold graph is isomorphic to the query's gold graph or has GED below a small threshold. Then compare hierarchical graph retrieval to the three image baselines on this fine-grained metric, and also report leave-one-out AP (query excluded from the database). If the graph method's AP drops toward or below the image baselines, or if same-type but topologically distinct circuits are retrieved ahead of different-type but topologically similar circuits, the same-type proxy—not topology—was driving the reported result.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The strongest claim—that graph representation enables topology-aware retrieval that beats image retrieval—is not actually tested by the paper's metric. In Section IV.B.2, AP_retrieval counts a hit when retrieved and query diagrams share the same coarse type label (e.g., 'buck'). The dataset contains only 10 types, so same-type is a weak proxy for the functional/topological similarity the method claims to exploit. Two same-type circuits can have different topologies (e.g., synchronous vs asynchronous buck, different feedback/compensation networks), while a buck and a boost converter can share component types and similar device counts. Because the experimental graph representations are simplified—undirected, node labels only (device/net type), edge labels omitted—GED between two graphs is largely determined by counts of component types and coarse connectivity. This makes it plausible that the 0.881 AP reflects type-level confounds (component inventory correlates with function) rather than the topological fidelity the paper emphasizes. The comparison to image-based methods uses the same type proxy; if image features are more sensitive to drawing style than to component inventory, the graph method's advantage may be an artifact of the label. Additionally, the retrieval protocol does not state that query diagrams are held out from the 503-image database, so self-matches could inflate AP for all methods. The paper provides no evidence that top-5 retrievals are topologically more similar to the query than are other same-type or different-type circuits.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","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.","tokens_in":15244,"tokens_out":9306,"duration_ms":77462,"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":[{"comment":"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.","section":"Section IV.B.2"},{"comment":"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.","section":"Section IV.B.1/IV.B.3"},{"comment":"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.","section":"Section IV.A.3, Table IV"},{"comment":"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.","section":"Section IV.A.2"},{"comment":"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.","section":"Section IV.B.3, Table VI"}],"minor_comments":[{"comment":"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').","section":"Abstract"},{"comment":"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.'","section":"Section II.A"},{"comment":"The grayscale conversion formula is missing explicit multiplication symbols; it should be written as GRAY = 0.299R + 0.587G + 0.114B.","section":"Section III.B.2, Eq. (4)"},{"comment":"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.","section":"Table III"},{"comment":"The caption reads 'CIRCUIT DIAGRAM RECOGNITION RESULT' but the table reports retrieval AP; it should be 'CIRCUIT DIAGRAM RETRIEVAL RESULT.'","section":"Table VII caption"},{"comment":"Figure 10 is duplicated: the same figure appears twice with slightly different captions. One copy should be removed.","section":"Figure 10"},{"comment":"The dataset description contains a typo: 'totaling 2 275 images (including 2 3304 circuit components)' – the stray '2' before '3304' should be removed.","section":"Section IV.A.1"},{"comment":"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.","section":"Table VI"},{"comment":"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.","section":"Section IV.C"}],"recommendation":"major_revision","confidential_remarks":"The paper addresses a practical problem, but the current evaluation does not support the central claim that graph topology improves retrieval. The 'same type' metric is a major weakness, and the holdout issue must be resolved. I recommend a major revision. Additionally, the related work section does not cite any prior work on circuit diagram retrieval specifically, so the 'pioneering' claim may be overstated; a more thorough literature search is needed. The dataset is self-constructed and not released, which limits reproducibility. The paper fits the scope of the journal but the impact is modest unless the evaluation is strengthened."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: this is a solid engineering paper with a modest, real contribution. The new thing is the full pipeline — GAM-YOLO component detection, two-step connected-domain net extraction, graph construction, and two-level GED retrieval with Class 1 then Class 4 representations. I haven't seen that exact combination applied to circuit diagram retrieval, and the authors cite the closest prior work ([21]) honestly.\n\nWhat it does well: the recognition numbers are plausible (mAP50 0.902 on 227 test images, 223/250 diagrams judged topologically correct), and the hierarchical retrieval achieves the Class 4 AP at roughly a quarter of the runtime. The qualitative example in Fig. 10 is genuinely informative: the graph method finds the same circuit with a different routing direction, while the image method does not. That is the strongest evidence in the paper for the core claim.\n\nThe soft spots are in the retrieval evaluation, and the stress-test note is right. AP_retrieval counts a hit when the retrieved diagram has the same type label as the query (e.g., both buck). With only 10 types, that is a weak proxy for the topological similarity the method claims to exploit. Same-type circuits can have different topologies (synchronous vs. asynchronous buck, different compensation networks), and different-type circuits can share component counts. So AP = 0.881 does not demonstrate that graph topology drives retrieval; it may partly reflect component inventory. The graph simplification — undirected, node labels only, no edge labels — makes this concern worse.\n\nTwo smaller issues: Table IV gives no YOLOv8 baseline, so the GAM attention module's contribution is not established, and the retrieval protocol doesn't state whether query diagrams are held out from the 503-image database. No error bars, no significance tests, no code or data. None of these are fatal; all are addressable in revision.\n\nWho benefits: analog designers looking for a practical way to search legacy schematic archives, and people working on plagiarism screening. The paper deserves a serious referee. I'd recommend major revision with a real topology-level retrieval metric (e.g., human-annotated functional similarity or netlist equivalence), a YOLOv8 ablation, and code/data release. If you work in EDA, it's worth a read; otherwise it's a competent niche contribution.","headline":"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.","tokens_in":15635,"tokens_out":2465,"would_cite":false,"duration_ms":25245,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Circuit diagram retrieval should be treated as graph retrieval, not image retrieval.","keywords":["circuit diagram retrieval","graph representation","graph edit distance","hierarchical retrieval","analog circuits","circuit recognition","GAM-YOLO","topological similarity"],"falsifier":"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.","tokens_in":14654,"feed_emoji":"🔌","tokens_out":7536,"duration_ms":68410,"temperature":0.7,"pith_summary":"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.","feed_headline":"Circuit graphs beat pixels at diagram retrieval","feed_subtitle":"Graph-based search retrieves the right circuit type 88.1 percent of the time versus 62.7 for image-based methods.","key_machinery":"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.","core_discovery":"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.","pith_inferences":["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."],"forward_implications":["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."],"supporting_citations":[{"why":"Supplies the YOLOv8 object detection architecture that GAM-YOLO modifies for circuit component recognition.","marker":"[43]"},{"why":"Defines graph edit distance, the similarity metric used in both retrieval stages.","marker":"[46]"},{"why":"Provides the enhanced A* algorithm for computing GED and the complexity analysis that motivates the hierarchical design.","marker":"[48]"},{"why":"Supplies the first image-based retrieval baseline against which the graph method is compared.","marker":"[49]"},{"why":"Supplies the second and third image-based retrieval baselines, with SVD/PCA and query expansion.","marker":"[50]"},{"why":"Source of the Class 1 graph representation used in the coarse retrieval stage.","marker":"[17]"},{"why":"Source of the Class 4 graph representation used in the fine retrieval stage.","marker":"[16]"}],"fun_headline_variants":["Graphs outscore pixels in circuit diagram retrieval","Hierarchical circuit graphs beat image search by 25 points","Circuit retrieval: graphs 88% vs pixels 63% accuracy","Graph-based circuit retrieval is more accurate and faster","Topology beats texture for circuit diagram search"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"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.","fun_headline_variants_meta":{"raw":{"variants":["Graphs outscore pixels in circuit diagram retrieval","Hierarchical circuit graphs beat image search by 25 points","Circuit retrieval: graphs 88% vs pixels 63% accuracy","Graph-based circuit retrieval is more accurate and faster","Topology beats texture for circuit diagram search"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000903,"raw_usage":{"total_tokens":3850,"prompt_tokens":873,"completion_tokens":2977,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":489,"completion_tokens_details":{"reasoning_tokens":2901}},"tokens_in":489,"tokens_out":2977,"duration_ms":22019,"temperature":1.0,"reasoning_tokens":2901,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-09T10:44:46.794841+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"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.","supporting_citations":[{"cited_title":"YOLOv8: A Novel Object Detection Algorithm with Enhanced Perfor mance and Robustness,","cited_arxiv_id":null,"evidence_quote":"Supplies the YOLOv8 object detection architecture that GAM-YOLO modifies for circuit component recognition."},{"cited_title":"A Distance Measure between Attributed Relational Graphs for Pattern -Recognition,","cited_arxiv_id":null,"evidence_quote":"Defines graph edit distance, the similarity metric used in both retrieval stages."},{"cited_title":"Accelerating Graph Similarity Search via Efficient GED Computation,","cited_arxiv_id":null,"evidence_quote":"Provides the enhanced A* algorithm for computing GED and the complexity analysis that motivates the hierarchical design."},{"cited_title":"Learning with average precision: Training image retrieval with a listwise loss,","cited_arxiv_id":null,"evidence_quote":"Supplies the first image-based retrieval baseline against which the graph method is compared."},{"cited_title":"PyRetri: A PyTorch-based library for unsupervised image retrieval by Deep Convolutional Neural Networks,","cited_arxiv_id":null,"evidence_quote":"Supplies the second and third image-based retrieval baselines, with SVD/PCA and query expansion."},{"cited_title":"A Novel Analog Physical Synthesis Methodology Integrating Existent Design Expertise,","cited_arxiv_id":null,"evidence_quote":"Source of the Class 1 graph representation used in the coarse retrieval stage."},{"cited_title":"Pretraining Graph Neural Networks for Few-Shot Analog Circuit Modeling and Design,","cited_arxiv_id":null,"evidence_quote":"Source of the Class 4 graph representation used in the fine retrieval stage."}],"review_version":1}