Core Viewpoint - The article discusses the challenges of generating ultra-long texts in the era of complex large models and introduces TokenSwift, a new inference acceleration framework that significantly improves efficiency while maintaining output quality [1][27][29]. Group 1: Challenges in Long Text Generation - Traditional autoregressive methods generate one token at a time, leading to performance degradation as sequence lengths increase to 100,000 tokens or more [4][5]. - The main bottlenecks include model redundancy, KV cache inflation, and semantic repetition, which hinder the efficiency and diversity of generated outputs [9][19]. Group 2: TokenSwift Framework - TokenSwift proposes a lightweight and efficient framework that restructures traditional autoregressive inference by introducing a mechanism based on multi-token drafting, parallel validation, and dynamic cache updates [7][11]. - The framework allows for the parallel generation of multiple candidate tokens, significantly reducing model reload frequency and I/O time while ensuring semantic relevance [12][17]. Group 3: Key Technical Innovations - The n-gram heuristic completion mechanism utilizes historical fragments to enhance the accuracy of token drafting, ensuring high semantic relevance [14]. - A tree-structured parallel validation module assesses the drafted tokens against standard autoregressive paths, ensuring lossless output quality [15][17]. - Dynamic KV management and repetition penalties are implemented to mitigate cache inflation and enhance output diversity, respectively [19][26]. Group 4: Performance Evaluation - Extensive experiments on various mainstream models demonstrate that TokenSwift achieves acceleration ratios exceeding 3 times while maintaining output quality consistent with original models [21][22]. - The acceleration effect becomes more pronounced with longer sequences, reducing generation time for 100K token tasks from nearly 5 hours to 1.5 hours [22]. Group 5: Conclusion and Future Implications - TokenSwift is not a new model but a universal acceleration strategy that can be integrated into existing models like LLaMA and Qwen, offering strong compatibility and deployment convenience [28]. - The framework's lossless guarantee for inference quality positions it as a robust technical support for future applications in multi-turn reasoning, code generation, and agent planning [29].