PersonaForge: Psychology-Grounded Dual-Process Architecture for Personality-Consistent Role-Playing Agents

Large Language Models excel at role-playing but struggle to maintain consistent personalities across extended multi-turn interactions. We propose PersonaForge, combining (1) a three-layer personality architecture grounded in psychological theory and (2) a dual-process generation mechanism inspired by cognitive science. We test two falsifiable claims: Claim 1 (Orthogonality): Psychology-grounded dimensions (Big Five + Defense Mechanisms) provide more orthogonal constraints than natural language descriptions, reducing long-dialogue drift. Claim 2 (Integration Necessity): High-dimensional personality constraints create "production interference" requiring a cognitive workspace (Inner Monologue) to resolve-removing it degrades performance below simpler baselines. Experiments on 88 characters demonstrate: (1) +19.4% personality consistency (PC) over the Structured-CoT baseline, with human correlation r = 0.82, (2) reduced drift over 50-turn conversations (6.3% vs. 24.8% baseline), and (3) +64.7% defense mechanism manifestation. External validation on RoleBench confirms generalization (73.2% win-rate, drift 8.4% vs. 20.4%). Selective dual-process activation achieves 96% of fullsystem performance with only 13.4% token overhead. Human evaluation confirms more authentic and psychologically coherent character behaviors. Code and data: https:// github.com/fQwQf/PersonaForge.