OpenAI-Responses

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                    "text": "\n用户现在需要把这篇论文的内容按段落分，输出JSON，每个段落有type和content对吧。首先先逐个梳理：\n首先第一个是标题，1 Introduction，type是\"heading\"，content是\"1 Introduction\"对吧？\n然后接下来第一个正文段落：\"Diffusion models [3–5] learn to reverse a process that incrementally corrupts data with noise, effectively decomposing a complex distribution into a hierarchy of simplified representations. This coarse-to-fine generative approach has proven remarkably successful across a wide range of applications, including image and video synthesis [6] as well as solving complex challenges in natural sciences [7].\" 这个type是\"paragraph\"。\n然后下一个正文段落：\"However, translating this success to the discrete domain of natural language presents critical challenges. The primary difficulty stems from the fact that standard diffusion process is naturally defined over continuous state spaces, thus not directly applicable to discrete domains such as natural language. To bridge this gap, many efforts have focused on novel adaptations, ranging from projecting discrete tokens into a continuous latent space (e.g., embeddings or a simplex) where diffusion can be applied [8–10], to constructing the diffusion process directly over discrete-state space by defining explicit state transition matrices [11–14]. Recent discrete-state approaches have demonstrated scalability and effectiveness with advanced architectures and training recipes [15].\" 也是paragraph。\n接下来下一段：\"Despite impressive progress, real-world deployment of discrete diffusion models for language is still hampered by two key challenges:\" paragraph对吧。\n然后是两个列表项对吧？第一个列表项：\"Inductive bias on token-order modeling. The usage of discrete diffusion for modeling and generating tokens in arbitrary orders is theoretically powerful and appealing [13, 14]; however, natural language is overwhelmingly processed in a sequential order. A purely random-order learning signal can in consequence be inefficient, or even detrimental for language modeling, dampening model performance.\" type是\"list_item\"。\n第二个列表项：\"Inference inefficiency. Although diffusion models are non-autoregressive, their iterative step-sensitive denoising procedure introduces severe latency, which undermines their major advantage over traditional autoregressive models, acting as a cumbersome bottleneck in practice.\" 也是list_item。\n然后下一段正文：\"In this work, we introduce Seed Diffusion Preview, a code-focused language model designed to achieve an elegant balance between speed and quality. Tackling these challenges directly, our model achieves a remarkable speed of 2146 tokens/second on H20 GPUs while maintaining competitive performance against similarly-sized standard language models across a diverse set of code evaluation benchmarks, establishing new state of the art on the speed-quality Pareto frontier.\" paragraph。\n然后下一个标题：\"2 Related Work\" type是heading。\n接下来正文：\"Non-autoregressive (NAR) models have long been considered an alternative to sequential decoding, valued for their potential of parallel inference. In the pre-LLM era, many early NAR methods demonstrated strong performance on specific tasks such as machine translation [16–18]. However, these approaches often lacked a rigorous theoretical foundation for density estimation, which limited their viability as general-purpose probabilistic language models.\" paragraph。\n下一段正文：\"Discrete diffusion models [11–14, 19] have emerged to close this gap. By optimizing the Evidence Lower Bound (ELBO), they provide a principled probabilistic framework for language modeling. The recent success of large-scale systems such as Mercury Coder [1] and Gemini Diffusion [2] is particularly notable. These models show that it is possible to narrow the quality gap with autoregressive systems while offering substantial speedup, thereby challenging the conventional wisdom on \"quality-speed trade-off\", raising new interest in NAR in the modern LLM era.\" paragraph。\n然后下一个标题：\"3 Seed Diffusion\" type heading。\n接下来正文：\"As the first experimental model in our Seed Diffusion series, Seed Diffusion Preview is specifically focused on code generation, thus adopting the data pipelines (code/code-related data only) and processing methodology of the open-sourced Seed Coder project [20]. The architecture is a standard dense Transformer, and we intentionally omit complex components such as LongCoT reasoning in this initial version to first establish a strong and efficient performance baseline. This section introduces its key components and training strategies.\" paragraph。\n最后还有页码？对，页码2，type是\"page_number\"，content是\"2\"。\n现在把这些整理成JSON数组对吧？每个元素是{\"type\": \"...\", \"content\": \"...\"}。要注意转义引号哦，比如里面的双引号要转义，还有换行啥的不要留，内容要准确。\n等下检查有没有漏的？对，每个部分都对吗？再核对原文：\n哦对，列表项前面的黑点，所以是list_item没错。然后标题都是一级标题？这里的1、2、3都是一级heading对吧。然后页码在最下面，是page_number。\n现在整理成正确的JSON格式：\n首先是数组：\n[\n  {\"type\": \"heading\", \"content\": \"1 Introduction\"},\n  {\"type\": \"paragraph\", \"content\": \"Diffusion models [3–5] learn to reverse a process that incrementally corrupts data with noise, effectively decomposing a complex distribution into a hierarchy of simplified representations. This coarse-to-fine generative approach has proven remarkably successful across a wide range of applications, including image and video synthesis [6] as well as solving complex challenges in natural sciences [7].\"},\n  {\"type\": \"paragraph\", \"content\": \"However, translating this success to the discrete domain of natural language presents critical challenges. The primary difficulty stems from the fact that standard diffusion process is naturally defined over continuous state spaces, thus not directly applicable to discrete domains such as natural language. To bridge this gap, many efforts have focused on novel adaptations, ranging from projecting discrete tokens into a continuous latent space (e.g., embeddings or a simplex) where diffusion can be applied [8–10], to constructing the diffusion process directly over discrete-state space by defining explicit state transition matrices [11–14]. Recent discrete-state approaches have demonstrated scalability and effectiveness with advanced architectures and training recipes [15].\"},\n  {\"type\": \"paragraph\", \"content\": \"Despite impressive progress, real-world deployment of discrete diffusion models for language is still hampered by two key challenges:\"},\n  {\"type\": \"list_item\", \"content\": \"Inductive bias on token-order modeling. The usage of discrete diffusion for modeling and generating tokens in arbitrary orders is theoretically powerful and appealing [13, 14]; however, natural language is overwhelmingly processed in a sequential order. A purely random-order learning signal can in consequence be inefficient, or even detrimental for language modeling, dampening model performance.\"},\n  {\"type\": \"list_item\", \"content\": \"Inference inefficiency. Although diffusion models are non-autoregressive, their iterative step-sensitive denoising procedure introduces severe latency, which undermines their major advantage over traditional autoregressive models, acting as a cumbersome bottleneck in practice.\"},\n  {\"type\": \"paragraph\", \"content\": \"In this work, we introduce Seed Diffusion Preview, a code-focused language model designed to achieve an elegant balance between speed and quality. Tackling these challenges directly, our model achieves a remarkable speed of 2146 tokens/second on H20 GPUs while maintaining competitive performance against similarly-sized standard language models across a diverse set of code evaluation benchmarks, establishing new state of the art on the speed-quality Pareto frontier.\"},\n  {\"type\": \"heading\", \"content\": \"2 Related Work\"},\n  {\"type\": \"paragraph\", \"content\": \"Non-autoregressive (NAR) models have long been considered an alternative to sequential decoding, valued for their potential of parallel inference. In the pre-LLM era, many early NAR methods demonstrated strong performance on specific tasks such as machine translation [16–18]. However, these approaches often lacked a rigorous theoretical foundation for density estimation, which limited their viability as general-purpose probabilistic language models.\"},\n  {\"type\": \"paragraph\", \"content\": \"Discrete diffusion models [11–14, 19] have emerged to close this gap. By optimizing the Evidence Lower Bound (ELBO), they provide a principled probabilistic framework for language modeling. The recent success of large-scale systems such as Mercury Coder [1] and Gemini Diffusion [2] is particularly notable. These models show that it is possible to narrow the quality gap with autoregressive systems while offering substantial speedup, thereby challenging the conventional wisdom on \\\"quality-speed trade-off\\\", raising new interest in NAR in the modern LLM era.\"},\n  {\"type\": \"heading\", \"content\": \"3 Seed Diffusion\"},\n  {\"type\": \"paragraph\", \"content\": \"As the first experimental model in our Seed Diffusion series, Seed Diffusion Preview is specifically focused on code generation, thus adopting the data pipelines (code/code-related data only) and processing methodology of the open-sourced Seed Coder project [20]. The architecture is a standard dense Transformer, and we intentionally omit complex components such as LongCoT reasoning in this initial version to first establish a strong and efficient performance baseline. This section introduces its key components and training strategies.\"},\n  {\"type\": \"page_number\", \"content\": \"2\"}\n]\n对，那个\"quality-speed trade-off\"里面的双引号要转义，刚才已经转了对吧。检查有没有错误，内容和原文一致吗？对的，没错。这样就可以了。"
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To bridge this gap, many efforts have focused on novel adaptations, ranging from projecting discrete tokens into a continuous latent space (e.g., embeddings or a simplex) where diffusion can be applied [8–10], to constructing the diffusion process directly over discrete-state space by defining explicit state transition matrices [11–14]. Recent discrete-state approaches have demonstrated scalability and effectiveness with advanced architectures and training recipes [15].\"\n    },\n    {\n        \"type\": \"paragraph\",\n        \"content\": \"Despite impressive progress, real-world deployment of discrete diffusion models for language is still hampered by two key challenges:\"\n    },\n    {\n        \"type\": \"list_item\",\n        \"content\": \"Inductive bias on token-order modeling. The usage of discrete diffusion for modeling and generating tokens in arbitrary orders is theoretically powerful and appealing [13, 14]; however, natural language is overwhelmingly processed in a sequential order. A purely random-order learning signal can in consequence be inefficient, or even detrimental for language modeling, dampening model performance.\"\n    },\n    {\n        \"type\": \"list_item\",\n        \"content\": \"Inference inefficiency. Although diffusion models are non-autoregressive, their iterative step-sensitive denoising procedure introduces severe latency, which undermines their major advantage over traditional autoregressive models, acting as a cumbersome bottleneck in practice.\"\n    },\n    {\n        \"type\": \"paragraph\",\n        \"content\": \"In this work, we introduce Seed Diffusion Preview, a code-focused language model designed to achieve an elegant balance between speed and quality. Tackling these challenges directly, our model achieves a remarkable speed of 2146 tokens/second on H20 GPUs while maintaining competitive performance against similarly-sized standard language models across a diverse set of code evaluation benchmarks, establishing new state of the art on the speed-quality Pareto frontier.\"\n    },\n    {\n        \"type\": \"heading\",\n        \"content\": \"2 Related Work\"\n    },\n    {\n        \"type\": \"paragraph\",\n        \"content\": \"Non-autoregressive (NAR) models have long been considered an alternative to sequential decoding, valued for their potential of parallel inference. In the pre-LLM era, many early NAR methods demonstrated strong performance on specific tasks such as machine translation [16–18]. However, these approaches often lacked a rigorous theoretical foundation for density estimation, which limited their viability as general-purpose probabilistic language models.\"\n    },\n    {\n        \"type\": \"paragraph\",\n        \"content\": \"Discrete diffusion models [11–14, 19] have emerged to close this gap. By optimizing the Evidence Lower Bound (ELBO), they provide a principled probabilistic framework for language modeling. The recent success of large-scale systems such as Mercury Coder [1] and Gemini Diffusion [2] is particularly notable. These models show that it is possible to narrow the quality gap with autoregressive systems while offering substantial speedup, thereby challenging the conventional wisdom on \\\"quality-speed trade-off\\\", raising new interest in NAR in the modern LLM era.\"\n    },\n    {\n        \"type\": \"heading\",\n        \"content\": \"3 Seed Diffusion\"\n    },\n    {\n        \"type\": \"paragraph\",\n        \"content\": \"As the first experimental model in our Seed Diffusion series, Seed Diffusion Preview is specifically focused on code generation, thus adopting the data pipelines (code/code-related data only) and processing methodology of the open-sourced Seed Coder project [20]. The architecture is a standard dense Transformer, and we intentionally omit complex components such as LongCoT reasoning in this initial version to first establish a strong and efficient performance baseline. This section introduces its key components and training strategies.\"\n    },\n    {\n        \"type\": \"page_number\",\n        \"content\": \"2\"\n    }\n]"
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