Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems

Key points

Individual LLM agents run into limits on sustained coordination, adaptation, and multi-step tasks across roles and environments.
Existing research on LLM-based agents is fragmented, with individual capability, multi-agent collaboration, and self-evolution treated as separate topics, which blocks a holistic understanding of their interdependence.
In tightly coupled multi-agent systems, error propagation and cascading failures are hard to diagnose automatically, and current systems lack mechanisms that turn diagnostic insight into structured self-improvement.
The paper presents the LIFE progression framework, which structures the operational lifecycle into four causally linked stages: Lay for capability-based build-up, Integrate for agent integration through collaboration, Find for defect discovery through cause attribution, and Evolve for autonomous self-improvement.
It systematically reviews and formalizes each stage, including taxonomies of individual agent components, multi-agent collaboration mechanisms, failure attribution methods, and self-evolution mechanisms.
It proposes and analyzes a closed-loop self-improvement framework in which failure diagnosis feeds directly into structural adaptation, such as collaboration reconfiguration or agent behavior improvement.

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