arXiv Machine Learning

SABLE: An NDA-Safe Closed-Loop LLM Framework for Analog Circuit Optimization in Industrial EDA Flows

arXiv:2607. 03701v1 Announce Type: cross Abstract: Large language models (LLMs) can propose circuit-optimization decisions, but industrial analog flows cannot expose foundry PDK content, proprietary schematics, absolute simulation paths, or license-bound tool state to a cloud endpoint.

arXiv AI
Sep 25

Stale Does Not Mean Unsafe: Guard Precision for Tool-Using LLM Agents under Infrastructure State Races

The paper investigates how tool‑using language‑model agents can safely commit changes to infrastructure when external state may change between read and commit. By distinguishing invalidating races from predicate‑preserving and irrelevant ones, the authors evaluate three commit‑time guard granularities—global epoch, read‑set version, and semantic commit predicate—using a deterministic simulator and three quantized model families. The study finds that only the complete predicate guard consistently eliminates unsafe commits, while freshness‑based guards block a large proportion of benign races and model‑side signals fail to replace precise semantic enforcement.

By Zihao Zheng, Jiayu Long, Baichuan Li, Junyi Yao
arXiv AI
Sep 17

PentestChain: A Cost-Aware, MCP-Orchestrated Framework for Automated Penetration Testing with Free-Tier LLMs

PentestChain is a ten‑phase automated penetration testing framework that uses a cost‑aware AI cascade, starting with a local 7B‑parameter Ollama model (qwen2.5‑7b) and then free‑tier OpenRouter and Cerebras models, with a rule‑based fallback. It exposes the entire pipeline through a Model Context Protocol (MCP) server that includes eleven tools. The authors evaluate the framework using standard testbeds (AutoPenBench, Cybench subset, PentestGPT 182‑sub‑task benchmark) and report that the local model keeps paid‑API cost at zero while detecting 26 services and enriching 34 CVEs on legacy targets.

By Rushabh Vipulkumar Patel, Dipo Dunsin, Mohammed Almaiah, Mohamed Chahine Ghanem
arXiv Machine Learning
Jul 1

Certified Speculative Execution for Untrusted AI Agents

arXiv:2606. 31023v1 Announce Type: cross Abstract: Hard-constrained sequential decision systems have no certified way to spend the test-time compute of modern AI: executing the multi-step drafts of a learned policy or a frozen LLM forfeits the feasibility guarantee a trusted solver provides, while invoking the solver at every step forfeits the speed the AI offers.

By Chenyu Zhou, Qiliang Jiang, Shuning Wu, Xu Zhou
arXiv AI
Sep 2

Analog-DB: An Agent-First Analog Integrated Circuit Database, From Blocks to Systems

Analog-DB is an open‑source, versioned database that stores analog integrated circuit designs in a process‑neutral topology, reusable testbenches, and machine‑readable datasheets using a domain‑specific language. The database’s parameterization scheme and schema‑governed contracts enable AI design agents to discover, compose, and retarget circuits across multiple process kits. In a case study, a coding agent used the released artifacts to size op‑amp cores for a chopper instrumentation amplifier, uncovering defects that a sizing‑only baseline missed.

By Danial Noori Zadeh, Mohamed B. Elamien
arXiv Machine Learning
Jun 9

AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation

arXiv:2606. 08173v1 Announce Type: cross Abstract: In sixth-generation (6G) networks, billions of cyber-physical systems (CPSs) - autonomous vehicles, smart grids, industrial robots, and remote-surgical equipment - will run over ultra-reliable low-latency slices, collapsing the gap between a remote breach and physical harm to milliseconds, a budget perimeter firewalls and centralised security operations centres cannot meet.

By Bilal Hussain, Muhammad Bilal, Tan Li, Haris Pervaiz, Xiao Tang, Qinghe Du, Fawad Ahmad, Muhammad Azhar, Jun Zhang