COLLATOR: Compositional Multi-Agent Orchestration with Counterfactual Reinforcement Learning
cs.AI
Submitted: 2026-05-14
Updated: 2026-09-29
Terminology
Sources
- AgentBalance: Backbone-then-Topology Design for Cost-Effective Multi-Agent Systems under Budget Constraints
- Evaluating Large Language Models Trained on Code
- Training Verifiers to Solve Math Word Problems
- WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models
- FlowReasoner: Reinforcing Query-Level Meta-Agents
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- Large Language Model based Multi-Agents: A Survey of Progress and Challenges
- Measuring Massive Multitask Language Understanding
- Automated Design of Agentic Systems
- Learning to Orchestrate Agents in Natural Language with the Conductor
- ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration
- Self-Consistency Improves Chain of Thought Reasoning in Language Models
- ReAct: Synergizing Reasoning and Acting in Language Models
- HieraMAS: Optimizing Intra-Node LLM Mixtures and Inter-Node Topology for Multi-Agent Systems
- Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems
- G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks
- Multi-agent Architecture Search via Agentic Supernet
- AFlow: Automating Agentic Workflow Generation
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