MIRROR: Cognitive Inner Monologue Between Conversational Turns for Persistent Reflection and Reasoning in Conversational LLMs
This work addresses three critical failure modes of large language models (LLMs) in dialogue: acquiescence bias, neglect of salient information, and inconsistent constraint prioritization. To mitigate these issues, we propose MIRROR, a cognitive architecture comprising two synergistic modules—Thinker (featuring an introspective inner-monologue manager and a cognitive controller) and Talker—that jointly enable cross-turn reflective reasoning, memory integration, and goal-directed inference. MIRROR introduces a human-inspired parallel introspection mechanism, orchestrating modular internal reasoning, persistent cross-turn cognitive state maintenance, and tri-dimensional coordination among goals, reasoning, and memory to drive cognition-informed response generation. Evaluated on the CuRaTe safety benchmark, MIRROR achieves a 156% relative performance improvement over baselines; when integrated across multiple models, it attains a stable overall accuracy exceeding 80%, representing an average gain of 21 percentage points.