Continual learning is becoming a critical capability for enterprise AI agents operating in evolving environments. This workshop brings together researchers and practitioners working on continual learning, agent systems, reinforcement learning, and enterprise AI deployment to discuss methodologies, benchmarks, evaluation protocols, and real-world challenges in building adaptive AI agents.
Topics of Interest
Model Adaptation and Learning
Catastrophic forgetting detection and mitigation, Compute- and cost-efficiency, Task-agnostic adaptation in non-stationary environments, Personalized and role-aware continual adaptation
Agent Orchestration and Workflow Evolution
Lifelong tool-use and workflow adaptation, Harness-level continual improvement, Multi-agent and human-agent ecosystems
Memory and Organizational Knowledge
Memory and enterprise knowledge evolution, Feedback-driven learning from enterprise signals
Systems, Governance, and Deployment
Compliance, safety, and alignment drift, Evaluation and harness engineering for enterprise agents, System-level continual learning and deployment
Applications and Case Studies
Real-world continual learning deployments such as IT operations, customer support, compliance monitoring, financial services, and asset management
Call for Papers
Important Dates
- Submission Deadline:
Sep 4, 2026 AOE - Notification Date:
Sep 29, 2026 AOE - Camera-Ready Deadline:
Nov 15, 2026 AOE - Workshop:
Dec 13, 2026
Submission Tracks
Research Papers
5β9 pages (excluding unlimited references and appendices)
Research papers should present original research contributions related to the workshop topics. Submissions should describe novel methods, systems, benchmarks, theoretical insights, or empirical studies.
Opinion Papers
Up to 5 pages (excluding unlimited references)
Opinion papers include position papers, vision papers, demonstrations, or experience reports that articulate new research directions, identify open challenges, or discuss opportunities in continual learning for enterprise AI agents. Extensive experimental evaluation is not required.
We also welcome papers based on previously published work or work currently under review, provided the submission is clearly identified as such. Such papers are encouraged when they help stimulate discussion within the workshop. Submissions describing preliminary work or ongoing research will not preclude subsequent publication in conferences or journals.
Submission and Review
- Submit your paper through OpenReview.
- Please follow the NeurIPS 2026 formatting guidelines.
- All submissions will undergo a double-blind peer review process.
Presentation
All accepted papers will be presented during the workshop poster session. A small number of outstanding papers will also be selected for spotlight oral presentations.
Program Schedule
December 13, 2026
Atlanta, Georgia, USA
Room C206
Full day, in person
| Time (EST) | Event |
|---|---|
| 8:20β8:30 | Opening Remarks |
| 8:30β9:30 | Oral Paper Presentation I β Knowledge and Memory Evolution
How do agents acquire and retain new knowledge?
|
| 9:30β10:30 | Keynote 1 β Bing Liu Professor of Computer Science, University of Illinois at Chicago |
| 10:30β10:45 | Coffee Break 1 |
| 10:45β11:45 | Keynote 2 β Rama Akkiraju Vice President of AI for IT, NVIDIA |
| 11:45β12:30 | Lunch Break |
| 12:30β13:30 | Keynote 3 β Azalia Mirhoseini Assistant Professor of Computer Science, Stanford University |
| 13:30β14:30 | Panel: Agenda in Continual Learning Conceptual Frameworks, Benchmarks, Open Source, Data Sharing, Security & Governance |
| 14:30β14:45 | Coffee Break 2 |
| 14:45β15:45 | Oral Presentation II β Reliable and Responsible Adaptation
How do we ensure that continual changes are safe, reliable, and beneficial?
|
| 15:45β16:00 | Closing Remarks |
| 16:00β17:00 | Poster Session |
Accepted Papers
-
Designer-RSI: Evolving Procedural Memory in the Wild for Agentic Graphic Design [Oral]
-
CONTRAMEM: Learning Self-Evolving Procedural Memory from Contrasting Multi-Model Trajectories [Oral]
-
Passing the Accuracy SLA While Failing the Fairness One: Disparity Drift and Its Cost in Continually Updated Classifiers [Oral]
-
DKL: Decoupled Knowledge Learning for Instruction-Tuned Language Models [Oral]
-
Conservative-First Release Mitigates Verifier Overfitting in Generated Agent Workflows [Oral]
-
Towards continual in-context learning via training Cartridges at test time [Oral]
-
When Should a Working Agent Change? Reversible Workflow Replacement for Continual Agent-System Adaptation [Poster]
-
Position: The MLOps-to-LLMOps Transition Demands a New Operational Paradigm [Poster]
-
Memory as the Organization of Knowledge: Four Neural Principles for Adaptive Agents [Poster]
-
HyperSkill: Self-Evolving LLM Agents via Hypergraph-Structured Skill Memory [Poster]
-
Measurable by Construction: Contradiction Density and the Evaluation of Belief-Forming Memory [Poster]
-
Assurance Has a Half-Life: Why Continual Learning Breaks Point-in-Time Governance for Enterprise AI Agents [Poster]
-
AURA: Amortized, User-Conditioned Reranking Adapters for Personalized LLM Agents [Poster]
-
EconSkills: Studying Skill Transfer and Retrieval for Web Agents on Live Economic Data [Poster]
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Change Passports: A Machine-Readable Standard for Auditable Artifact Promotion in Continually-Learning Agents [Poster]
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UniSD: Towards A Unified Self-Distillation Framework for Large Language Models [Poster]
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VIGIL: Verifier-Informed Gated Improvement Loop for Spreadsheet Question Answering [Poster]
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Deliberation Memory: Preserving How Humans and Heterogeneous Agents Reach, Contest, and Revise Decisions [Poster]
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Privileged Context as Drift in On-Policy Self-Distillation [Poster]
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ASTRA - Agentic System for Ticket Resolution and Analysis [Poster]
-
EchoPath: Execution-Level Replayable Memory for GUI Agents [Poster]
-
RECAST: dynamic REplay for Counterfactual Agentic Simulation from static Trajectories [Poster]
-
REM: What to Store, When to Recall, What to Forget in Experience Memory for Coding Agents [Poster]
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Continual Evaluation for Enterprise Agent Governance: Architecture and Evidence from a Policy Layer [Poster]
-
Continuous Process-Level Evaluation for Evolving Enterprise AI Agent Skills [Poster]
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Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails [Poster]
-
PRISM: Prompt-Refined In-Context System Modeling for Financial Retrieval [Poster]
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Continual Learning for Enterprise Agents is a Change-Control Problem [Poster]
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Training-Free Continual Learning for Multimodal Forecasting via Prompt Evolution [Poster]
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Adaptive Auto-Harness: Sustained Self-Improvement on Open-Ended Task Streams [Poster]
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ESCROW: Guarded and Dual-Objective Continual Maintenance for Agents in Policy-Governed Enterprise Workflows [Poster]
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Benchmarking Continual Learning in Orchestration Agents [Poster]
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Sensitive but Not Specific: Log-Side Screening for Reward Hacking in Continually Updated Agents [Poster]
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Continual Enterprise World Model Discovery in Dynamic Systems [Poster]
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When the Cross-Silo Federation Goes Offline: Continual Learning for Site Onboarding with Limited Unlabeled Data [Poster]
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Beyond Leaderboards: Continual, Task-Specific LLM Evaluation for Enterprise Finance [Poster]
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A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering [Poster]
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Agentic Context Learning with Self-Discovered Specification [Poster]
-
When Accumulated Memory Starts to Hurt: Forgetting and Cost in Non-Parametric Continual Adaptation [Poster]
-
Scaling Proactive Compute: When the Agent Anticipates the Task [Poster]
Keynote Speakers
Organizers
Reviewers
We gratefully acknowledge the reviewers who support the CLEA workshop review process.
Arpita Agrawal
Lead Machine Learning Engineer
Adobe
Naman Ahuja
Software Engineer
Meta
Naresh Alapati
Principal Software Engineer
Walmart
Gaurav Kumar Ambasta
Senior Software Engineer
Amazon
Abdulrasaq Amolegbe
CEO & Co-founder
AgentStatus
Sowmyanka Andalam
Engineering Lead
Intuit
Vladimir Beskorovainyi
Chief Technology Officer
Artwell
Deepali Bhanushali
Senior Software Engineer
Twilio
Manjot Bilkhu
Machine Learning Engineer
Apple
Bala Vignesh Charllo
Member of Technical Staff
OpenAI
Wenjun Chen
Senior Software Engineer
Meta
Huang Cheng
Staff Software Engineer
Meta
Aditya Sunil Choksi
AI Engineer
Tata Consultancy Services
Martin Ciesielski-Listwan
Founder
Stealth Startup
Ashish Dsa
Chief Technology Officer
Arbor
Prem Kumar Gadhanki
Senior Software Engineering Manager
Capital One
Sajal Garg
Principal Software Engineer
Microsoft
Ishita Gupta
Software AI/ML Engineer
Alai Studios
Domonkos Haffner
AI Architect
DiffuseDrive
Mahdi Hemmati
Principal AI Engineer
Huawei
Varad Ingale
MS student
University of Massachusetts Amherst
Haris Jalal
Software Engineer
Oracle
Surbhi Jha
Senior Software Engineer
NVIDIA
Aastha Jhunjhunwala
Senior Solutions Architect
NVIDIA
Alyssia Jovellanos
Researcher
Stealth Startup
Rapael Kalandadze
CTO & Co-Founder
Wandero
Christopher Kevin
Principal AI Engineer
NVIDIA
Fuzail Khan
Senior Software Engineer
Meta
Harshita Khandelwal
AI/ML Software Development Engineer
Amazon
Prateek Kohli
Principal Data Scientist
Walmart
Venkata Sai Girish Konda
Principal Software Engineer
Microsoft
Yi Sien Ku
Founding Engineer
ArchiBoost AI
Rohan Kulkarni
Senior Software Engineer
Meta
Aisvarya Sampath Kumar
Application Support Analyst
Mercuria Energy America
Prasoon Kumar
AI Engineer
Gilead Sciences
Bala Sai Kunapareddi
Lead AI/ML Engineer
Optum
Pranav Ladkat
Senior Research Engineer
Amazon
Chang Hun Lee
Software Engineer
Solomon AI
Artur Lima
Research Assistant
Worcester Polytechnic Institute
Jingze Liu
Senior Data & Applied Scientist
Microsoft
Vikas Luthra
Technical Lead
Komatsu North America
Sarthak Mishra
Software Development Engineer
Amazon
Lenin Mookiah
Data Science Consultant
Sogeti
Rushab Munot
Equity Quantitative Researcher
Lord Abbett
Sai Vinod Nagabhairava
Staff Software Engineer
Meta
Nandish Nanjappa
Staff Software Engineer
Attentive Mobile
Vanooshe Nazari
Teaching Assistant
Iran University of Science and Technology
Aarati Andrea Noronha
Senior Machine Learning Engineeer
Agility Robotics
Mayank Pahadia
Staff Software Engineer
Tinder
Kumar Pallav
Member of Technical Staff
Perplexity AI
Amitkumar Pandey
Product Leader
Walmart
German I Parisi
Principal Researcher
IBM
Shivaraman Parthasarathy
Senior Technical Architect
SalesForce
Rishabh Patil
Software Developer
Ashu Phaugat
Principal Software Development Engineer
CVS Health
Anirban Poddar
Product Manager
Meta
Srikanth Prabala
Senior Applied Scientist
Amazon
Phudish Prateepamornkul
AI Scientist
DataX
Mohammad Ramezanali
Principal AI Scientist
SalesForce
Mohammadmojtaba Roshani
AI Engineer
Shiraz University
Swastik Roy
Senior Applied Scientist
Amazon
Sourajit Saha
PhD student
University of Maryland
Rohit Bhalchandra Sahasrabuddhe
Senior Software Development Engineer
Amazon
Nisarg Shah
Staff ML Engineer
Uber
Juharasha Shaik
Director
VISA
Harshit Sharma
Director of AI Transformation
ITS Logistics
Raghav Sharma
Machine Learning Engineer
Workday
Damodar Shetyo
Principal Engineer
Amazon
Japjit Singh
Applied AI lead
Prateek Singh
Vice President & ML Scientist
JPMorganChase
Atin Sood
Research Manager
IBM
Ankit Sultana
Software Engineer
Notion
Karthik Suresh
Staff Machine Learning Engineer
Adobe
FNU Tejinder
Senior Manager
Deloitte Consulting
Ankur Tomar
Senior Applied Scientist
Amazon
JP Vasseur
Senior Distinguished Engineer
NVIDIA
Tushar Vatsa
Machine Learning Engineer
Adobe
Prakasam Venkatachalam
Technology Leader
LPL Financial
Vivek Verma
Machine Learning Scientist
Apple
Siddharth Vohra
Senior AI Research Engineer
Amazon
Shine Wu
Researcher
Halluminate
Jiahe Yan
Software Engineer
Snowflake
Chen Yang
PhD student
Texas A&M University
Xi Yang
Lead Machine Learning Engineer
SalesForce
Osama Yousuf
Principal Engineer
Western Digital
Yao Yu
Senior Machine Learning Engineer
Meta
Yuxuan Zhang
Researcher
Vector Institute
Siming Zhao
Data Scientist
Zihao Zheng
Senior Software Engineer
HubSpot