arXiv AI June 26, 2026 By Adam Mondl, Matthew Maisel, John H. Brock

Autoformalization of Agent Instructions into Policy-as-Code

arXiv:2606. 26649v1 Announce Type: new Abstract: Agent safety in high-stakes domains requires formal policy enforcement, but most existing approaches either rely on probabilistic guardrails (fine-tuned classifiers, prompt-based steering) that offer no formal guarantees, or on hand-coded symbolic enforcement that does not scale to the breadth of real policy specifications.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

1d ago

Toward Safe LLM Agents: A Survey of Specification, Verification, and Enforcement

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.

By Pierre Dantas, Lucas Cordeiro, Ehsan Nowroozi, Tihanyi Norbert

llms agents safety

arXiv AI

Jul 7

AutoCedar: An Agentic Framework for Verifier-Guided Access Control Policy Synthesis

arXiv:2607. 03656v1 Announce Type: cross Abstract: Large Language Models are increasingly used to turn natural-language requirements into code.

By Adarsh Vatsa, Sachi Shome, Yingming Zhou, William Eiers

llms agents benchmarks

arXiv AI

Jul 7

AgentLTL: A Trace-Verification Framework for Measuring, Enforcing, and Training Procedural Compliance in Tool-Using LLM Agents

arXiv:2607. 02599v1 Announce Type: cross Abstract: Tool-using LLM agents are usually evaluated by final-answer correctness or LLM judges.

By La\"ila Elkoussy (LRE, EPITA), Julien Perez (LRE)

llms agents fine-tuning benchmarks safety

arXiv Machine Learning

Jun 19

AgentArmor: A Framework, Evaluation, \& Mitigation of Coding Agent Failures

arXiv:2606. 19380v1 Announce Type: cross Abstract: Software engineering and deployment are increasingly being delegated to AI coding agents.

By Kenneth Ge, Andre Assis

agents safety

Hugging Face Trending Papers

Jun 24

PolicyAlign: Direct Policy-Based Safety Alignment for Large Language Models

Safety alignment of large language models (LLMs) typically depends on high-quality supervision data, such as safe demonstrations or preference pairs. However, in real-world deployment, emerging safety requirements are often specified as natural-language policies, while corresponding supervision data may be costly, delayed, or unavailable.

llms efficiency safety

arXiv Machine Learning

Jun 17

Clarify Before You Draw: Proactive Agents for Robust Text-to-CAD Generation

arXiv:2602. 03045v2 Announce Type: replace Abstract: Large language models have recently enabled text-to-CAD systems that synthesize parametric CAD programs (e.

By Bo Yuan, Zelin Zhao, Petr Molodyk, Bin Hu, Yongxin Chen

llms agents fine-tuning

Related stories

Toward Safe LLM Agents: A Survey of Specification, Verification, and Enforcement

AutoCedar: An Agentic Framework for Verifier-Guided Access Control Policy Synthesis

AgentLTL: A Trace-Verification Framework for Measuring, Enforcing, and Training Procedural Compliance in Tool-Using LLM Agents

AgentArmor: A Framework, Evaluation, \& Mitigation of Coding Agent Failures

PolicyAlign: Direct Policy-Based Safety Alignment for Large Language Models

Clarify Before You Draw: Proactive Agents for Robust Text-to-CAD Generation