Oral Presentation
Selected papers presented orally during the workshop sessions, with 7 accepted.
Machine Learning/CORE Unranked
CATS@ICML26 2026
Continual Adaptation at Scale: Towards Sustainable AI Workshop
The CATS@ICML2026 workshop focuses on enabling fast, sustainable continual adaptation of large foundation models to reduce their massive data, compute, and energy costs. It explores how models can learn and adapt continuously through everyday interactions, addressing challenges in efficiency, alignment, multimodality, and deliberate forgetting to move toward more human-like, lifelong learning AI systems.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Poster Presentation
Accepted papers displayed in a poster session, with 42 accepted.
Research areas in scope
01
Key Research Directions
Scale & EfficiencyLifecycle & AlignmentMultimodalityDeliberate Forgetting
02
Call for Papers Topics
Frameworks for Adaptation: Reframing Post-Training and Alignment (SFT/RLHF) as continual adaptation to mitigate vulnerabilities from distribution shifts (e.g., alignment drift), and tailoring strategies to each lifecycle phase.Mechanisms of Adaptation: Analyzing parametric (PEFT), non-parametric (RAG), and semi-parametric (ICL) methods to design modular architectures optimized for low-cost adaptation.Theoretical Foundations of Adaptation: Understanding the fine-tuning optimization landscape and how concepts are stored and modified in high-dimensional space.Evaluation & Benchmarks for Adaptation: Moving beyond Class-Incremental Learning to realistic scenarios (temporal news shifts, evolving code, agent workflows) and establishing metrics for efficiency, alignment retention, and targeted removal.
Policies worth checking twice
- Submissions are subject to a blind review process (implied by standard conference practice and absence of author names in call for papers).
- There is a strict submission deadline of May 5, 2026 (Anywhere on Earth).
- Camera-ready deadline is June 5, 2026.
- Review period is May 6–19, 2026.
- Accepted papers are published as part of the workshop proceedings.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026