arXiv:2606.18837v1 Announce Type: cross Abstract: Large Language Model (LLM)-based automatic Multi-Agent Systems (MAS) generation has become a crucial frontier for tackling complex tasks. However, existing methods face a dilemma between model capability and experience retention. Inference-time MAS leverages frozen frontier LLMs but repeats identical searches without learning from past experience. Conversely, Training-time MAS internalizes experience via gradient updates but is constrained by the low capability ceiling of smaller models, and is hard to scale to large frontier LLMs. To bridge th
The rapid advancement of Large Language Models has made LLM-based Multi-Agent Systems a crucial frontier, but current approaches struggle with balancing model capability and experience retention.
This research addresses a fundamental limitation in AI agent development, potentially enabling more capable and efficient autonomous systems that learn from past interactions.
Existing trade-offs between using high-capability models versus models that retain experience could be overcome, accelerating the practical application of advanced multi-agent AI.
- · AI software developers
- · Companies implementing AI agents
- · Research institutions in AI
- · Companies relying on repetitive, unoptimized AI agent deployments
- · Manual orchestration of complex tasks
More robust and adaptive AI agents become feasible for production environments.
Reduced human oversight requirements for complex automated processes will emerge as agents learn and improve autonomously.
The development of highly sophisticated autonomous AI systems could accelerate, leading to broader economic and societal restructuring.
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Read at arXiv cs.AI