AECG: Asymmetric Experience Consolidation and Governance In Multi-Agent Systems
Large language model (LLM)-based multi-agent systems increasingly rely on memory to transform execution trajectories into reusable procedural knowledge. Yet repeated retrieval also makes memory errors persistent: memory pollution arises when outdated, weakly supported, or spuriously successful procedures become recurring components of future reasoning. Multi-agent execution introduces an additional structural risk. Scope collapse occurs when procedural knowledge escapes the coordination scope in which it was shown effective and is repeatedly reused at incompatible decision levels, allowing local errors to influence cascades of downstream decisions. Meanwhile, task-level failures provide a...
| Authors | Ao Tian, Jialong Liu, Daqi Zheng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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