Organizations: Department of Computer Science, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4QF, UK. · British Telecom, UK.
Adaptive control in AI-native networks remains challenging because tasks such as dynamic Radio Access Network (RAN) slicing require persistent trade-off decisions among spectrum efficiency, service quality, and reconfiguration stability. Conventional Reinforcement Learning (RL) compresses these competing objectives into handcrafted scalar rewards, requiring exhaustive tuning for a balanced trade-off. Language model (LM) agents provide a new path toward adaptive control because they can semantically represent complex preference relations, but prompt-based adaptation struggles to retain long-horizon interaction history due to finite context limits. We propose a Reflection-Driven Self-Improvement (ReDSI) framework that replaces scalar rewards with semantic feedback for continuous policy adaptation. This framework is built upon a Small Language Model (SLM) agent for fast decision-making, coupled with a bi-perspective reflection to transform interaction history into preference data, subsequently distilling long-horizon decision experience into the SLM parameters through preference-based fine-tuning. Evaluated on a dynamic RAN slicing task, our method achieves an optimal trade-off across multiple competing objectives, outperforming standard RL baselines and existing LM-based agents by demonstrating substantially greater sample efficiency. These results highlight a viable paradigm of self-improving LM agents for reward-free multi-objective continuous control in AI-native networks.
Figures & tables
Fig. 1 : Overview of ReDSI. (a) During online policy inference, the Actor allocates PRBs based on state feedback, while its interactions with the RAN environment are recorded as trajectory H . The O-RAN elements illustrate a conceptual deployment mapping. (b) During offline parallel reflection, Reflector workers use the full trajectory H to label each decision as positive ( + ) or negative ( − ). They generate a candidate action for each negatively labeled decision, forming the reflection-labeled trajectory H′ . (c) During offline policy refinement, each RfR iteration generates rollout outputs R from original negative-example prompts, combines H′ and R into preference dataset D , and updates the Actor through KTO. The same H′ is reused without additional environment interaction. Updated parameters support subsequent RfR iterations and online control.
Metric
GBR Traffic
Non-GBR Traffic
Active UE count
20
4
Transmission duration
Exp(mean = 15 sec)
Exp(mean = 15 sec)
Idle duration
Exp(mean = 15 sec)
Exp(mean = 15 sec)
Bit rate
0.5 Mb/s
2 Mb/s
Packet Size
512 bytes
512 bytes
QoS Requirement
Delay < 10 ms
Delay < 50 ms
TABLE I : Traffic Model Parameters
Parameter
Value
Transmission power
30 dBm
Base station antenna gain
0 dB
Base station antenna pattern
Antenna Model in 3GPP TR 38.901
Noise figure
5 dB
Carrier frequency
2120 MHz
Propagation model
Urban Propagation Loss Model
TABLE II : Radio Channel Parameters
Fig. 2 : Performance comparison of RL baselines, Reflexion, and the proposed ReDSI framework across multiple objectives
Alg.
Env. Steps
Avg. SE
Reconf.
PQoS
Ep. Return
ReDSI
300
5.35 ± 0.07
21.09 ± 6.59
8.56 ± 3.76
25702.2
Reflexion
300
5.29 ± 0.04
29.45 ± 9.26
8.63 ± 5.25
25314.69
DQN
4.8e5
5.21 ± 0.26
46.20 ± 9.60
15.91 ± 4.31
22519.1
PPO
4.8e5
3.58 ± 0.09
119.6 ± 21.08
1.10 ± 1.04
18532.3
SAC
4.8e5
5.74 ± 0.09
44.77 ± 16.2
59.96 ± 19.9
11704.3
TABLE III : Performance comparison (mean ± standard deviation). ‘Env. Steps’ reports training/adaptation environment steps per run. ‘Ep. Return’ sums per-slice rewards over three slices and 300 steps. The best and second-best results are marked in bold and underline , respectively.
Fig. 4 : KTO training dynamics of ReDSI using one reflection-labeled trajectory. (a)–(f) Chosen and rejected scores across six RfR iterations. (g) Scores gradually approaching zero, indicating diminishing returns from repeated refinement on the same trajectory.
Open Radio Access Networks (O-RAN) promise flexible 6G network access through disaggregated, software-driven components and open interfaces, but this programmability also increases operational complexity. Multiple control loops coexist across the service management layer and RAN Intelligent Controller (RIC), while independently developed control applications can interact in unintended ways. In parallel, recent advances in generative Artificial Intelligence (AI) are enabling a shift from isolated AI models toward agentic AI systems that can interpret goals, coordinate multiple models and control functions, and adapt their behavior over time. This article proposes a multi-scale agentic AI framework for O-RAN that organizes RAN intelligence as a coordinated hierarchy across the Non-Real-Time (Non-RT), Near-Real-Time (Near-RT), and Real-Time (RT) control loops: (i) A Large Language Model (LLM) agent in the Non-RT RIC translates operator intent into policies and governs model lifecycles. (ii) Small Language Model (SLM) agents in the Near-RT RIC execute low-latency optimization and can activate, tune, or disable existing control applications; and (iii) Wireless Physical-layer Foundation Model (WPFM) agents near the distributed unit provide fast inference close to the air interface. We describe how these agents cooperate through standardized O-RAN interfaces and telemetry. Using a proof-of-concept implementation built on open-source models, software, and datasets, we demonstrate the proposed agentic approach in two representative scenarios: robust operation under non-stationary conditions and intent-driven slice resource control.
Hojjat Navidan, Mohammad Cheraghinia, Jaron Fontaine +5
IDLab, Department of Information Technology at Ghent University - imec, Ghent, Belgium · Department of Electrical and Computer Engineering, Princeton University, USA
The convergence of multiple network domains, including radio access, optical transport, and core networks, under unified intelligent control is a fundamental requirement for future 6G systems. This is important because existing network controllers remain largely domain-specific, such as the O-RAN RIC for radio, or they lack native support for AI-driven automation across heterogeneous infrastructure. As a result, safe and coordinated agentic control of multi-domain networks is still an open challenge. In this paper, we present the AI-Native Network Controller (AI-NNC), an open-source and modular framework that enables agentic AI control across diverse network domains. The framework is designed around a protocol-agnostic architecture in which each physical device is integrated through a lightweight Python adapter, while control logic is implemented through domain-specific control applications. Beyond closed-loop control, the framework also supports dataset collection, agentic AI experimentation, and coordinated testbed operation using the same validated control and measurement interfaces. This design enables a safer paradigm for autonomous network management, where AI agents operate through validated applications rather than issuing commands directly to network equipment.
Merim Dzaferagic
School of Computer Science and Statistics, Trinity College Dublin, Ireland
The O-RAN ALLIANCE introduces architectural flexibility through disaggregation and AI-driven control. While the RAN Intelligent Controller (RIC) enables centralized optimization, current AI approaches lack reasoning capabilities and explainability. We propose eXplainable RIC (X-RIC), a novel framework integrating agentic AI into O-RAN architectures. X-RIC combines reactive Reinforcement Learning (RL) with strategic agentic reasoning through: (i) automated Behavioral Cloning (BC) using domain heuristics for sample-efficient training, (ii) a Telecom Language Model (TLM), a transformer-based reasoning module providing contextual abstraction and interpretability, and (iii) closed-loop co-adaptation within the Near-RT RIC control loop enabling continuous policy refinement. To ensure deployment readiness, we introduce BARX (Benchmarking Agentic Reasoning for RAN), a domain-specific evaluation framework assessing agentic control across latency, compliance, and determinism metrics. We validate X-RIC on SLA-aware network slicing in simulation and O-RAN-compliant testbed deployments. X-RIC achieves 89% operational cost reduction while maintaining SLA compliance, converging 75% faster with 50% fewer training steps and 46% lower variance.
Adhwaa Al-Chaab, Ayman Younis, Dario Pompili
†Department of Electrical and Computer Engineering, Rutgers University–New Brunswick, NJ, USA