Research Papers
5–9 pages (excluding unlimited references and appendices); should present original research contributions such as novel methods, systems, benchmarks, theoretical insights, or empirical studies.
Machine Learning/CORE Unranked
NeurIPS-CLEA 2026
NeurIPS 2026 Workshop on Continual Learning for Enterprise AI Agents
The NeurIPS-CLEA 2026 workshop focuses on continual learning for enterprise AI agents, bringing together researchers and practitioners to address challenges in building adaptive AI systems that evolve in dynamic environments. It emphasizes methodologies for lifelong learning, agent orchestration, memory systems, governance, and real-world deployments in domains like IT operations, customer support, and financial services.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Opinion Papers
Up to 5 pages (excluding unlimited references); include position papers, vision papers, demonstrations, or experience reports that articulate new research directions, identify open challenges, or discuss opportunities; extensive experimental evaluation is not required.
Research areas in scope
01
Model Adaptation and Learning
Catastrophic forgetting detection and mitigationCompute- and cost-efficiencyTask-agnostic adaptation in non-stationary environmentsPersonalized and role-aware continual adaptation
02
Agent Orchestration and Workflow Evolution
Lifelong tool-use and workflow adaptationHarness-level continual improvementMulti-agent and human-agent ecosystems
03
Memory and Organizational Knowledge
Memory and enterprise knowledge evolutionFeedback-driven learning from enterprise signals
04
Systems, Governance, and Deployment
Compliance, safety, and alignment driftEvaluation and harness engineering for enterprise agentsSystem-level continual learning and deployment
05
Applications and Case Studies
Real-world continual learning deployments such as IT operationsReal-world continual learning deployments such as customer supportReal-world continual learning deployments such as compliance monitoringReal-world continual learning deployments such as financial servicesReal-world continual learning deployments such as asset management
Policies worth checking twice
- Submissions undergo a double-blind peer review process
- Submissions must follow NeurIPS 2026 formatting guidelines
- Papers based on previously published work or work under review are welcome, provided clearly identified
- Submissions describing preliminary or ongoing research will not preclude subsequent publication in conferences or journals
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026