据媒体2月27日报道，在业界对新一代旗舰模型DeepSeek V4的翘首期盼中，DeepSeek团队却悄然放出了一篇新的学术论 文。 这篇论文由DeepSeek联合北大、清华共同撰写，将研究方向投向了决定大模型实际应用落地的关键一环——推理速度，为 日益复杂的AI智能体，提供一套高效的底层系统解决方案。 具体来说，新论文介绍了一个名为DualPath的创新推理系统，专门针对智能体工作负载下的大模型（LLM）推理性能进行 优化。通过引入"双路径读取KV-Cache（类似记忆缓存）"机制，重新分配存储网络负载，将离线推理吞吐量最高提升 1.87 倍，在线服务的每秒智能体运行数平均提升 1.96 倍。 论文在引言部分提到，大模型正从单轮对话机器人和独立推理模型，快速演进为智能体系统 ——能够自主规划、调用工 具，并通过多轮交互解决实际任务。这种应用范式的转变，推动大模型推理工作负载发生重大变革：从传统的人类-大模型 交互，转向人类-大模型-环境交互，交互轮次可达数十甚至数百轮。 面对传闻，DeepSeek依旧保持其一贯的沉默，目前并未进行任何回应。 此前，DeepSeek被大量用户吐槽风格突变，"变冷淡"，从原本细 ...

Core Viewpoint - The DeepSeek team has released a new academic paper focusing on optimizing inference speed for large language models (LLMs), which is crucial for the practical application of AI agents [4][5]. Group 1: Research and Innovation - The paper, co-authored with Peking University and Tsinghua University, introduces an innovative inference system called DualPath, designed to enhance the performance of LLMs under agent workloads [4]. - The DualPath system employs a "dual-path reading KV-Cache" mechanism, redistributing storage network load, resulting in an offline inference throughput increase of 1.87 times and an average increase of 1.96 times in the number of agent operations per second for online services [4][5]. Group 2: Industry Context and Expectations - The introduction of DualPath addresses the significant changes in inference workloads as LLMs evolve from simple dialogue systems to complex agent systems capable of multi-turn interactions, which can reach dozens or even hundreds of rounds [4]. - There is a growing expectation for the release of DeepSeek's next flagship model, DeepSeek V4, with various rumors about its launch timeline ranging from early February to March [6]. - Recent leaks suggest that DeepSeek is testing a V4 Lite model, codenamed "Sealion-lite," which supports a context window of 1 million tokens and native multimodal inference [6]. Group 3: Market Reactions and Concerns - Despite the technical advancements presented in the paper, there is a sentiment in the industry that such optimizations are seen as a necessity due to GPU shortages, with some viewing it as "dirty work" rather than innovative [5]. - Concerns have been raised among investment institutions that the release of the new model could lead to significant market volatility, similar to the previous year's model launch [6].