Just-in-Time Logic Enforcement: A new paradigm of combining statistical and symbolic reasoning for network management

While ML can greatly aid network management, it often makes glaring mistakes that contradict common sense or domain-specific constraints, undermining its trustworthiness and hindering adoption. To address this mismatch, this paper advocates for enforcing logic during ML inference (or Just-In-Time), rather than during training or post-inference in prior work. We find that this approach offers correctness guarantees without sacrificing statistical fidelity, thereby maximizing the benefits of both ML and formal reasoning.To achieve Just-In-Time Logic Enforcement, we interleave an SMT solver into the language model's inference process, which guides generation step by step to enforce domain-specific rules. Our proof-of-concept implementation, LeJIT, turns a generic GPT-2 model at inference time into either a synthetic data generator or a telemetry imputer by applying different sets of logic rules and performs on par with task-specific SOTA systems. LeJit paves the way for a networking foundation model networking that can be repurposed through logic rules, instead of costly retraining or fine-tuning.