On the Stability and Realizability of Recurrent Polynomial Surrogate Ternary Logic Gate Networks
arXiv:2605.24649v1 Announce Type: new Abstract: Recurrent Neural Networks (RNNs) can learn to predict Signal Temporal Logic (STL) verdicts online from partial trajectories, but deploying them as runtime monitors in safety-critical systems demands more than predictive accuracy. Standard RNN architectures offer no structural guarantee that outputs degrade gracefully under sensor degradation; a dropped input can silently flip a verdict from safe to unsafe. We introduce the Recurrent Differentiable Ternary Logic Gate Network (R-DTLGN), a recurrent architecture that operates over Kleene's three-val
The increasing reliance on AI for safety-critical systems necessitates new architectures that provide robustness against input degradation, a problem highlighted by the limitations of current RNNs.
This research directly addresses a core challenge in deploying AI in high-stakes environments, potentially unlocking broader real-world applications for AI agents and autonomous systems.
The introduction of R-DTLGN offers a novel architectural approach to recurrent networks, providing structural guarantees for graceful degradation under sensor failure, differentiating it from standard RNNs.
- · AI developers
- · Safety-critical system operators
- · Semiconductor industry
- · Autonomous vehicle manufacturers
- · Companies relying on non-robust AI models for safety-critical applications
Improved reliability and trustworthiness of AI systems in demanding operational contexts.
Accelerated adoption of AI in industries with strict safety regulations, such as defense, aerospace, and medical devices.
The development of new hardware optimized for ternary logic and robust recurrent architectures, influencing compute supply chains.
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