CONCAT: Consensus- and Confidence-Driven Ad Hoc Teaming for Efficient LLM-Based Multi-Agent Systems
cs.MA, cs.CL
Submitted: 2026-05-28
Updated: 2026-09-22
Comments: We identified a potential issue in the repeated-run evaluation of our method that may have caused unintended prompt overlap across runs and affected the reported results. We therefore withdraw the manuscript for further investigation and re-evaluation
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Evaluating Large Language Models Trained on Code
- Training Verifiers to Solve Math Word Problems
- Multi-Agent Collaboration via Evolving Orchestration
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
- The Llama 3 Herd of Models
- Large Language Model based Multi-Agents: A Survey of Progress and Challenges
- OpenAI o1 System Card
- WebThinker: Empowering Large Reasoning Models with Deep Research Capability
- GAIA: a benchmark for General AI Assistants
- Qwen2 Technical Report
- Optimizing Sequential Multi-Step Tasks with Parallel LLM Agents
- AgentOrchestra: Orchestrating Multi-Agent Intelligence with the Tool-Environment-Agent(TEA) Protocol
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