DAI 2026 (International Conference on Distributed Artificial Intelligence) is a CCF C conference held in Hong Kong, China on 2026-11-29. The paper submission deadline is 2026-07-27. Acceptance notifications are sent on 2026-09-16.
Scope and Topics of Interest
Topics of interest include, but are not limited to, the following areas. Authors will be asked to select one or more relevant areas during submission.
Agent Engineering & Infrastructure
Agent frameworks, harnesses, and operating systems: LangGraph, AutoGen, CrewAI, Smolagents, OpenAI Agents SDK
Memory architectures: long-term, short-term, episodic, factual, and experiential memory
Skill acquisition and atomic skills
Tool use: tool selection, grounding, and reliability
Context engineering: context windows, compression, and selective retrieval
Agent protocols: MCP, A2A, and interoperability standards
AgentOps: observability, debugging, evaluation, and failure recovery
Agent identity, reputation, and provenance
Foundations of Agent Learning
Reinforcement learning, multi-agent reinforcement learning, and cooperative or competitive learning
Post-training for agents
Self-play, curriculum, and open-ended learning
Continual learning, meta-learning, and transfer
Reward design and credit assignment
Distributed, privacy-preserving, and collaborative learning
Scaling laws and empirical theory of agent learning
Self-Evolving & Self-Improving Agents
Self-improvement and recursive self-modification
Meta-reasoning and self-reflection
Experience distillation: from trajectories to transferable knowledge
Co-evolution of policies and critics
Multi-agent evolutionary systems
Gödel-style self-rewriting agents
Benchmarks and evaluation for self-evolving systems
Multi-Agent Cooperation & Human-Agent Interaction
Cooperative multi-agent reinforcement learning, credit assignment, and teamwork
Communication, language emergence, and negotiation
LLM-based multi-agent orchestration
Ad-hoc teamwork and zero-shot coordination
Coalition formation and distributed problem solving
Collective intelligence and swarm behavior
Trust, explainability, and accountability
AI agents as digital employees, collaborators, and competitors
Human-agent and human-robot interaction
Agent-based human interaction analysis
Agents for enhancing human cooperation
Game Theory, Economics & Agent Markets
Algorithmic game theory and equilibrium computation
Mechanism and market design, auctions, and social choice
Strategic behavior of LLM agents and algorithmic collusion
Machine-payable APIs and agent-to-agent transactions
Contract theory and principal-agent models for AI
Blockchain economics and decentralized systems
Behavioral game models and bounded rationality
Security games
Embodied Multi-Agent Systems
Multi-robot learning, coordination, and swarms
Vision-Language-Action models for agent teams
World models for multi-agent planning
Sim-to-real transfer in multi-agent settings
Heterogeneous embodied teams
Safety layers and hardware-software co-design for physical agents
Science of AI & AI for Science
Science of AI
Evaluation, benchmarking, and reproducibility of agent systems
Interpretability of multi-agent LLM systems
Emergent behavior, scaling laws, and phase transitions
Failure modes, red-teaming, and safety evaluation
Theoretical foundations of agentic AI
AI for Science
AI agents for scientific discovery
AI agents in mathematics, physics, chemistry, biology, and materials
Automated experiment design and execution
Scientific literature understanding and hypothesis generation
Human-agent collaborative research
Agent-based simulation of societies
Policy, governance, and alignment of agent collectives
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