DPAgent is an agentic framework that detects 90.98% of AI-groomed deceptive samples and repairs 77% of deceptive interfaces while exploring 80% of pattern types with 10% of baseline page visits.
Don’t detect, just correct: Can llms defuse deceptive patterns directly?
2 Pith papers cite this work. Polarity classification is still indexing.
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Users adjust AI agent personalities differently by task context, forming distinct profiles that increase perceived anthropomorphism, autonomy, and trust.
citing papers explorer
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DPAgent-in-the-Middle: Agentic Defense and Repair Against AI-Groomed Deceptive Patterns
DPAgent is an agentic framework that detects 90.98% of AI-groomed deceptive samples and repairs 77% of deceptive interfaces while exploring 80% of pattern types with 10% of baseline page visits.
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From Fixed to Flexible: Shaping AI Personality in Context-Sensitive Interaction
Users adjust AI agent personalities differently by task context, forming distinct profiles that increase perceived anthropomorphism, autonomy, and trust.