The Flow has not summarised this story yet — read it at arXiv Computation and Language.
NEXUS is a modular neuro‑symbolic framework that simultaneously learns a PDDL domain model and an LTL safety specification for embodied agents. It repairs action schemas using execution failures and converts detected hazards into reusable LTL constraints via hierarchical semantic and translation sampling. During symbolic search, LTL progression enforces these constraints, allowing the system to differentiate between physically unreachable states and safety‑induced refusals, and experiments on SafeAgentBench demonstrate superior safety‑task completion and low violation rates even under jailbreak attacks.
arXiv:2607. 14543v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used as the reasoning backbone of embodied agents, enabling robots to interpret visual scenes, follow language instructions, and plan multi-step actions.
arXiv:2602.03100v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed as agents that operate in real-world environments, introducing safety risks beyond linguisti...
arXiv:2608.21928v1 Announce Type: new Abstract: In embodied AI, safety risk can be latent: a benign instruction and a safe scene become hazardous only when composed. Prior work has advanced embodied...
arXiv:2510. 12985v3 Announce Type: replace Abstract: We present SENTINEL, a framework for formally evaluating the physical safety of foundation model (FM)-based embodied agents.
arXiv:2608. 19729v1 Announce Type: new Abstract: Vision-language-model-based embodied agents can complete instructed tasks but often violate safety constraints in the process, a problem recently framed as interactive safety.