Full papers
Up to 7 pages (excluding references) using the IJCAI template describing novel work, with additional pages allowed in the appendix.
AI/CORE Unranked
AutoAI-FM 2026
AutoAI Meets Foundation Models
The AutoAI-FM 2026 workshop explores the intersection of Automated Artificial Intelligence (AutoAI) and foundation models, including large language models (LLMs). It aims to foster research on how AutoAI methods can enhance the performance, safety, and efficiency of foundation models, and vice versa, by leveraging in-context learning, meta-algorithmic optimization, and adaptive configurations across domains like machine learning, optimization, and agentic systems.
Key deadlines
Verified September 9, 2026Full paper
May 27, 2026
AoE
Conference timeline
Submission and decisions
Full paperKey deadline
May 27, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Hot-off-the-press
Describing previously published, peer-reviewed work that appeared on or after 1 January 2025; authors must submit the PDF of their published work and an additional paragraph explaining its relevance to the workshop.
Poster only presentations
For recently accepted papers or ongoing work; registration requires emailing the title, authors list, and abstract to the workshop contact.
Research areas in scope
01
Themes and Scope
Foundation models for AutoAIAutoAI for foundation modelsNatural language interfaces for AutoAI systemsIn-context learning for AutoAI and meta-algorithmicsZero-shot and few-shot AutoAIBenchmarking and reproducibility of AutoAI methods in the foundation-model eraGreen and resource-efficient AutoAI with foundation modelsSafety, robustness, and reliability of foundation models via AutoAIAutoAI for fine-tuning and adaptationAutoAI for pretraining, post-training, and distillationModel selection, routing, and orchestration using AutoAIAutoAI for agentic and tool-using foundation modelsAutomated scaling analysis and performance extrapolation for foundation modelsInterpretability and explanation of algorithms, models, and performance
Policies worth checking twice
- The review process is single blind.
- Full papers are reviewed by at least three reviewers and evaluated based on technical soundness, readability, and novelty.
- Hot-off-the-press papers are evaluated based on relevance to the workshop and the quality of the original publication venue.
- Submissions are allowed to use LLMs for assistance in writing, but authors are responsible for all content.
- Hallucinated references and facts will lead to desk rejection.
- Papers must use the IJCAI author kit.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026