arXiv cs.AI· Xijun Wang, Zhaoyang Liu, Xiang Chen, Howard H. Yang, Chenyuan Feng·· 1 天前AI 评分22
Context Graph：AI-RAN 中可解释的多智能体决策链
Context Graph: Explainable Multi-Agent Decision Chains in AI-RAN
AI 导读
研究者提出 Context Graph，一种异构有向图，将策略、测量、决策与告警组织成可查询的解释层，用于追踪 AI-RAN 中多个 xApp、rApp 智能体的跨智能体决策链。
正文 · 原文
Abstract:AI-RAN distributes network control across multiple autonomous agents, namely xApps and rApps operating over shared radio resources. When an alert or degradation appears, operators currently lack a structured way to trace which decision caused it, what context triggered that decision, and how other agents interacted. Existing explainability tools focus on single-model feature attribution or linear event logging, neither of which captures cross-agent decision chains. We propose the Context Graph, a heterogeneous directed graph that organizes policies, measurements, decisions, and alerts into a queryable explanation layer. Its foundational primitive is the Decision-Event node acting as a causal junction, where incoming edges capture triggering context and outgoing edges track downstream consequences that are progressively upgraded through counterfactual verification. Instantiated over existing E2, A1, and O1 data flows, our proof-of-concept demonstrates that compliance and diagnosis queries are resolved at sub-millisecond latency over graphs of tens of thousands of nodes, while reliably distinguishing true causal impacts from spurious correlations.
| Comments: | This work has been submitted to the IEEE for possible publication |
| Subjects: | Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Information Theory (cs.IT) |
| Cite as: | arXiv:2611.16879 [cs.NI] |
| (or arXiv:2611.16879v1 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16879 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xijun Wang [view email]
[v1]
Thu, 12 Nov 2026 14:43:22 UTC (226 KB)
来源：arXiv cs.AI · arxiv.org