Main Research Track
6-8 pages (excluding references and appendices) Full papers presenting novel methods, theoretical analyses, or comprehensive empirical results related to the workshop topics.
Machine Learning/CORE Unranked
MALGAI 2026
Workshop on Multi-Agent Learning and Its Opportunities in the Era of Generative AI
Apr 26 2026Official workshop site Site reachable
The ICLR 2026 Workshop on Multi-Agent Learning and Generative AI (MALGAI 2026) explores the convergence of multi-agent learning and generative AI, focusing on LLM-based multi-agent systems, real-world distributed control, and human-AI interaction. It brings together researchers from machine learning, game theory, and human-computer interaction to advance theoretical and practical frameworks for next-generation multi-agent generative AI systems.
Key deadlines
Verified September 9, 2026Full paper
February 11, 2026
AoE
Workshop timeline
Submission and decisions
Full paperKey deadline
February 11, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Blueprint Track
2-4 pages (excluding references and appendices) Visionary, exploratory, or critical perspectives, including conceptual frameworks, preliminary research, new perspectives, or tools/benchmarks.
Research areas in scope
01
Multi-Agent Learning Paradigms for LLMs
Cooperative multi-agent reinforcement learning for improving coordination between modules within the multi-agent systemLLMs (LLM orchestration)Adversarial training for improving the generalizability of the single LLM trainingOpen multi-agent reinforcement learning/ad hoc teamwork for a multi-agent system LLMs to deal with some unknown and situational function/data providersFormalism of full/partial information required in modelling multi-agent system LLMs and the minimal information each agent needsStrengths/weaknesses of natural language as both the action space and the observation space in the multi-agent system LLMsCriteria for evaluating a "well structured" multi-agent system LLMs in completing a task (e.g., game-theoretic approaches and models)Coordination mechanisms for improving performance of multi-agent system LLMs, which can be either predefined or learned from dataStructures (e.g., chains, graphs, etc.) to represent a multi-agent system for LLMsApplication of coordination graphs (e.g., DAGs, factor graphs, etc.) on decomposing reward functions for training multi-agent system LLMs
02
Generative AI for Multi-Agent Learning
World models for improving the data quality for multi-agent learningReward models for improving multi-agent learning with sparse rewardsGenerative AI to generate a diverse set of agent modelsGenerative models (e.g., diffusion models) for improving multi-agent learningGraph-based generative AI for improving graph-structured multi-agent learning and emergent communication between agentsMulti-agent systems for the modular generative models
03
Multi-Agent Exploration for Generative AI
Multi-agent exploration for coordinating modules in the modular generative AI modelsThe role of entropy of agent policies (generators) in the modular generative AI learning
04
Environments for Testing and Developing Multi-Agent Learning
Environments of real-world decentralised or distributed control problemsComputationally efficient environments for generative AI-based multi-agent learningLight environments (without LLMs) for simulating the human-AI interaction processJAX environments for accelerating multi-agent simulation processes
05
Human-AI Interaction
Learning paradigms for improving the capability of AI agents to adapt to human instructions or proactively guide humansCapable and interpretable (explainable) human models trained by generative AIAppropriate medium of conveying human instructions to AI agents (e.g., natural language, formal methods and learning embeddings)Approaches for estimating human intentions enabling AI agents to make better decision
Policies worth checking twice
- Submissions are required to use the provided workshop LaTeX template.
- Double-blind review policy applies.
- Submissions must be managed through OpenReview.
- AI-generated papers are not permitted for normal or tiny paper submissions.
- All AI contributions must remain under human oversight and validation.
- The role of AI (if any) in the preparation of submissions must be transparently acknowledged.
- We will follow the official ICLR 2026 Policies on Large Language Model Usage.
- We will strictly adhere to the ICLR policy on Conflicts of Interest (COI).
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026