The 2026 IEEE International Conference on LLM-Aided Design (LAD) will focus on how to use LLM (Large Language Model) technology to help design circuits, software, and computing systems with improved quality, productivity, robustness, and cost. It is the first international conference dedicated to this topic, aiming to showcase results that leverage generative-AI advances and provide methods and solutions for design automation, software development, and other fields. The conference will host leading researchers, present open-source LLM models, datasets, tool flows, and offer benchmarking, testing, and validation methods.
The main theme of LAD this year will revolve around agentic optimization and scaling inference-time methods, but we welcome a broad range of topics on new methodologies, tools, datasets, and benchmarks pertaining to:
● Agentic workflows for design automation and optimization
● LLM inference-time techniques for design
● LLM-aided HW/SW design, code generation, and test plan generation
● System-level design methodology development with LLMs
● Finetuning of large foundation models for specialization in design automation
● New datasets and benchmarks of relevance to LLM-aided design
● Evaluation and verification of LLM-aided designs
● LLM-aided design for software development, IT automation, site reliability, and regulatory compliance.
● LLMs for EDA, including RTL, HLS, physical design, and EDA scripting
● LLMs for reasoning and logic used in design process
● Computational efficiency of LLM-aided design tools
● Data science and data analytics for LLM-aided design
● Security of LLM-generated designs
● Privacy, copyright, and other regulatory concerns around LLM-aided design
● LLM-aided bug-fixing
● LLM-aided design for various application domains, such as 3D manufacturing, material or drug discovery, sustainability, cybersecurity, etc.
● LLM-aided design for emerging technologies, such as quantum computing, neuromorphic computing, and electro-optic co-design
IMPORTANT DATES
Abstract Submission: Mar 2nd, 2026 (AoE)
Full Paper Submission: Mar 9th, 2026 (AoE)
Notif. of Acceptance: May 12th, 2026 (AoE)
Camera ready paper: Jun 6th, 2026 (AoE)
SUBMISSION INSTRUCTIONS
The conference invites up to 6-page regular papers in the IEEE Conference format (https://www.ieee.org/conferences/publishing/templates.html). Page limits do not include references. Papers should be anonymized for double-blind peer review. We encourage papers with a commitment to open and reproducible research, including datasets and methods. Papers with open-source implementations will be highlighted at the conference. All papers will be published on IEEEXplore. Papers can be submitted via OpenReview (https://openreview.net/group?id=IEEE.org/LAD/2026) by Mar 2nd, 2026 (AoE). See the conference website for more details.
DATASETS AND BENCHMARKS PAPERS
LAD'26 welcomes papers describing new Datasets and Benchmarks of relevance to the LLM-Aided Design community. Papers describing new datasets and benchmarks must follow the exact same rules and procedures as regular (up to) 6-page papers; they will be peer reviewed; and accepted papers will be published in the proceedings. Datasets and Benchmarks papers must include an explicit commitment to releasing all artifacts publicly if accepted. The commitment should be added to the Conclusion section of the paper.
PAPER FORMATTING
Authors should follow the recommended IEEE Conference format for their submissions to ensure compatibility with IEEEXplore. Manuscripts must be anonymized to avoid disclosing author identities. LAD’26 encourages open-source and reproducible research. Authors can provide anonymized URLs to their datasets and methods in the paper, or commit to open release on paper acceptance (this is not mandatory for regular papers). For Datasets and Benchmarks papers, there is a specific requirement to release all artifacts publicly if accepted.
POLICY ON SUBMISSIONS
LAD’26 expects previously unpublished papers describing original research. Accepted LAD’26 papers will appear on IEEEXplore and count as formal, copyrighted, archival publications.
ORGANIZING COMMITTEE
General Chairs Azaliza Mirhosseini (Stanford), Yong Liu (Cadence)
Program Chairs JV Rajendran (TAMU), Cong Hao (GaTech), Jimmy Cheng (Synopsys)
Finance Chair Chenglong Miao (Cadence)
Publicity Chair Jeff Goeders (BYU)
Local Chair Ehsan Degan (IBM)
Web Chairs Kaiwen Cao (UIUC), Feilian Huang (Cadence), Zijian Ding (UCLA)
Sponsorship Chair Nathaniel Pinckney (NVIDIA)
Panel Chair Rajeev Jain (Qualcomm)
Publications Chairs Kanad Basu (UT Dallas), Jingyu Pan (Cadence)
Competition Chairs Mark Ho (NVIDIA), Ivan Lobov (Google), Vidya Chhabria (ASU),
Sergio Guadarrama (Google)
Keynotes Chair Anna Goldie (Ricursive Intelligence)
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