AECG: Asymmetric Experience Consolidation and Governance In Multi-Agent Systems

arXiv:2610.05176 · cs.AI · Submitted 2026-10-04 · Read on arXiv

cs.AI

Submitted: 2026-10-04

Updated: 2026-10-04

Comments: 22 pages,5 figures

Code: https://github.com/fenhg297/AECG

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Multi-Agent System — 1× in this paper · explained in 6 episodes

Sources

• Improving Factuality and Reasoning in Language Models through Multiagent Debate
• Learning Hierarchical Procedural Memory for LLM Agents through Bayesian Selection and Contrastive Refinement
• LEGOMem: Modular Procedural Memory for Multi-agent LLM Systems for Workflow Automation
• Discounted Beta-Bernoulli Reward Estimation for Sample-Efficient Reinforcement Learning with Verifiable Rewards
• MemGPT: Towards LLMs as Operating Systems
• Cognitive Architectures for Language Agents
• ConMem: Structured Memory-Guided Adaptation in Training-Free Multi-Agent Systems
• From Memory to Skills: Evidence-Grounded Co-Evolution Governance for Long-Horizon LLM Agents
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• Agent Workflow Memory
• AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
• Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems
• Bayesian-Agent: Posterior-Guided Skill Evolution Across LLM Agent Harnesses
• Useful Memories Become Faulty When Continuously Updated by LLMs

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