Semantic Cooperative Games for Contribution Attribution in LLM-Based Multi-Agent Systems
Contribution attribution has become a central problem in LLM-based multi-agent systems, where final outputs are produced through multiple agents, message exchanges, and ordered workflow dependencies. Existing attribution methods often rely on counterfactual valuation, such as removing agents or comparing score changes across altered agent subsets. In language-mediated workflows, these methods require repeated model calls, introduce high variance, and do not explicitly capture the intermediate semantic states through which agents produce, preserve, and transform task-relevant information. We propose Semantic Cooperative Games (SCG), a framework that represents a realized language flow as a...
| Authors | Pengyi Jiang, Xiaoguang Zhu, Quanyan Zhu |
|---|---|
| Primary category | cs.AI |
| Published | 14 May 2026 |
| Last updated at source | 1 October 2026 |
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