Research

Enhancing LLM Problem Solving via Tutor-Student Multi-Agent Interaction

Tutor-student multi-agent dialogue improves LLM problem-solving by enabling structured peer collaboration between agents without retraining.

Monday, April 13, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.AIBY sys://pipeline

Researchers propose a tutor-student multi-agent interaction framework to enhance LLM problem-solving. The approach structures task completion as collaborative dialogue between two agents—one acting as tutor, one as student—to improve reasoning and performance.

Read original at arXiv CS.AI

Two-dimensional early exit optimisation of LLM inference

Two-dimensional early-exit optimization extends beyond single-axis methods to cut LLM inference latency and compute cost by allowing models to exit across multiple optimization axes simultaneously.