X-RIC: An Agentic RAN-Intelligent Controller Benchmark for Enhancing Reinforcement Learning
- Published
- Source
- arXiv
- Paper number
- 1214
- Field
- Reasoning / RL
- arXiv ID
- 2610.22269
Key points
- The authors propose eXplainable RIC (X-RIC), a framework that integrates agentic AI into O-RAN architectures.
- The BARX evaluation framework assesses agentic reasoning candidates in closed-loop control across four dimensions: latency, compliance, determinism, and policy impact.
- The Telecom Language Model (TLM) augments LLaMA3-8B with a trainable RAN-state encoder that maps network telemetry into 256-dimensional embeddings.
- On SLA-aware network slicing it achieves 89% operational cost reduction, converging 75% faster with 50% fewer training steps and 46% lower variance.
- The non-fine-tuned LLM increases cost by 90.3% due to hallucinations that violate resource constraints.
- On the OpenAirInterface over-the-air testbed, X-RIC500 maintains margins of 17 Mbps for eMBB and 5 ms for URLLC.
Paper links
External research summaries. These are not HDATF publications or measured product results.