Tag
The PRAG framework combines traditional RAG with a Paninian rule engine for safer medical AI, achieving a 71% reduction in unsafe answers on MedQA. It provides auditable rule traces and is open-sourced.
This paper presents ESBMC-PLC, the first open-source formal verifier with native support for IEC 61131-3 Ladder Diagram programs using SMT-based model checking, enabling automated verification of safety-critical industrial control logic.
DiRecT introduces a training-free algorithm for safe diffusion-based planning that enforces constraints only on final clean trajectories using receding-horizon denoising, improving safety and performance over existing methods.
BadWorld is a label-free adversarial framework that reveals structural vulnerabilities in visual world models by generating imperceptible perturbations that cause catastrophic failures in future rollouts.
This paper proposes SafeLLM, an extraction-based approach for retrieving information from safety-critical documents, showing that line-number selection outperforms rewriting-based RAG methods in reducing hallucinations while maintaining high recall.
ScenePilot proposes a feasibility-guided, boundary-driven framework for generating safety-critical scenarios for autonomous driving, using constrained multi-objective reinforcement learning to produce physically valid yet failure-inducing scenarios.
This paper proposes a novel transformer verification approach that uses ReLU to represent precise but non-linear bounds for dot products, enabling precise and efficient verification. The method outperforms state-of-the-art baselines on sentiment analysis models.