arXiv:2606. 08021v1 Announce Type: cross Abstract: As large language model (LLM) agents are integrated into autonomous cloud operations, distributed systems face a semantic reliability problem: proposer agents can generate production mutations, such as modifying IAM policies, opening firewall security groups, or executing data exports, that are syntactically valid and statically authorized but operationally unsafe.
By Jun He, Deying Yu
The paper introduces PACT, a benchmark designed to evaluate how well enterprise AI assistants follow compliance rules when faced with various pressures such as persistent users or hurried managers. PACT covers twelve regulated domains and forty-eight realistic multi‑turn scenarios, pairing each rule with a shortcut that violates it and applying different pressures across wording and system‑prompt modes. Using PACT, the authors profile six metrics of compliance and aggregate them into a PACTScore, revealing significant variability among 22 LLM models and that even top performers misapply rules 6–10% of the time, with user pressure increasing violations by 65% on average.
By Mika Okamoto, Ansel Kaplan Erol
arXiv:2607. 24006v1 Announce Type: cross Abstract: Cloud telemetry arrives at a scale that, paradoxically, makes intrusion understanding harder rather than easier.
By Mohan Manivannan, Dalal Alharthi
arXiv:2608. 15101v1 Announce Type: new Abstract: Policy evaluation often estimates direct benefits and costs while treating the institutional environment as fixed.
By Wesley Shu
arXiv:2606. 02965v1 Announce Type: new Abstract: Benchmarks for autonomous agents measure whether agents complete tasks, yet this framing is systematically blind to whether an agent should have proceeded at all.
By Victor Ojewale, Suresh Venkatasubramanian
arXiv:2510. 20692v3 Announce Type: replace-cross Abstract: Access control policies are reliability-critical configuration artifacts in cloud systems, yet administrators frequently struggle to verify that a policy permits exactly what they intend.
By Adarsh Vatsa, Bethel Hall, William Eiers
arXiv:2607. 18267v1 Announce Type: cross Abstract: The transition to sixth-generation (6G) networks transforms wireless infrastructure into a cognitive substrate supporting Vehicle-to-Everything (V2X), Industrial IoT (IIoT), and Integrated Sensing and Communication (ISAC).
By Anthony Kiggundu, Michael Zentarra, Christoph Lipps, Hans D. Schotten
The paper presents a unified framework for proactive service agents, defining proactivity as an agent’s ability to infer service opportunities from incomplete signals and decide whether to remain silent, ask, assist, or act. It models this as a partially observable sequential decision process constrained by authorization and risk, integrating timing, content, and delivery into a single structured action. The authors categorize existing methods along a decision pipeline—state and need estimation, intervention gating, action construction, and feedback adaptation—and propose standardized metrics for evaluating triggering, timing, calibration, user burden, safety, and policy value across diverse interaction modalities.
By Yan Tang, Tingyu Cao, Yuanbo Tang, Huaze Tang, Keer Hu
The paper introduces Assurance-Conditioned Continuous Authorization (ACCA), a framework that maintains operational authority for autonomous AI agents by separating evidence validity from contextual applicability. ACCA derives assurance claims from eligible evidence, resolves a multidimensional effective-authority envelope, and uses a typed dependency graph for selective re‑evaluation of authorization‑relevant changes. The authors demonstrate the approach with controlled scenario tests in a trading domain, showing implementation‑level evidence for the method’s effectiveness.
By Mehdi Shajari
arXiv:2608. 08577v1 Announce Type: new Abstract: Fraud operations must allocate events among automatic approval, analyst review, and automatic blocking even though the labels needed to evaluate these actions are selective and delayed.
By Jie Deng (Tongji University, Shanghai, China)
arXiv:2608. 16402v1 Announce Type: new Abstract: Large language model-based agentic frameworks primarily optimize capability: whether an agent can reason, retrieve information, call tools, delegate work, and complete a goal.
By Bhaskar Tripathi, Anurag Kumar, Ramendra Kumar, Bhavesh Gadhe
arXiv:2606. 29073v1 Announce Type: cross Abstract: Model Context Protocol (MCP)-style ecosystems give language-model applications a practical connection layer for tools, resources, prompts, and transports.
By Ting Liu