The paper introduces VLM-CAD, a workflow that uses Vision Language Models (VLMs) for analog circuit sizing while mitigating spatial blindness and logical hallucinations. It incorporates a neuro‑symbolic parsing module, Image2Net, to convert schematics into topological graphs and JSON, and an Explainable Trust Region Bayesian Optimization method, ExTuRBO, to guide design decisions with sensitivity evidence. Experiments on 12 sizing tasks across six circuits and four technology platforms show a Strict Pass@1 of 23.3% and a Relaxed Pass@1 of 91.7%.
arXiv:2609.15668v1 Announce Type: cross Abstract: Through pre-training on extensive text and image datasets, current multi-modal large language models (MLLMs) achieve strong performance on general ta...
arXiv:2602.11678v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have shown remarkable progress in visual understanding, yet they suffer from a critical limitation: struct...
arXiv:2605.22902v2 Announce Type: replace-cross Abstract: Generative Vision-Language Models (VLMs) perform well on multimodal reasoning, but how visual inputs are transformed to text remains poorly u...
arXiv:2603. 23867v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts.
arXiv:2607. 11338v1 Announce Type: new Abstract: Symbolic expressions can effectively characterize and predict circuit behavior, but deriving them directly from circuit schematics is challenging.
arXiv:2608. 09374v1 Announce Type: new Abstract: Electrical circuit analysis requires more than recognizing components in an image.
arXiv:2605.13156v2 Announce Type: replace Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in bridging visual perception and natural language understanding, enabling...
arXiv:2605. 14054v2 Announce Type: replace Abstract: Achieving robust perception-reasoning synergy is a central goal for advanced Vision-Language Models (VLMs).
arXiv:2606. 10833v1 Announce Type: new Abstract: Vision-Language Models (VLMs) demonstrate strong performance on general multimodal reasoning benchmarks, yet their ability to perform engineering reasoning remains largely unexplored.
THEIA is a multimodal dataset that pairs thousands of analog circuit layout images with question‑answer conversations, and it introduces a benchmark using a fine‑tuned vision‑language model to analyze GDSII files. The dataset and benchmark enable designers to interact with and query physical layouts as intuitive, meaningful entities. Experiments on five realistic tasks show the fine‑tuned model outperforms general‑purpose vision‑language models by up to 73%, revealing a significant gap between general multimodal reasoning and domain‑specific layout understanding.
arXiv:2606. 06664v1 Announce Type: cross Abstract: Despite high accuracy, Vision Transformer (ViT) predictions can be driven by spurious cues, raising the need to understand their inner workings before safe deployment.
arXiv:2508. 17298v3 Announce Type: replace-cross Abstract: Compositional visual reasoning has emerged as a key research frontier in multimodal AI, aiming to endow machines with the human-like ability to decompose visual scenes, ground intermediate concepts, and perform multi-step logical inference.