arXiv:2605. 09045v2 Announce Type: replace Abstract: Agentic frameworks are the software layer through which AI agents act in the world.
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arXiv:2608. 14590v1 Announce Type: new Abstract: LLM agents increasingly perform irreversible real-world actions, including database updates, API calls, file operations, and autonomous use of tools.
arXiv:2608. 03609v1 Announce Type: new Abstract: Agentic systems driven by large language models (LLMs) are increasingly deployed in real-world workflows where they act on persistent operational data.
arXiv:2608. 11274v1 Announce Type: cross Abstract: The dominant paradigm treats AI safety as a property to be instilled during model training via RLHF, DPO, or Constitutional AI.
The paper introduces MAGS, a multi-agent framework that automatically generates executable programs with formal safety guarantees. MAGS translates LLM-generated code into the verification-aware language Dafny, repairs any safety violations using verifier feedback, and then compiles the verified code back into executable form. Evaluations on 220 diverse examples—including CUDA kernels, terminal scripts, and robotic-arm tasks—show a 100% success rate in producing programs that meet frozen safety specifications, with additional safety and functional tests confirming strong performance across domains.
arXiv:2602. 20064v2 Announce Type: replace-cross Abstract: Large language models are increasingly deployed as agents: they plan, call tools, read untrusted data, and act on the results.
Sapien is a policy engine that enforces stateful contextual policies for autonomous AI agents, specifying allowed tool‑call sequences with an extended regular expression that includes stateful predicates, deferred policy generation, and scoped semantic checks. The system maintains performance close to an unconstrained agent while significantly reducing malicious actions, ruling out 93‑95% of attacks on AgentDojo and 62‑85% on Toolathlon, outperforming traditional tool allowlists on long‑horizon tasks.