Three Roles, One Model: Role Orchestration at Inference Time to Close the Performance Gap Between Small and Large Agents

Researchers have developed an inference-time scaffolding method that enhances small language models' performance on complex tasks without additional training resources. By deploying Qwen3-8B in three distinct roles—summarization, reasoning, and correction—the model's task completion rate nearly doubles compared to its raw form, making it competitive with larger models. This approach highlights the potential for structured interventions to bridge the performance gap between small and large language models.

Read the full article at arXiv cs.AI (Artificial Intelligence)

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