OmniSch: A Multimodal PCB Schematic Benchmark For Structured Diagram Visual Reasoning

arXiv cs.CV / 4/2/2026

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Key Points

  • OmniSch is introduced as a new multimodal benchmark for evaluating LMMs’ ability to interpret PCB schematic diagrams and convert them into machine-readable spatially weighted netlist graphs.
  • The dataset covers 1,854 real-world PCB schematics and supports four tasks: visual grounding, diagram-to-graph topological reasoning, geometric reasoning for layout-dependent connection weights, and tool-augmented agentic reasoning.
  • It provides extensive supervision signals, including 109.9K grounded instances that align 423.4K semantic labels to visual regions, targeting fine-grained understanding.
  • The initial results highlight significant weaknesses in current LMMs, such as unreliable fine-grained grounding, brittle layout-to-graph parsing, inconsistent global connectivity reasoning, and inefficient visual search behavior.

Abstract

Recent large multimodal models (LMMs) have made rapid progress in visual grounding, document understanding, and diagram reasoning tasks. However, their ability to convert Printed Circuit Board (PCB) schematic diagrams into machine-readable spatially weighted netlist graphs, jointly capturing component attributes, connectivity, and geometry, remains largely underexplored, despite such graph representations are the backbone of practical electronic design automation (EDA) workflows. To bridge this gap, we introduce OmniSch, the first comprehensive benchmark designed to assess LMMs on schematic understanding and spatial netlist graph construction. OmniSch contains 1,854 real-world schematic diagrams and includes four tasks: (1) visual grounding for schematic entities, with 109.9K grounded instances aligning 423.4K diagram semantic labels to their visual regions; (2) diagram-to-graph reasoning, understanding topological relationship among diagram elements; (3) geometric reasoning, constructing layout-dependent weights for each connection; and (4) tool-augmented agentic reasoning for visual search, invoking external tools to accomplish (1)-(3). Our results reveal substantial gaps of current LMMs in interpreting schematic engineering artifacts, including unreliable fine-grained grounding, brittle layout-to-graph parsing, inconsistent global connectivity reasoning and inefficient visual exploration.