[
    {
        "id": "osp-22431",
        "type": "article-journal",
        "title": "SecJev: Bringing Security Expertise to System One Decision Models",
        "author": [
            {
                "family": "Chen",
                "given": "Zheng"
            },
            {
                "family": "Yu",
                "given": "Fei"
            },
            {
                "family": "Huang",
                "given": "Haohao"
            },
            {
                "family": "Li",
                "given": "Yang"
            },
            {
                "family": "Chen",
                "given": "Anlong"
            },
            {
                "family": "Chen",
                "given": "Lei"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/secjev-bringing-security-expertise-to-system-one-decision-models",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Security workflows need models that turn complex observations and explicit policies into decisions. System One models introduced by Jev return typed predictions and probabilities; security specialization supplies the domain expertise behind those predictions. We introduce SecJev, to our knowledge the first family of Jev-like decision models specialized for security, spanning 0.8B to 9B parameters. Built on Kev's single-pass candidate scorer, SecJev learns Boolean, choice, and ordered decisions from text, telemetry, and observation histories. We develop SecJev-Corpus to unify source-label prediction and explicit-policy evaluation across 14 tasks and eight sources. It covers tool outputs, traffic, federated updates, consensus, authentication, and vehicle messages. Scene-weighted training adapts the models across these domains while preserving a shared typed decision interface. Security specialization improves every model in the family; SecJev-0.8B outperforms general Kev-9B by 20.51 percentage points in task-macro accuracy. Comparisons with answer-only generative fine-tuning show close accuracy and latency with lower peak inference memory. Tests on new source groups reproduce gains over Kev in prompt-injection and traffic decisions, with capture-dependent false alarms. We release adapters, decision heads, SecJev-Corpus, and training and inference code."
    }
]