Workshops at ICLR 2026

2026-01-13 · Source: ICLR Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, short

Summary

The ICLR 2026 conference has announced 40 accepted workshops, selected from 151 total submissions, with 135 deemed valid. This represents a 1.24x increase in submissions compared to ICLR 2025. The workshops, scheduled for April 26 and 27, 2026, cover a diverse range of topics including Agentic Systems, Alignments, Continual Learning, Generative Models, Memory, and Reasoning. The selection process involved at least three reviews per proposal, with chairs ensuring fairness, adherence to criteria like COI management and in-person planning, and promoting diversity across topics, speakers, and organizers. A total of 417 reviews were submitted by 74 reviewers, leading to 16 desk rejections and 95 reviewed rejections.

Key takeaway

For research scientists considering submitting workshop proposals to major conferences, you should meticulously review the specific guidance criteria, including COI management and LLM usage policies. Focus on developing unique topics and diverse organizing teams to increase your chances of acceptance, as chairs actively seek to avoid topic and organizer recurrence. Plan for in-person participation and support contributed work tracks.

Key insights

ICLR 2026 accepted 40 diverse workshops from 135 valid submissions, reflecting significant growth and rigorous review.

Principles

• Ensure diversity in topics and organizers.
• Prioritize in-person planning for workshops.
• Adhere to LLM usage policies.

Method

Proposals underwent at least three reviews, checked by chairs for fairness and adherence to hard criteria, then evaluated for diversity and soft criteria before final decisions.

In practice

• Review workshop guidance criteria carefully.
• Manage Conflicts of Interest (COI).
• Support Tiny Paper track submissions.

Topics

• Agentic Systems
• Generative AI
• Large Language Models
• AI for Science
• AI Safety & Trustworthiness

Best for: Research Scientist, AI Researcher, AI Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by ICLR Blog.