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Overview
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<a href="/2026/06/16/Agent-Engineering-Radar-2026-06-16/" class="post-title-link" itemprop="url">【Agent Engineering Radar】2026-06-16 Agent 工程技术雷达</a>
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<time title="Created: 2026-06-16 09:00:00" itemprop="dateCreated datePublished" datetime="2026-06-16T09:00:00+08:00">2026-06-16</time>
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<time title="Modified: 2026-06-18 21:24:39" itemprop="dateModified" datetime="2026-06-18T21:24:39+08:00">2026-06-18</time>
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<p>本期信号很明确：agent 技术栈的竞争正在从“哪个框架更会跑 demo”，转向“哪个框架更容易被实现、验证、观测和约束”。公开评测、trace 规范、MCP 工具连接和 skills 基准都指向同一个结论：先建设吸收机制，再选择框架。</p>
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<link itemprop="mainEntityOfPage" href="https://alanfangblog.com/2026/06/11/Speech-Dialog-Data-Synthesis-Quality-Gates/">
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<a href="/2026/06/11/Speech-Dialog-Data-Synthesis-Quality-Gates/" class="post-title-link" itemprop="url">语音对话合成数据工程：Schema、口语化与质量闸门</a>
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<time title="Created: 2026-06-11 14:30:00" itemprop="dateCreated datePublished" datetime="2026-06-11T14:30:00+08:00">2026-06-11</time>
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<time title="Modified: 2026-06-18 19:17:15" itemprop="dateModified" datetime="2026-06-18T19:17:15+08:00">2026-06-18</time>
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<p>合成对话数据的风险不是“生成得不够多”，而是生成得太像模板、太干净、太不符合语音输入的真实噪声。数量扩大很容易，难的是让样本既覆盖任务，又不把模型训练成只会回答标准文本。</p>
<p>所以语音对话合成数据要先设计质量闸门，再谈规模。</p>
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<a href="/2026/06/10/Speech-Batch-Consistency-Debugging/" class="post-title-link" itemprop="url">音频模型 Batch 一致性排查：从有效输入区间到逐层 diff</a>
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<p>同一个音频，batch size 不同却输出不同，这是音频模型排障里很典型的问题。它通常不是“模型随机性”一句话能解释的，而是 padding、mask、dtype、subsampling、归一化或缓存边界出了问题。</p>
<p>排查这类问题，关键是把有效输入区间和逐层差异记录下来。</p>
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<a href="/2026/06/10/Realtime-Speech-Turn-Taking-Evaluation/" class="post-title-link" itemprop="url">实时语音 Turn-taking 评测：从端点检测到可接话判断</a>
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<p>实时语音系统里，判断用户“说完了”比看起来更难。静音不一定代表结束，短停顿可能只是思考；语义已经完整，也可能还会补充条件。Turn-taking 要解决的不是单纯端点检测，而是系统什么时候可以安全接话。</p>
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<a href="/2026/06/10/Agentic-Coding-Governance/" class="post-title-link" itemprop="url">Agentic Coding 工程治理：多模型协作先定义责任边界</a>
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<p>代码生成工具越来越强，多模型协作也越来越常见。但真正的问题已经不是“哪个模型会写代码”，而是多个代理如何共享上下文、谁能写文件、谁负责审查、如何避免互相覆盖，以及如何验证最终结果。</p>
<p>Agentic Coding 的难点更像工程治理，而不是单纯模型能力。</p>
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<a href="/2026/06/10/ASR-Noise-Structured-Extraction-Evaluation/" class="post-title-link" itemprop="url">ASR 噪声下的结构化抽取评测：从文本相似到实体归因</a>
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<p>ASR 结果用于结构化抽取时，评测不能只看整句文本相似度。真实系统关心的是关键实体有没有抽对、位置是否合理、错误来自识别还是 NER、以及噪声会不会把下游结构化结果带偏。</p>
<p>因此，ASR + NER 的评测要从“文本像不像”转到“实体是否可归因”。</p>
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<a href="/2026/06/10/LLM-Speech-Inference-Serving-Observability/" class="post-title-link" itemprop="url">LLM 与语音模型推理服务：先把延迟拆成可观测链路</a>
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<p>推理服务的问题经常被简化成“换更快的框架”。vLLM、SGLang、Triton 都很重要，但如果系统不能解释一次请求的延迟来自排队、预填充、解码、音频前端、网络还是后处理，换框架只是碰运气。</p>
<p>语音和 LLM 结合后，延迟问题更复杂：音频切片、流式 partial、模型队列、token 生成和系统超时会叠在一起。</p>
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<a href="/2026/06/10/PEFT-Engineering-Tradeoffs/" class="post-title-link" itemprop="url">PEFT 工程取舍：省参数只是入口，部署路径才是边界</a>
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<p>PEFT 常被理解成“少训练一些参数”。这句话没错，但它只说了训练成本，没有说清楚工程边界。真正的选择不是参数越少越好，而是训练显存、适配能力、推理开销、上下文占用、版本管理和回滚方式之间的取舍。</p>
<p>如果不把这些约束写清楚，LoRA、Prefix-Tuning、P-Tuning、QLoRA 很容易被比较成一张简单榜单，而不是可复用的工程组件。</p>
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<a href="/2026/06/10/ASR-Data-Quality-Pipeline-Open-Source/" class="post-title-link" itemprop="url">ASR 数据质量流水线：伪标签、切分与可追溯评测</a>
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<p>ASR 数据工程最容易被低估。模型指标波动时，很多人先调模型结构，但真正的问题常常在数据：切分不稳、伪标签质量不清、噪声样本混入、热词和实体覆盖不足、评测集不可追溯。</p>
<p>一条可用的数据质量流水线，核心不是“多收一点音频”，而是让每个样本都能解释来源、标签、置信度和进入训练集的理由。</p>
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<a href="/2026/06/10/Speech-LLM-Audio-Token-Alignment/" class="post-title-link" itemprop="url">语音大模型工程：音频 token、LLM 主干与对齐契约</a>
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