Negar Shahabi

Saeid Jamshidi, Kawser Wazed Nafi, Arghavan Moradi Dakhel et al.

The rapid advancement and adaptability of Large Language Models (LLMs) highlight the need for moral consistency, the capacity to maintain ethically coherent reasoning across varied contexts. Existing alignment frameworks, structured approaches designed to align model behavior with human ethical and social norms, often rely on static datasets and post-hoc evaluations, offering limited insight into how ethical reasoning may evolve across different contexts or temporal scales. This study presents the Moral Consistency Pipeline (MoCoP), a dataset-free, closed-loop framework for continuously evaluating and interpreting the moral stability of LLMs. MoCoP combines three supporting layers: (i) lexical integrity analysis, (ii) semantic risk estimation, and (iii) reasoning-based judgment modeling within a self-sustaining architecture that autonomously generates, evaluates, and refines ethical scenarios without external supervision. Our empirical results on GPT-4-Turbo and DeepSeek suggest that MoCoP effectively captures longitudinal ethical behavior, revealing a strong inverse relationship between ethical and toxicity dimensions (correlation rET = -0.81, p value less than 0.001) and a near-zero association with response latency (correlation rEL approximately equal to 0). These findings demonstrate that moral coherence and linguistic safety tend to emerge as stable and interpretable characteristics of model behavior rather than short-term fluctuations. Furthermore, by reframing ethical evaluation as a dynamic, model-agnostic form of moral introspection, MoCoP offers a reproducible foundation for scalable, continuous auditing and advances the study of computational morality in autonomous AI systems.

Saeid Jamshidi, Negar Shahabi, Foutse Khomh et al.

Software-Defined Networking (SDN) is increasingly adopted to secure Internet-of-Things (IoT) networks due to its centralized control and programmable forwarding. However, SDN-IoT defense is inherently a closed-loop control problem in which mitigation actions impact controller workload, queue dynamics, rule-installation delay, and future traffic observations. Aggressive mitigation may destabilize the control plane, degrade Quality of Service (QoS), and amplify systemic risk. Existing learning-based approaches prioritize detection accuracy while neglecting controller coupling and short-horizon Reinforcement Learning (RL) optimization without structured, auditable policy evolution. This paper introduces a self-reflective two-timescale SDN-IoT defense solution separating fast mitigation from slow policy governance. At the fast timescale, per-switch Proximal Policy Optimization (PPO) agents perform controller-aware mitigation under safety constraints and action masking. At the slow timescale, a multi-agent Large Language Model (LLM) governance engine generates machine-parsable updates to the global policy constitution Pi, which encodes admissible actions, safety thresholds, and reward priorities. Updates (Delta Pi) are validated through stress testing and deployed only with non-regression and safety guarantees, ensuring an auditable evolution without retraining RL agents. Evaluation under heterogeneous IoT traffic and adversarial stress shows improvements of 9.1% Macro-F1 over PPO and 15.4% over static baselines. Worst-case degradation drops by 36.8%, controller backlog peaks by 42.7%, and RTT p95 inflation remains below 5.8% under high-intensity attacks. Policy evolution converges within five cycles, reducing catastrophic overload from 11.6% to 2.3%.