Víctor Mayoral-Vilches, Hugo Osuna, Aitana Cornago · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.30361 · cited by 0 works (OpenAlex) · open-access PDF
ونحن نستعرض ما إذا كان التمثيل العاطفي المنطقي في نموذج الذكاء الاصطناعي يمكن أن يعزز أداء الذكاء الاصطناعي. باستخدام CT0 ، وهو محرك التحويل الذكي 4B الذي تم إنشاؤه بـ alias3-micro ، فإننا نستخرج 171 وجهة نظر الذكاء من تدفقه السلبي دون إعادة تدريب وتقييم التدخلات الإدارية على وظائف التمثيل في 415 حملة. تحديد وجهة نظر الذكاء بشكل ديناميكي أثناء التنفيذ. مع ما يقرب من 80 ميغابايت من قرارات على الفور لكل طلب ، فإن هذا النهج يزيد من الأداء بنسبة 4.2 في المائة نقطة على الفور ، مما يزيد من تأثير الذكاء الاصطناعي ، مما يعتمد بشكل كبير
Machine translation (Meta M2M100); the English original follows.
Human hackers rely on composure, persistence, vigilance, and confidence under pressure. We investigate whether analogous emotion representations in AI models can improve cybersecurity AI performance. Using CT0, a 4B-parameter cybersecurity transformer released as alias3-micro, we extract 171 emotion directions from its residual stream without retraining and evaluate steering interventions on cybench-subtask across 415 campaigns. Steering toward calm, serene, or desperate improves performance by 1.6 to 2.1 percentage points across ten seeds, while other emotions show limited or negative effects. Results depend strongly on steering direction, layer, and intensity. We further introduce a lightweight context-aware router that selects emotion directions dynamically during execution. With approximately 80 ms decision overhead per request, this approach improves performance by 4.2 percentage points across ten seeds, outperforming fixed steering and achieving a positive 95% confidence interval. These findings suggest that emotion representations in language models are measurable, controllable, and useful for adaptive cybersecurity reasoning.
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Topics: Hacker, Computer science, Artificial intelligence, Psychology, Router, Psychological intervention
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