{"slug": "decision-models-as-policy-gates-for-coding-agents", "title": "Decision Models as Policy Gates for Coding Agents", "summary": "A November 1, 2026 arXiv paper reports that writing explicit policy boundaries as examples and counterexamples raised recall by 5.5 to 26.1 percentage points across all 23 available decision models tested as runtime classifiers for coding agents, and cut false positives on 21 of them. In the study, which labeled 2,643 synthetic tool calls blind against 28 rules with a sealed holdout, Jev 1.14's recall rose from 89.3% to 98.2% while unnecessary interruptions fell from 30.6% to 4.8% of 252 clean actions, at a median latency of 120 ms and $0.46 per thousand requests. The authors released the dataset, rule texts and harness, and report that on 16 new scenarios written after the initial studies, a follow-up improved recall on 22 of 23 models and reduced unnecessary interruptions on 21.", "body_md": "# Computer Science > Computation and Language\n\n  [Submitted on 1 Nov 2026]\n\n# Title:Decision Models as Policy Gates for Coding Agents: Explicit Policy Boundaries Improve Detection and Reduce False Alarms\n\n[View PDF](https://arxiv.org/pdf/2611.02155)\n\n[HTML (experimental)](https://arxiv.org/html/2611.02155v1)\n\nAbstract:Coding agents can destroy data, leak secrets and message the wrong person faster than a human can review. Could a decision model catch these risks at runtime? We evaluated 22 hosted decision models and two prompted general models as classifiers for a multi-rule policy engine. A synthetic dataset of 2,643 tool calls was labeled blind against 28 rules, with a sealed holdout. With a single shared preamble in both conditions, explicit boundaries written as examples and counterexamples raised recall by 5.5-26.1 percentage points on all 23 available models and reduced false positives on 21. Jev 1.14's recall rose from 89.3% to 98.2%, while unnecessary interruptions fell from 30.6% to 4.8% of 252 clean actions. With examples, its median latency was 120 ms at $0.46 per thousand requests. Strong models retained the recall gains when concrete names were replaced by generic descriptions; three largely retained them when the same boundaries were written as prose. On 16 new scenarios written after the initial studies, a follow-up improved recall on 22 of 23 models and reduced unnecessary interruptions on 21. 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[**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/decision-models-as-policy-gates-for-coding-agents", "canonical_source": "https://arxiv.org/abs/2611.02155", "published_at": "2026-11-03 18:39:12+00:00", "updated_at": "2026-11-03 18:51:30.492315+00:00", "lang": "en", "topics": ["ai-agents", "ai-safety", "ai-research", "large-language-models", "ai-tools"], "entities": ["arXiv", "Jev 1.14"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/decision-models-as-policy-gates-for-coding-agents", "markdown": "https://wpnews.pro/news/decision-models-as-policy-gates-for-coding-agents.md", "text": "https://wpnews.pro/news/decision-models-as-policy-gates-for-coding-agents.txt", "jsonld": "https://wpnews.pro/news/decision-models-as-policy-gates-for-coding-agents.jsonld"}}