Enhancing LLM Problem Solving via Tutor-Student Multi-Agent Interaction
Enhancing LLM Problem Solving via Tutor-Student Multi-Agent Interaction explores A tutor-student multi-agent system that enhances LLM problem-solving performance in coding tasks through structured, role-differentiated interaction.. Commercial viability score: 7/10 in Agents.
Use This Via API or MCP
Use the canonical paper page as a proof artifact
This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Owned Distribution
Subscribe to the weekly brief
Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisAsymmetric Actor-Critic for Multi-turn LLM AgentsViability: 6.0
- Builds On ThisAdaptive Confidence Gating in Multi-Agent Collaboration for Efficient and Optimized Code GenerationViability: 3.0
- Builds On ThisConversation for Non-verifiable Learning: Self-Evolving LLMs through Meta-EvaluationViability: 4.0
- Prior WorkFrom Untamed Black Box to Interpretable Pedagogical Orchestration: The Ensemble of Specialized LLMs Architecture for Adaptive TutoringViability: 7.0
- Prior WorkAdaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual Learning
Related Resources
- TransportAgents(glossary)
- AgentSpeak(glossary)
- Mixture-of-Agents(glossary)
- What is the future of AI agents according to Nothing's CEO?(question)
- How do LLM efficiency advancements impact the development of AI agents?(question)
- How does AgentXRay contribute to the explainability of AI agents in complex decision-making processes?(question)
- Agents – Use Cases(use_case)