Core Insights - The article discusses the significant performance gap between text-based AI models and voice interaction systems, highlighting that voice systems struggle with reasoning tasks compared to their text counterparts [5][29]. Group 1: Research Findings - The VERA study by Duke University and Adobe systematically measured the impact of voice modality on reasoning ability across 12 mainstream voice systems, using 2,931 specially designed test questions [3][5]. - The most striking finding was that OpenAI's GPT family showed a 68.7 percentage point difference in performance between text and voice models, indicating a stark contrast in reasoning capabilities [5][29]. - The best text model, GPT-5, achieved a 74.8% accuracy on math competition questions, while the voice version, GPT-realtime, only managed 6.1% [6][29]. Group 2: Testing Methodology - The research evaluated voice systems on five dimensions: mathematical reasoning, web information synthesis, graduate-level science questions, long dialogue memory, and factual retrieval [10][14]. - A unique "voice-native" transformation process was employed to ensure that the test questions were suitable for voice interaction, including converting numbers to words and symbols to spoken expressions [17][18]. Group 3: Performance Analysis - The average accuracy for text models was approximately 54%, while voice models averaged around 11.3%, resulting in a 42.7 percentage point gap [32]. - The study identified various error types and failure patterns across different architectures, revealing a collective challenge within the industry [28][26]. Group 4: Underlying Issues - The article outlines three main reasons for the performance gap: irreversible streaming commitment, cognitive resource allocation dilemmas, and erroneous chain reactions [21][22][24]. - The architecture of voice systems inherently limits their ability to perform deep reasoning tasks, as they prioritize fluency over accuracy [21][23]. Group 5: Future Directions - The research emphasizes the need for a fundamental rethinking of how deep reasoning can be integrated into real-time dialogue systems, rather than merely connecting text models to text-to-speech systems [37][39]. - Potential breakthroughs could involve asynchronous architecture innovations, intelligent buffering strategies, editable internal states, and parallel processing of complex tasks [41].