【专题研究】LLMs used是当前备受关注的重要议题。本报告综合多方权威数据，深入剖析行业现状与未来走向。
现在他们利用vibe code的能力，就可以针对特定用例去扩展和高度定制应用程序。比如我想要一个为迈阿密Miami团队开发的会议室预订App，由于迈阿密有一些奇怪的HR政策，所以那个供20人使用的App需要随时查看Workday以及其他各种系统。过去我肯定负担不起让内部团队投入IT资源构建它的成本，因为账单金额会太高，但现在我也许可以轻松构建它。这个App在底层使用了Workday在全球的数据和规则，但它给了我一个非常定制化的interface，去为迈阿密前台完成一些非常针对他们需求的特定工作。这非常强大，但它并不能完全取代人类的工作。
。关于这个话题，新收录的资料提供了深入分析
除此之外，业内人士还指出，Follow topics & set alerts with myFT
多家研究机构的独立调查数据交叉验证显示，行业整体规模正以年均15%以上的速度稳步扩张。
。新收录的资料对此有专业解读
进一步分析发现，You can download and play Chromatron here.
与此同时，Abstract:Humans shift between different personas depending on social context. Large Language Models (LLMs) demonstrate a similar flexibility in adopting different personas and behaviors. Existing approaches, however, typically adapt such behavior through external knowledge such as prompting, retrieval-augmented generation (RAG), or fine-tuning. We ask: do LLMs really need external context or parameters to adapt to different behaviors, or do they already have such knowledge embedded in their parameters? In this work, we show that LLMs already contain persona-specialized subnetworks in their parameter space. Using small calibration datasets, we identify distinct activation signatures associated with different personas. Guided by these statistics, we develop a masking strategy that isolates lightweight persona subnetworks. Building on the findings, we further discuss: how can we discover opposing subnetwork from the model that lead to binary-opposing personas, such as introvert-extrovert? To further enhance separation in binary opposition scenarios, we introduce a contrastive pruning strategy that identifies parameters responsible for the statistical divergence between opposing personas. Our method is entirely training-free and relies solely on the language model's existing parameter space. Across diverse evaluation settings, the resulting subnetworks exhibit significantly stronger persona alignment than baselines that require external knowledge while being more efficient. Our findings suggest that diverse human-like behaviors are not merely induced in LLMs, but are already embedded in their parameter space, pointing toward a new perspective on controllable and interpretable personalization in large language models.。关于这个话题，新收录的资料提供了深入分析
值得注意的是，其次是基础设施和生态的成熟，包括LangChain、AutoGPT等开源框架经过两年的迭代，已经形成了一套标准化的开发范式，极大地缩短了开发周期；Dify、Coze（扣子）等低代码/无代码平台的普及，让不懂代码的业务人员也能通过拖拉拽快速生成一个专用智能体；值得一提的是2025年Anthropic发布的MCP（模型上下文协议）和skills（技能系统）给智能体生态提供了重要的标准和启发：MCP作为一个开源协议标准，令大模型与外部数据源或工具之间的交互更统一、便捷，Skills则是把人类设计的完成某类任务所需的能力/工作流打包起来，让Agent在这类任务上可以更稳定的工作，虽然技术含量不高，但在当下有很强的实用性。
面对LLMs used带来的机遇与挑战，业内专家普遍建议采取审慎而积极的应对策略。本文的分析仅供参考，具体决策请结合实际情况进行综合判断。