GVS5H: Zero-Shot Self-Orchestration with Ledger-Based Control for Improved LLM Coding Performance

arXiv:2608.26480 · cs.MA, cs.AI, cs.CL, cs.SE · Submitted 2026-08-27 · Read on arXiv

cs.MA, cs.AI, cs.CL, cs.SE

Submitted: 2026-08-27

Updated: 2026-09-21

Terminology

• Accuracy — 11× in this paper · explained in 1 episode
• Benchmark — 9× in this paper · explained in 2 episodes
• Verifier — 8× in this paper · explained in 2 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Multi-Agent System — 3× in this paper · explained in 6 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• GRPO — 1× in this paper · explained in 6 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Multi-agent Collaborative Framework — 1× in this paper · explained in 2 episodes
• Multi-hop Reasoning — 1× in this paper · explained in 3 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Self-consistency — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Large Language Model based Multi-Agents: A Survey of Progress and Challenges
• Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?
• Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets
• ReAct: Synergizing Reasoning and Acting in Language Models
• Reflexion: Language Agents with Verbal Reinforcement Learning
• Self-Refine: Iterative Refinement with Self-Feedback
• Sakana Fugu Technical Report
• Learning to Orchestrate Agents in Natural Language with the Conductor
• RouteLLM: Learning to Route LLMs with Preference Data
• MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
• ChatDev: Communicative Agents for Software Development
• AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
• CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
• AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation
• MapCoder: Multi-Agent Code Generation for Competitive Problem Solving
• TRINITY: An Evolved LLM Coordinator
• ARIADNE: Agentic Reward-Informed Adaptive Decision Exploration via Blackboard-Driven MCTS for Competitive Program Generation
• Exploring Advanced LLM Multi-Agent Systems Based on Blackboard Architecture
• Improving Factuality and Reasoning in Language Models through Multiagent Debate
• More Agents Is All You Need

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