Full papers
Up to 8 pages, excluding references and appendices.
Machine Learning/CORE Unranked
OE-Agent 2026
ACML2026: Open and Efficient Agentic AI for Resource-Constrained Environments
The OE-Agent 2026 workshop, held in conjunction with ACML 2026, focuses on advancing open and efficient agentic AI systems that operate under resource constraints, moving beyond reliance on frontier-scale proprietary models. It brings together researchers to explore algorithmic and system-level innovations in open-weight models, memory, retrieval, tool use, and multi-agent collaboration for accessible, privacy-preserving, and sustainable AI deployment.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Short papers
Up to 4 pages, reporting preliminary results, emerging ideas, or position statements.
Research areas in scope
01
Topics of Interest
Open-weight foundation models and small language models for agentic AIEfficient reasoning, planning, and test-time scaling for agentsMemory, retrieval, tool use, and multi-agent collaborationResource-constrained development and deployment, privacy-preserving AI, and edge agentsBenchmarking and evaluation of open and efficient agentic systemsApplications of open and efficient agents in various domains such as science, industry, and society
Policies worth checking twice
- Submissions must be anonymized
- Submissions must be formatted using the official ACML 2026 LaTeX template and style files
- Each submission will receive at least two reviews
- Reviews assess relevance, originality, technical quality, clarity, reproducibility, and potential impact
- Particular attention is given to work that advances open and efficient agentic AI under realistic computational, deployment, or resource constraints
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026