# Rivaling Transformers: Multi-Scale Structured State-Space Mixtures for Agentic 6G O-RAN

arXiv: https://arxiv.org/abs/2510.05255 · HTML: https://mlsanity.com/paper/2510.05255/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2025.

## Built on by (most cited first, 4 of 4)

- [A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks](https://mlsanity.com/paper/2510.19973/) (2025)
- [Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning](https://mlsanity.com/paper/2511.02748/) (2025)
- [Agentic AI as a Network Control-Plane Intelligence Layer for Federated Learning over 6G](https://mlsanity.com/paper/2603.09141/) (2026)
- [LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G](https://mlsanity.com/paper/2601.12375/) (2026)

## Abstract (arXiv)

In sixth-generation (6G) Open Radio Access Networks (O-RAN), proactive control is preferable. A key open challenge is delivering control-grade predictions within Near-Real-Time (Near-RT) latency and computational constraints under multi-timescale dynamics. We therefore cast RAN Intelligent Controller (RIC) analytics as an agentic perceive-predict xApp that turns noisy, multivariate RAN telemetry into short-horizon per-User Equipment (UE) key performance indicator (KPI) forecasts to drive anticipatory control. In this regard, Transformers are powerful for sequence learning and time-series forecasting, but they are memory-intensive, which limits Near-RT RIC use. Therefore, we need models that maintain accuracy while reducing latency and data movement. To this end, we propose a lightweight Multi-Scale Structured State-Space Mixtures (MS3M) forecaster that mixes HiPPO-LegS kernels to capture multi-timescale radio dynamics. We develop stable discrete state-space models (SSMs) via bilinear (Tustin) discretization and apply their causal impulse responses as per-feature depthwise convolutions. Squeeze-and-Excitation gating dynamically reweights KPI channels as conditions change, and a compact gated channel-mixing layer models cross-feature nonlinearities without Transformer-level cost. The model is KPI-agnostic -- Reference Signal Received Power (RSRP) serves as a canonical use case -- and is trained on sliding windows to predict the immediate next step. Empirical evaluations conducted using our bespoke O-RAN testbed KPI time-series dataset (59,441 windows across 13 KPIs). Crucially for O-RAN constraints, MS3M achieves a 0.057 s per-inference latency with 0.70M parameters, yielding 3-10x lower latency than the Transformer baselines evaluated on the same hardware, while maintaining competitive accuracy.