Call for Papers: RExAI 2026

International Workshop on Formal Requirements Engineering and Artificial Intelligence

May 4, 2026 

co-located with NFM 2026 (May 5 - May 7, 2026)

Los Angeles, California, USA

Overview

This workshop explores the intersection of formal requirements engineering and artificial intelligence (AI), addressing a central challenge in modern software and AI system development: how to precisely specify requirements for increasingly complex, heterogeneous, and autonomous systems, and how to verify and validate that such systems meet those requirements.

As AI technologies become deeply embedded in safety and mission-critical domains, from autonomous vehicles to medical diagnostics, financial systems to industrial automation and space exploration, the need for rigorous, formal approaches to requirements is becoming increasingly important. At the same time, traditional requirements engineering methods face new challenges when applied to systems with learning-enabled components, unpredictable behaviors, and emergent properties. The opacity of AI models and the semantic gap between high-level requirements and low-level model inputs and internals create significant impediments to verifying and validating that such systems meet their specified requirements.

The workshop welcomes extended abstract contributions on formal specification languages for AI systems, verification and validation techniques, requirements for trustworthy AI, case studies from real-world applications, and novel applications of AI to requirements engineering itself. We aim to foster dialogue between communities that have traditionally worked separately, building bridges toward more reliable, safe, and trustworthy AI systems grounded in rigorous requirements practices. Extended abstracts can summarize and cite results from recent published paper(s) and/or state your perspective.

Areas of interest include but are not limited to:

• How can we formally specify requirements for systems with learning-enabled components?
• How can formal frameworks capture fairness, safety, robustness and explainability requirements for AI systems?
• How do we verify that AI systems meet their specified requirements?
• What role can AI play in automating requirements elicitation, formalization analysis, and validation?
• How do we bridge high-level requirements and behavior of AI-enabled systems to enable traceability, safety assurance, and certification?

Important dates:

• Submission deadline: March 16, 2026
• Notification: April 3, 2026
• Workshop: May 4, 2026

Paper submission guidelines

We invite extended abstracts of 2-4 pages (excluding references) in LNCS format, https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines. All submissions must be in English and fall into one of the following categories:

1. New and Emerging Work: Presents novel research in the focus areas of the workshop. Submissions will be evaluated primarily on "novelty".
2. Summary of Recent Results: Presents existing work and highlights its contribution in terms of relevance and impact in the focus areas of the workshop. Submissions will be evaluated primarily on "impact".

Please note that:

• No Formal Proceedings: We welcome submissions of work that has already been presented or submitted elsewhere. No copyright transfer is required; we only request permission to post accepted abstracts on the workshop website.
• Journal Special Issue: Authors of selected accepted abstracts will be invited to submit extended versions for a journal special issue.

All submissions will be reviewed by members of the Program Committee. The paper review process is single-blind, which means that the author identities are not required to be anonymous and are visible to the PC members/reviewers, but reviewer identities are not visible to the authors. No special efforts are required to anonymize content in the paper (such as referencing the authors’ prior work).

Policy on the use of Gen AI (same as NFM)

We understand the convenience afforded by the use of generative AI-based large language models to produce text in the submitted manuscript. However, we strongly encourage the authors to check the generated text for factual errors and inconsistencies. We encourage the authors to adopt appropriate standards for citing products obtained using generative AI (such as text, tables, graphics). Use of AI-based coding assistants is permitted, and we encourage authors to disclose the use of such tools as the community may find this scientifically interesting.

Submission will be via the OpenReview link:

https://openreview.net/group?id=NFM/2026/Workshop/RExAI

To submit a paper on OpenReview, you must first create a profile and log in to the system. Then, navigate to the specific conference or venue’s page on OpenReview, find the “Conference Submission” link, and click on it. Fill out the submission form, which will prompt you for paper details like title, authors, abstract, and keywords, before uploading the PDF of your paper.

Step-by-Step Submission Process

1. Create an Account & Log in
   • If you don’t have one, sign up for an account on OpenReview.
   • Log in using your credentials.
2. Find Your Conference
   • Navigate to the workshop’s page on OpenReview (NFM Workshop RExAI 2026)
3. Locate the Submission Link
   • Select “NFM 2026 Workshop RExAI Submission” to access the submission form.
4. Complete the Submission Form
   • Add Paper Details: Enter the title of your paper and all authors (each must have an OpenReview account).
   • Provide Keywords & Summary: Add relevant keywords and a short abstract.
   • Upload Your PDF: Submit the full PDF version of your paper.
5. Finalize Submission
   • Follow any final instructions (e.g., license agreement, confirmation).
   • Submit and confirm.

Important Considerations

• Author Profiles: All submitting authors must have an active OpenReview profile.
• Email Address: Your profile’s preferred email is used for notifications.
• Editing: You can edit your submission and upload new versions until the submission deadline.

 Chairs

• Anastasia Mavridou, KBR Inc., NASA Ames — anastasia.mavridou@nasa.gov
• Marie Farrell, The University of Manchester — marie.farrell@manchester.ac.uk
• Divya Gopinath, KBR Incd., NASA Ames — divya.gopinath@nasa.gov
• Hazel Taylor, The University of Manchester — hazel.taylor@manchester.ac.uk