Core Viewpoint - Tencent's Hunyuan team has introduced the SEAT adaptive parallel reasoning framework, transforming complex reasoning tasks from a "single-engine airship" into a "multi-engine rocket," enhancing the capabilities of large models in handling intricate reasoning challenges [7][44]. Group 1: SEAT Framework Overview - The SEAT framework integrates both sequential and parallel scaling paradigms, allowing for extensive exploration and deep refinement of reasoning processes [15][43]. - It employs a multi-round parallel reasoning approach, significantly enhancing the model's exploration capabilities by generating multiple independent reasoning paths simultaneously [16][20]. - The framework is designed to be plug-and-play, enabling easy integration with existing large language models without requiring additional training [29][44]. Group 2: Performance Enhancements - Initial experiments show that even with a minimal parallel setup (N=2), the SEAT framework can achieve a remarkable accuracy improvement of +14.1% for a 32B model and +24.5% for a 7B model [28]. - As the number of parallel paths increases (up to N=8), performance continues to improve, demonstrating the framework's powerful exploration capabilities [23]. Group 3: Semantic Entropy as Navigation - The SEAT framework introduces semantic entropy as a self-supervised metric to gauge the consistency of reasoning outputs, acting as a "navigation sensor" to determine when to stop computations [27][32]. - Two navigation strategies are implemented: a predefined threshold approach and an adaptive threshold-free mechanism, both aimed at optimizing the reasoning process [35][36]. Group 4: Safety Mechanisms - The SEAT framework includes a safety mechanism to prevent "semantic entropy collapse," which can lead to overconfidence and erroneous outputs in smaller models [38][40]. - By monitoring semantic entropy, the framework can issue stop commands before the model's performance deteriorates, ensuring stable reasoning outcomes [40][44].