Call for Papers | ICMLAA 2027

Call for Papers

Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas but are not limited to:

Pre-training mechanisms and unsupervised learning of foundation models
Supervised fine-tuning and parameter-efficient tuning (PEFT)
Large model alignment, constraint learning and value optimization
Model distillation, pruning and sparse learning for foundation models
Transfer learning and domain adaptation of pre-trained models
Continual learning and incremental optimization for large models
Hyperparameter optimization and model generalization enhancement
Efficient training algorithms for low-resource foundation models

LLM-based agent architecture design and iterative upgrading
Autonomous tool calling and multi-function invocation learning
Agent memory mechanism, experience accumulation and retrieval learning
Task decomposition, automatic planning and autonomous execution
Long-horizon autonomous decision learning for intelligent agents
Dynamic goal adjustment and self-correction learning of agents
Generative agent behavior simulation and autonomous iteration
General-purpose agent construction based on foundation models

Multi-agent cooperative learning and group decision optimization
Multi-agent game learning, adversarial and collaborative equilibrium
Heterogeneous multi-agent fusion learning and task allocation
Decentralized and distributed multi-agent learning algorithms
Communication-aware multi-agent interactive learning
Scalable learning for large-scale multi-agent systems
Multi-agent meta-learning and fast adaptive collaboration
Conflict detection and coordination learning of multi-agents

Deep reinforcement learning for sequential autonomous decision-making
Model-based and model-free reinforcement learning optimization
Hierarchical reinforcement learning for complex task execution
Exploration-exploitation balance learning in autonomous systems
Reward function design and self-rewarding learning
Policy gradient optimization and robust policy learning
Imitation learning and inverse reinforcement learning for agents
Real-time reinforcement learning for dynamic autonomous scenarios

Embodied perception learning and environment sensing modeling
Robot motion control learning and autonomous manipulation
Vision-based embodied intelligent decision and interaction learning
Mobile robot autonomous navigation and path planning learning
Manipulator operation learning and precise autonomous control
Dynamic environment adaptive embodied learning
Human-robot interaction learning and collaborative operation
Embodied agent generalization learning in open environments

Few-shot learning for intelligent agent task adaptation
Zero-shot generalization learning of autonomous agents
Meta-learning and fast adaptive learning for new agent tasks
Data-efficient learning for low-resource agent scenarios
Cross-domain transfer learning for heterogeneous agent tasks
Weakly supervised learning for agent decision optimization
Semi-supervised learning for autonomous system iteration
Generalizable feature learning for unseen agent tasks

Neural world model construction and environment simulation learning
Future state prediction learning for dynamic environments
Physical consistency learning of simulated world models
Real-world environment reconstruction and predictive modeling
World model guided agent planning and decision learning
Temporal sequence learning and dynamic trend prediction
Multi-scale environment modeling and scenario reasoning learning
Simulation-to-real transfer learning for world model deployment

Cross-modal fusion learning of vision, text and sensor data
Multimodal feature alignment and unified representation learning
Multimodal prompt learning for autonomous agent understanding
Audio-visual fusion learning for environmental perception agents
Multimodal intent recognition and interactive learning
Cross-modal retrieval and matching learning for agent tasks
Multimodal generation learning for agent behavior output
Robust multimodal learning under noisy environment

Model lightweight compression and low-latency learning
Edge-side adaptive inference and incremental learning
Terminal autonomous learning with limited computing resources
Edge-cloud collaborative machine learning for agents
Low-power learning algorithms for embedded intelligent agents
Model quantization and binarization optimization learning
Real-time learning iteration for edge deployment scenarios
Resource-aware dynamic learning scheduling for agents

Robust learning against adversarial attacks on agent systems
Explainable machine learning for agent decision interpretability
Causal learning and causal reasoning for reliable agents
Privacy-preserving learning and federated learning for agents
Uncertainty estimation and risk-aware learning
Fault tolerance learning and abnormal behavior detection
Safety constraint learning for autonomous agent execution
Ethical and normative learning for intelligent agent behavior

Logical reasoning learning and deductive reasoning optimization
Inductive and abductive reasoning for complex agent tasks
Chain-of-thought learning and step-by-step reasoning iteration
Autonomous task planning and hierarchical logic learning
Knowledge graph enhanced agent reasoning learning
Long-term logical consistency learning for agent decisions
Reasoning error correction and self-verification learning
Cross-task reasoning generalization and migration learning

Machine learning for industrial autonomous robot control
Intelligent manufacturing agent learning and autonomous scheduling
Autonomous detection and fault diagnosis learning for industry
Smart logistics and warehouse autonomous agent applications
Industrial digital twin agent learning and interactive iteration
Autonomous inspection agent learning for industrial equipment
Smart city and traffic autonomous decision agent systems
Practical optimization and engineering deployment of industrial ML agents