7 articles tagged with #temporal-logic. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers developed RoboGuard, a two-stage safety architecture to protect LLM-enabled robots from harmful behaviors caused by AI hallucinations and adversarial attacks. The system reduced unsafe plan execution from over 92% to below 3% in testing while maintaining performance on safe operations.
Researchers introduce a neuro-symbolic framework that integrates Linear Temporal Logic constraints into transformer-based reinforcement learning policies, enabling AI systems to satisfy high-level temporal requirements while maintaining competitive performance. The method compiles logical specifications into deterministic finite automata and uses differentiable signals to regularize training, demonstrating improved constraint satisfaction in navigation tasks.
Researchers introduce a novel shielding framework for reinforcement learning agents that guarantees safety without requiring prior knowledge of system dynamics. By combining robust MDPs with linear temporal logic specifications and PAC learning guarantees, the approach enables the creation of minimally restrictive safety shields for unknown environments while maintaining strong performance as data accumulates.
Researchers propose a Neuro-Symbolic Predictive Process Monitoring approach that combines deep learning with Linear Temporal Logic constraints to improve suffix prediction accuracy in business process management. The method introduces a differentiable logical loss function that ensures generated sequences satisfy both predictive accuracy and temporal logic constraints, with applications extending beyond BPM to general symbolic sequence generation tasks.
Researchers propose a meta-programming framework that enables flexible implementation of temporal logic extensions for Answer Set Programming (ASP) through a unified declarative system. The work introduces metasp, a tool that allows rapid exploration of different temporal logics—including linear-time (TEL), metric (MEL), and dynamic (DEL) variants—without modifying core ASP system code.
Researchers present a novel logical framework for understanding encoder-decoder transformers using temporal logic extended with counting and past modalities. The work provides theoretical foundations for how these architectures process information across attention mechanisms, with implications for LLM interpretability and design.
Researchers developed PPO-LTL, a new framework that integrates Linear Temporal Logic safety constraints into Proximal Policy Optimization for safer reinforcement learning. The system uses Büchi automata to monitor safety violations and converts them into penalty signals, showing reduced safety violations while maintaining competitive performance in robotics environments.
