Decoupled Multi-Agent Orchestration
cs.AI
Submitted: 2026-10-06
Updated: 2026-10-07
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- COLLATOR: Compositional Multi-Agent Orchestration with Counterfactual Reinforcement Learning
- The Era of Agentic Organization: Learning to Organize with Language Models
- Uno-Orchestra: Parsimonious Agent Routing via Selective Delegation
- Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
- Prompt-to-Leaderboard
- FlowReasoner: Reinforcing Query-Level Meta-Agents
- Agent Q-Mix: Selecting the Right Action for LLM Multi-Agent Systems through Reinforcement Learning
- MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision
- MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks
- Towards a Science of Scaling Agent Systems
- Multi-Agent Coordination Adaptation via Structure-Guided Orchestration
- Experience as a Compass: Multi-agent RAG with Evolving Orchestration and Agent Prompts
- LLM Bandit: Cost-Efficient LLM Generation via Preference-Conditioned Dynamic Routing
- Rethinking Predictive Modeling for LLM Routing: When Simple kNN Beats Complex Learned Routers
- Asynchronous Credit Assignment for Multi-Agent Reinforcement Learning
- Unified-MAS: Universally Generating Domain-Specific Nodes for Empowering Automatic Multi-Agent Systems
- MAS-on-the-Fly: In-Context Structural Adaptation of LLM-Based Multi-Agent Systems
- A Survey of Temporal Credit Assignment in Deep Reinforcement Learning
- AOrchestra: Automating Sub-Agent Creation for Agentic Orchestration
- ORI: O Routing Intelligence
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- Agents' Last Exam
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems