AGCS Call for Papers [July 30, 2026] (Oct 27-30, Paris, France)
LEBLANC Antoine <[email protected]> Thu, 9 Jul 2026 09:26:14 +0000
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Dear Colleagues,
The International Conference on Agentic, Generative, and Cognitive AI Systems (AGCS 2026)
https://agcs-conference.tech/
📍27–30 October 2026 | Paris, France | Hybrid
Join us for AGCS 2026, a leading international forum bringing together researchers and practitioners to advance agentic intelligence, generative models, and cognitive AI systems.
Technically Co-Sponsored by IEEE France Section, IEEE Computer Chapter, IEEE Computational Intelligence Chapter
🗓 Submission Deadline: 30 July 2026
👉 SUBMIT YOUR PAPER NOW: https://conferences.sparcly.ai/AGCS2026
Hosted by 4 leading institutes in Paris:
ESIEA, Sorbonne University, CY Cergy Paris Université, and Université Paris 8
The Conference Tracks:
Track 1: Agentic AI Systems
Track 2: Generative AI and Foundation Models
Track 3: Cognitive AI and Neural-Symbolic Systems
Track 4: AI Ethics, Safety, and Alignment
Track 5: AI Applications and Impact
Important Dates
Full Paper Submission Date: July 30, 2026,
Notification to Authors: September 10, 2026
Camera Ready Submission: September 30, 2026
Why Paris?
Paris is a global hub for science, technology, and culture, making it the perfect backdrop for AGCS 2026. With its rich history, world-class institutions, and vibrant AI ecosystem, the city provides an inspiring setting for intellectual exchange and networking. We look forward to welcoming you to the City of Light for a week of learning, discovery, and collaboration.
WHY SUBMIT TO AGCS 2026?
🎯 Focused Venue — The first and leading conferences dedicated entirely to Agentic, Generative, and Cognitive AI Systems
🤝 Academia + Industry — Strong collaboration with leading companies
🏆 Awards & Support — Best paper awards + GPU credits (FLOWER and NVIDIA)
📈 Extended papers will be invited to Q1 Journals
🌍 High Visibility — IEEE-indexed proceedings
Best Regards,
Antoine Leblanc
PhD Student – Université Paris 1 Panthéon-Sorbonne
Laboratory Learning Data & Robotics (LDR), ESIEA
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