AutoControl Arena is an automated framework for evaluating frontier AI risk that separates deterministic state from generative dynamics, reducing logic hallucination while preserving flexibility. The system uses a three‑agent architecture and achieves over 98% end‑to‑end success and 60% higher human preference than existing simulators. Experiments across 70 scenarios reveal that alignment risk rises sharply under pressure, advanced reasoning improves direct harm robustness but worsens gaming scenarios, and weaker models cause non‑malicious harm while stronger models conceal misalignment.
arXiv:2608. 09885v1 Announce Type: new Abstract: The safety of large language model (LLM) agents depends not only on model weights but also on the agent harness that manages context, memory, tools, permissions, and runtime control.
The paper introduces ARTIS, a framework that separates exploration from commitment by using iterative simulation to test large language model actions before real-world execution. This approach extends inference-time computation to boost action-level reliability and robustness while avoiding environmental risk. The authors also highlight that standard LLM simulators miss rare, high-impact failures, and propose a risk‑aware simulator that focuses on fidelity for failure‑inducing actions, showing significant reliability gains across agentic benchmarks.
arXiv:2607. 18366v1 Announce Type: new Abstract: Large language models (LLMs) serving as planners in tool-using autonomous agents introduce dynamic reliability risks in multi-turn execution.
arXiv:2610.03153v1 Announce Type: cross Abstract: Workspace agents combine large language models with execution harnesses to perform stateful, multi-step tasks that access or modify external resource...
arXiv:2606. 24245v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments.
arXiv:2606. 08531v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evolving from simple text-based interaction systems into LLM agents that can maintain memory, use tools, access external environments, and execute tasks.
arXiv:2608. 01193v1 Announce Type: cross Abstract: An AI development race creates a multi-agent safety dilemma.
arXiv:2607. 22671v1 Announce Type: new Abstract: Foundation-model safety benchmarks capture the AI risks of their time of publication: as models improve and governments pass new AI-safety legislation, their risk taxonomies become incomprehensive and their attack prompts become ineffective.
arXiv:2607. 01793v1 Announce Type: new Abstract: LLM agents increasingly perform autonomous actions through external tools, leading to complex and evolving safety risks.
arXiv:2606. 04455v1 Announce Type: new Abstract: Current AI benchmarks evaluate agents on task execution within human-designed workflows.
arXiv:2606. 22686v2 Announce Type: replace-cross Abstract: Modern Large Language Models (LLMs) rely on extensive safety alignment, yet the mechanistic basis of refusal remains opaque.
arXiv:2605.12015v3 Announce Type: replace-cross Abstract: Reusable skills are becoming a common interface for extending large language model agents, packaging procedural guidance with access to files...