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<span style="font-weight: 700;">Call for Papers: RExAI 2026<br>
</span><span style="font-weight: 400;">International Workshop on Formal Requirements Engineering and Artificial Intelligence<br>
May 4, 2026<br>
co-located with NFM 2026 (May 5 - May 7, 2026), Los Angeles, California, USA</span></div>
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</span><span style="font-weight: 700;">Overview</span></div>
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<span style="font-weight: 400;">This workshop explores the </span><span style="font-weight: 700;">intersection of formal requirements engineering and artificial intelligence (AI)</span><span style="font-weight: 400;">, 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.</span></div>
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<span style="font-weight: 400;">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.</span></div>
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<span style="font-weight: 400;">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.</span></div>
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<span style="font-weight: 700;">Areas of interest</span><span style="font-weight: 400;"> include but are not limited to:</span></div>
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How can we formally specify requirements for systems with learning-enabled components?</div>
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How can formal frameworks capture fairness, safety, robustness and explainability requirements for AI systems?</div>
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How do we verify that AI systems meet their specified requirements?</div>
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What role can AI play in automating requirements elicitation, formalization analysis, and validation?</div>
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How do we bridge high-level requirements and behavior of AI-enabled systems to enable traceability, safety assurance, and certification?</div>
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<span style="font-weight: 700;">Important dates</span><span style="font-weight: 400;">:</span></div>
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Submission deadline: March 16, 2026</div>
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Notification: April 3, 2026</div>
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Workshop: May 4, 2026</div>
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<span style="font-weight: 700;">Paper submission guidelines</span></div>
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<span style="font-weight: 400;">We invite extended abstracts of 2-4 pages (excluding references) in LNCS format,
<u><a style="color: rgb(17, 85, 204);" class="OWAAutoLink" id="OWA1cd115ad-d7c4-1956-9cc8-0a5a50f2e81d" href="https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines">https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines</a></u>.
All submissions must be in English and fall into one of the following categories:</span></div>
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<span style="font-weight: 700;">New and Emerging Work:</span> Presents novel research in the focus areas of the workshop. Submissions will be evaluated primarily on "novelty".</div>
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<span style="font-weight: 700;">Summary of Recent Results</span>: 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".</div>
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<span style="font-weight: 400;">Please note that:</span></div>
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<span style="font-weight: 700;">No Formal Proceedings:</span> 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.</div>
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<span style="font-weight: 700;">Journal Special Issue:</span> Authors of selected accepted abstracts will be invited to submit extended versions for a journal special issue.</div>
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<span style="font-weight: 400;">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).</span></div>
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<span style="font-weight: 700;">Policy on the use of Gen AI (same as NFM)</span></div>
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<span style="font-weight: 400;">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.</span></div>
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<span style="font-weight: 700;">Submission will be via the OpenReview link:</span></div>
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<span style="font-weight: 700;"><u><a style="color: rgb(17, 85, 204);" class="OWAAutoLink" id="OWAa27767d0-46f8-a6b9-71bb-9cecaee903ca" href="https://openreview.net/group?id=NFM/2026/Workshop/RExAI">https://openreview.net/group?id=NFM/2026/Workshop/RExAI</a></u></span></div>
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<span style="font-weight: 400;">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 venues 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.</span></div>
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<b>Step-by-Step Submission Process</b></div>
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Create an Account & Log in</div>
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If you dont have one, sign up for an account on <a style="color: rgb(0, 0, 0);" class="OWAAutoLink" id="OWAab1a3e1a-7569-d06d-3fdd-3c9c090607ad" href="https://openreview.net/">
OpenReview</a>.</div>
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Log in using your credentials.</div>
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Find Your Conference</div>
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Navigate to the workshops page on OpenReview (NFM Workshop RExAI<a style="color: rgb(0, 0, 0);" class="OWAAutoLink" id="OWA1cb6085b-44a8-dbc6-e303-fd3fa1386691" href="https://openreview.net/group?id=NFM/2026"> 2026</a>)</div>
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Locate the Submission Link</div>
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Select NFM 2026 Workshop RExAI Submission to access the submission form.</div>
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Complete the Submission Form</div>
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Add Paper Details: Enter the title of your paper and all authors (each must have an OpenReview account).</div>
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Provide Keywords & Summary: Add relevant keywords and a short abstract.</div>
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Upload Your PDF: Submit the full PDF version of your paper.</div>
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Finalize Submission</div>
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Follow any final instructions (e.g., license agreement, confirmation).</div>
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Submit and confirm.</div>
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<span style="font-weight: 700;">Important Considerations</span></div>
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Author Profiles: All submitting authors must have an active OpenReview profile.</div>
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Email Address: Your profiles preferred email is used for notifications.</div>
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Editing: You can edit your submission and upload new versions until the submission deadline.</div>
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<span style="font-weight: 400;"> </span><span style="font-weight: 700;">Chairs</span></div>
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Anastasia Mavridou, KBR Inc., NASA Ames <u><a style="color: rgb(17, 85, 204);" class="OWAAutoLink" id="OWA7657aae3-d424-33d6-b560-45d5641822b0" href="mailto:anastasia.mavridou@nasa.gov">anastasia.mavridou@nasa.gov</a></u></div>
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Marie Farrell, The University of Manchester marie.farrell@manchester.ac.uk</div>
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Divya Gopinath, KBR Inc., NASA Ames divya.gopinath@nasa.gov</div>
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<span style="font-weight: normal;">Hazel Taylor, The University of Manchester hazel.taylor@manchester.ac.uk</span></div>
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