arXiv AI

AutoVSR: Automatic Visual-to-Symbolic Reasoning for Symbolic Expression Generation from Circuit Schematic

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 AI

Aug 11

CircuitReason-1k: Benchmarking Long-Horizon Visual-to-Symbolic Reasoning inElectrical Circuits

arXiv:2608. 09374v1 Announce Type: new Abstract: Electrical circuit analysis requires more than recognizing components in an image.

By Xinqi Yang, Kang An, Tengyue Wang, Zhongyu Yang, Chenxu Du, Yuanchi Zhu, Hebao Zhu, Ziliang Wang, Faqiang Qian, Yunli Yang, Qibing Ren

llms multimodal benchmarks

arXiv AI

Jul 1

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation

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.

By Weixin Chen, Antonio Vergari, Han Zhao

llms fine-tuning multimodal

arXiv Machine Learning

Jul 3

SINA: A Fully Automated Circuit Schematic Image to Netlist Generator Using Artificial Intelligence

arXiv:2607. 01609v1 Announce Type: new Abstract: Recent advances in Artificial Intelligence (AI) have revolutionized Electronic Design Automation (EDA), particularly through Large Language Models (LLMs) for circuit design tasks.

By Saoud Aldowaish, Yashwanth Karumanchi, Kai-Chen Chiang, Mohammed Ayman Habib, Finn Murphy, Rishen Cao, Morteza Fayazi

llms computer-vision multimodal benchmarks

Hugging Face Trending Papers

Jul 2

SINA: A Fully Automated Circuit Schematic Image to Netlist Generator Using Artificial Intelligence

Recent advances in Artificial Intelligence (AI) have revolutionized Electronic Design Automation (EDA), particularly through Large Language Models (LLMs) for circuit design tasks. However, their application to analog and mixed-signal domains remains limited by the lack of machine-readable representations of existing circuit design knowledge.

llms computer-vision multimodal benchmarks

arXiv AI

Jun 29

Verifiable Geometry Problem Solving: Solver-Driven Autoformalization and Theorem Proposing

arXiv:2606. 27926v1 Announce Type: new Abstract: Geometry Problem Solving have increasingly adopt the neuro-symbolic paradigm, combining neural intuition with symbolic rigor.

By Can Li, Ting Zhang, Junbo Zhao, Hua Huang

agents reinforcement-learning multimodal

arXiv Machine Learning

Jul 3

A Survey of Circuit Foundation Model: Foundation AI Models for VLSI Circuit Design and EDA

arXiv:2504. 03711v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI)-driven electronic design automation (EDA) techniques have been extensively explored for VLSI circuit design applications.

By Wenji Fang, Jing Wang, Yao Lu, Shang Liu, Yuchao Wu, Yuzhe Ma, Zhiyao Xie

llms fine-tuning

arXiv AI

Jun 10

Do VLMs Reason Like Engineers? A Benchmark and a Stage-wise Evaluation

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.

By Syed Wasiq, Syed Mohamad Tawseeq, Yashwant Pravinrao Bangde, Debaditya Roy

llms nlp multimodal benchmarks

arXiv AI

Jul 15

FormalAnalyticGeo: A Neural-Symbolic Based Framework for Multimodal Analytic Geometry Problem Generation

arXiv:2607. 12982v1 Announce Type: new Abstract: Math reasoning has achieved significant progress with the rapid advancement of Multimodal Large Language Models (MLLMs), however analytic geometry remains largely underexplored, primarily due to the scarcity of annotated samples.

By Ruoran Xu, Wending Gao, Qiufeng Wang

llms multimodal

arXiv AI

1d ago

Euclid-Omni : A Unified Neuro-Symbolic Framework for Plane Geometry

arXiv:2608. 14585v1 Announce Type: new Abstract: Euclidean geometry is a compelling testbed for AI reasoning, as it demands the combination of intuitive diagram understanding, axiomatic deduction, and algebraic computation.

By Zhaoyu Li, Hangrui Bi, Youyuan Zhang, Wenjie Ma, Zenan Li, Zhaolei Zhang, Xujie Si, Kaiyu Yang

llms multimodal benchmarks

arXiv Machine Learning

Aug 10

Symbolic Graphics Programming with Large Language Models

arXiv:2509. 05208v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise visual content remains underexplored.

By Yamei Chen, Haoquan Zhang, Yangyi Huang, Zeju Qiu, Kaipeng Zhang, Yandong Wen, Weiyang Liu

llms reinforcement-learning multimodal benchmarks

arXiv AI

Jul 9

Explain Before You Answer: A Survey on Compositional Visual Reasoning

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.

By Fucai Ke, Joy Hsu, Zhixi Cai, Zixian Ma, Xin Zheng, Xindi Wu, Sukai Huang, Weiqing Wang, Pari Delir Haghighi, Gholamreza Haffari, Ranjay Krishna, Jiajun Wu, Hamid Rezatofighi

llms agents multimodal benchmarks safety

Hugging Face Trending Papers

Jul 15

SD-MAR: Multi-image Analytical Reasoning via Synthetic Data and Reinforcement Learning

Vision Language Models (VLMs) demonstrate strong perceptual abilities but remain limited in tasks requiring analytical reasoning across multiple visual states, such as multi-image comparison, change detection, and multi-step visual inference. These capabilities are critical for real-world multimodal applications where reasoning must be grounded in systematic differences between visual contexts.

llms reinforcement-learning fine-tuning multimodal benchmarks

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