arXiv:2601. 21249v2 Announce Type: replace Abstract: Breakthroughs in language and vision have motivated increasingly general foundation models for time series and physical dynamics, where evidence is promising but less mature.
arXiv:2605. 17909v2 Announce Type: replace Abstract: As autonomous agentic systems scale across regulated critical infrastructures, the lack of mechanistic, hardware-rooted enforcement for high-frequency policy updates presents a fundamental safety gap.
LLM-based agents have shown strong capabilities in automated data analysis and are increasingly moving toward long-horizon, multi-stage analytical workflows. However, as the analytical process evolves...
arXiv:2607. 22868v1 Announce Type: new Abstract: Runtime guardrails act before irreversible tool calls, but their guarantees depend on what policy state is representable, what a judge observes, and whether intervention changes future behavior.
arXiv:2607. 16130v1 Announce Type: cross Abstract: AI governance increasingly requires judgments about whether an AI system remains adequately trustworthy over time, whether observed changes are tolerable, and how such judgments should be documented in a transparent and contestable way.
The paper introduces LogiC-Diff, a logic-conditioned bi-stage diffusion framework that embeds Signal Temporal Logic (STL) specifications into AI-enabled cyber‑physical system (CPS) forecasting models. By using STL as a conditioning signal, the method repairs inputs and refines outputs to jointly mitigate adversarial perturbations and enforce desired temporal behaviors. Experiments on two real‑world CPS datasets show that LogiC-Diff consistently improves robustness and specification compliance across various sensor faults and cyber attacks, outperforming reconstruction‑based defenses.
arXiv:2606. 18532v1 Announce Type: cross Abstract: AI systems are increasingly evaluated in bounded environments that combine isolation, simulation, instrumentation, supervision, and evidence capture.
arXiv:2607. 19292v1 Announce Type: cross Abstract: Current AI safety discourse still focuses disproportionately on visible failures, including obvious harms, dramatic misuse, and hypothetical catastrophic scenarios.
arXiv:2607. 29405v1 Announce Type: new Abstract: Agentic AI systems act through multi-step trajectories that combine planning, tool use, memory, interaction, and adaptation.
The article surveys how large language models (LLMs) can be incorporated into networked control systems, cyber‑physical systems, and multi‑agent networks without violating stability and safety guarantees. It proposes treating the LLM as a slow supervisor that sets high‑level goals, while a fast, certified inner loop preserves physical stability. The survey maps LLM characteristics—such as inference latency, API failures, tokenization, and hallucinations—to classical control challenges and highlights the growing gap between model capability and formal safety assurances, calling for future research on stability proofs.
arXiv:2609.06036v1 Announce Type: new Abstract: Proposal-based controllers---learned policies, language-model planners, and other black-box \emph{generators}---are increasingly deployed behind runtim...
arXiv:2604. 02478v2 Announce Type: replace Abstract: Deep learning models excel at detecting anomaly patterns in normal data.
The paper introduces SecTB-RTL, an auditable framework for evaluating AI-generated RTL verification plans against 31 tasks and 124 hardware‑security regressions. In a confirmatory run, the AI model’s responses were rejected by the provider’s schema, and after a schema‑only repair, only nine of 1,857 accepted responses passed the production semantic validator, revealing a mismatch between generation and execution rules. The study demonstrates that schema acceptance does not guarantee execution validity and provides a benchmark, failure‑preserving contract, incident provenance, and governance controls to prevent misreporting of infrastructure behavior as model behavior.