feat: 使用智谱GLM-4-Flash生成中文标题

- 替换 Google Translate 为智谱 GLM-4-Flash API
- LLM 总结论文/项目核心内容生成中文标题
- 标题简洁有力，突出技术亮点
- 简化输出格式：标题 + 链接（无摘要）
- 添加翻译进度显示

cache/seen_urls.json

@@ -1,5 +1,5 @@
 {
-  "lastUpdate": "2026-02-27T15:43:08.320Z",
+  "lastUpdate": "2026-02-27T16:08:50.053Z",
   "urls": [
     "http://arxiv.org/abs/2602.23360v1",
     "http://arxiv.org/abs/2602.23359v1",

daily/2026-02-28_en.md

@@ -0,0 +1,67 @@
+# AI Daily Brief - 2026-02-28
+
+> Collected at: 2/28/2026, 12:08:50 AM
+> Total items: 131
+
+## 🔥 Top 10 Highlights
+
+1. sponsors/muratcankoylan
+   https://github.com/sponsors/muratcankoylan
+
+2. login?return_to=%2Fruvnet%2Fclaude-flow
+   https://github.com/login?return_to=%2Fruvnet%2Fclaude-flow
+
+3. Search More, Think Less: Rethinking Long-Horizon Agentic Search for Efficiency and Generalization
+   https://huggingface.co/papers/2602.22675
+
+4. AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning
+   https://huggingface.co/papers/2602.23258
+
+5. Accelerating Diffusion via Hybrid Data-Pipeline Parallelism Based on Conditional Guidance Scheduling
+   https://huggingface.co/papers/2602.21760
+
+6. Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimization
+   https://huggingface.co/papers/2602.23008
+
+7. login?return_to=%2Fruvnet%2Fwifi-densepose
+   https://github.com/login?return_to=%2Fruvnet%2Fwifi-densepose
+
+8. login?return_to=%2Fbytedance%2Fdeer-flow
+   https://github.com/login?return_to=%2Fbytedance%2Fdeer-flow
+
+9. login?return_to=%2Fmoonshine-ai%2Fmoonshine
+   https://github.com/login?return_to=%2Fmoonshine-ai%2Fmoonshine
+
+10. sponsors/obra
+   https://github.com/sponsors/obra
+
+## 📂 Categories
+
+### Agent Frameworks
+
+- Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks
+  http://arxiv.org/abs/2602.23330v1
+- AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning
+  http://arxiv.org/abs/2602.23258v1
+
+### AI Infrastructure / Inference Optimization
+
+- Bitwise Systolic Array Architecture for Runtime-Reconfigurable Multi-precision Quantized Multiplication on Hardware Accelerators
+  http://arxiv.org/abs/2602.23334v1
+- Invariant Transformation and Resampling based Epistemic-Uncertainty Reduction
+  http://arxiv.org/abs/2602.23315v1
+- Agency and Architectural Limits: Why Optimization-Based Systems Cannot Be Norm-Responsive
+  http://arxiv.org/abs/2602.23239v1
+- InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models
+  http://arxiv.org/abs/2602.23200v1
+- Assessing Deanonymization Risks with Stylometry-Assisted LLM Agent
+  http://arxiv.org/abs/2602.23079v1
+- Rejection Mixing: Fast Semantic Propagation of Mask Tokens for Efficient DLLM Inference
+  http://arxiv.org/abs/2602.22868v1
+- Differentiable Zero-One Loss via Hypersimplex Projections
+  http://arxiv.org/abs/2602.23336v1
+- FairQuant: Fairness-Aware Mixed-Precision Quantization for Medical Image Classification
+  http://arxiv.org/abs/2602.23192v1
+
+---
+*Generated by AINewsCollector*

daily/2026-02-28_zh.md

@@ -0,0 +1,67 @@
+# AI Daily Brief - 2026-02-28
+
+> 采集时间: 2026/2/28 00:08:24
+> 总条目: 131
+
+## 🔥 Top 10 重要消息
+
+1. 《构建、优化与调试智能体系统：全面智能体技能集》
+   https://github.com/sponsors/muratcankoylan
+
+2. Claude智能多代理编排平台：构建企业级对话AI系统
+   https://github.com/login?return_to=%2Fruvnet%2Fclaude-flow
+
+3. “深度搜索，少思多行：重思长周期智能搜索以提升效率和泛化能力”
+   https://huggingface.co/papers/2602.22675
+
+4. 多智能体系统信息流优化：AgentDropoutV2测试时剪枝技术
+   https://huggingface.co/papers/2602.23258
+
+5. 基于条件指导调度的混合数据管道并行加速扩散模型
+   https://huggingface.co/papers/2602.21760
+
+6. 混合策略强化学习：探索性记忆增强大型语言模型代理
+   https://huggingface.co/papers/2602.23008
+
+7. 基于WiFi的颠覆性全身姿态估计系统InvisPose：实时穿墙追踪
+   https://github.com/login?return_to=%2Fruvnet%2Fwifi-densepose
+
+8. 开源SuperAgent工具，融合沙箱、记忆、工具、技能与子代理，高效处理多级任务
+   https://github.com/login?return_to=%2Fbytedance%2Fdeer-flow
+
+9. 边缘设备快速精准语音识别技术突破
+   https://github.com/login?return_to=%2Fmoonshine-ai%2Fmoonshine
+
+10. 构建智能体技能框架与软件开发方法论新范式
+   https://github.com/sponsors/obra
+
+## 📂 分类汇总
+
+### Agent 框架
+
+- 构建专家级投资团队：细粒度交易任务的多智能体LLM系统
+  http://arxiv.org/abs/2602.23330v1
+- 多智能体系统信息流优化：AgentDropoutV2测试时剪枝技术
+  http://arxiv.org/abs/2602.23258v1
+
+### AI 基础设施 / 推理优化
+
+- 硬件加速器上基于位运算的运行时重构多精度量化乘法阵列架构
+  http://arxiv.org/abs/2602.23334v1
+- 基于不变变换与重采样减少认知不确定性的AI模型
+  http://arxiv.org/abs/2602.23315v1
+- 基于规范响应的优化系统无法适应机构与架构限制
+  http://arxiv.org/abs/2602.23239v1
+- 硬件感知无调优量化KV缓存，优化大语言模型解码效率
+  http://arxiv.org/abs/2602.23200v1
+- 利用文体学辅助LLM代理评估匿名化风险
+  http://arxiv.org/abs/2602.23079v1
+- 拒绝混合：高效DLLM推理中掩码标记快速语义传播技术
+  http://arxiv.org/abs/2602.22868v1
+- 基于超单纯形投影的可微分0-1损失
+  http://arxiv.org/abs/2602.23336v1
+- 公平量化：医疗图像分类的公平感知混合精度量化
+  http://arxiv.org/abs/2602.23192v1
+
+---
+*Generated by AINewsCollector*

skill/ai-news-collector/collect.js

@@ -16,43 +16,69 @@ const DAILY_DIR = path.join(__dirname, '../../daily');
 // 代理配置
 const PROXY_URL = process.env.HTTP_PROXY || process.env.HTTPS_PROXY || 'http://127.0.0.1:7890';
 
+// 智谱 AI API 配置
+const ZHIPU_API = 'https://open.bigmodel.cn/api/paas/v4/chat/completions';
+const ZHIPU_KEY = process.env.ZHIPU_KEY || '64536e2512184e36afaa08a057f6879c.o7hCohyniLdPF2Xn';
+const ZHIPU_MODEL = 'glm-4-flash';
+
 // 翻译缓存
 const translateCache = new Map();
 
-// 使用 Google Translate API 翻译文本
+// 使用 LLM 总结并翻译为中文标题
-function translateToChinese(text) {
+function summarizeToChinese(title, summary) {
-  if (!text || text.length === 0) return text;
+  const cacheKey = `${title}|||${summary}`;
-  
+  if (translateCache.has(cacheKey)) {
-  // 检查缓存
+    return translateCache.get(cacheKey);
-  if (translateCache.has(text)) {
-    return translateCache.get(text);
   }
   
   try {
+    const prompt = `请将以下 AI 论文/项目信息总结为一句话中文标题（30字以内，突出核心贡献或创新点）：
+
+标题：${title}
+摘要：${summary || '无'}
+
+要求：
+1. 只输出翻译后的标题，不要其他内容
+2. 标题要简洁有力，突出技术亮点
+3. 使用专业术语的中文译名`;
+
     const proxyFlag = PROXY_URL ? `--proxy "${PROXY_URL}"` : '';
-    const encodedText = encodeURIComponent(text.slice(0, 500)); // 限制长度
+    const requestBody = JSON.stringify({
-    const url = `https://translate.googleapis.com/translate_a/single?client=gtx&sl=en&tl=zh-CN&dt=t&q=${encodedText}`;
+      model: ZHIPU_MODEL,
+      messages: [
+        { role: 'system', content: '你是一个AI技术专家，擅长总结论文和项目核心内容。' },
+        { role: 'user', content: prompt }
+      ],
+      max_tokens: 100,
+      temperature: 0.3
+    });
+    
+    // 写入临时文件避免命令行转义问题
+    const tmpFile = `/tmp/zhipu_request_${Date.now()}.json`;
+    fs.writeFileSync(tmpFile, requestBody);
     
     const result = execSync(
-      `curl -s ${proxyFlag} -L --max-time 10 "${url}"`,
+      `curl -s ${proxyFlag} -X POST "${ZHIPU_API}" -H "Content-Type: application/json" -H "Authorization: Bearer ${ZHIPU_KEY}" -d @${tmpFile}`,
-      { encoding: 'utf8', timeout: 15000 }
+      { encoding: 'utf8', timeout: 30000, maxBuffer: 1024 * 1024 }
     );
     
+    // 清理临时文件
+    try { fs.unlinkSync(tmpFile); } catch (e) {}
+    
     const json = JSON.parse(result);
-    // 解析翻译结果
+    let translated = json.choices?.[0]?.message?.content?.trim() || title;
-    let translated = '';
+    
-    if (Array.isArray(json) && Array.isArray(json[0])) {
+    // 清理可能的多余内容
-      for (const part of json[0]) {
+    translated = translated.split('\n')[0].trim();
-        if (part && part[0]) translated += part[0];
+    if (translated.length > 60) {
-      }
+      translated = translated.slice(0, 57) + '...';
     }
     
-    const finalText = translated || text;
+    translateCache.set(cacheKey, translated);
-    translateCache.set(text, finalText);
+    return translated;
-    return finalText;
   } catch (err) {
-    // 翻译失败，返回原文
+    // 翻译失败，返回原标题
-    return text;
+    return title;
   }
 }
 
@@ -216,17 +242,26 @@ function generateMarkdownZH(items, topCount, topics, date) {
   md += `> 总条目: ${items.length}\n\n`;
   
   md += `## 🔥 Top ${topCount} 重要消息\n\n`;
+  console.log(`   翻译 Top ${topCount}...`);
   for (let i = 0; i < top10.length; i++) {
     const item = top10[i];
-    const titleZH = translateToChinese(item.title);
+    process.stdout.write(`   [${i + 1}/${top10.length}] `);
-    const summaryZH = item.summary ? translateToChinese(item.summary.slice(0, 200)) : '';
+    const titleZH = summarizeToChinese(item.title, item.summary);
-    md += `${i + 1}. [${titleZH}](${item.url}) - **${item.source}**\n`;
+    md += `${i + 1}. ${titleZH}\n   ${item.url}\n\n`;
-    if (summaryZH) md += `   > ${summaryZH.slice(0, 150)}${summaryZH.length > 150 ? '...' : ''}\n`;
-    md += '\n';
   }
   
   md += `## 📂 分类汇总\n\n`;
   
+  let totalCategoryItems = 0;
+  for (const topic of topics) {
+    const topicItems = items.filter(item => {
+      const text = `${item.title} ${item.summary}`.toLowerCase();
+      return topic.keywords.some(k => text.includes(k.toLowerCase()));
+    }).filter(item => !top10.includes(item));
+    totalCategoryItems += topicItems.length;
+  }
+  
+  let processedCount = 0;
   for (const topic of topics) {
     const topicItems = items.filter(item => {
       const text = `${item.title} ${item.summary}`.toLowerCase();
@@ -236,8 +271,10 @@ function generateMarkdownZH(items, topCount, topics, date) {
     if (topicItems.length > 0) {
       md += `### ${topic.name}\n\n`;
       for (const item of topicItems.slice(0, 10)) {
-        const titleZH = translateToChinese(item.title);
+        processedCount++;
-        md += `- [${titleZH}](${item.url}) - ${item.source}\n`;
+        process.stdout.write(`   [${processedCount}/${totalCategoryItems}] `);
+        const titleZH = summarizeToChinese(item.title, item.summary);
+        md += `- ${titleZH}\n  ${item.url}\n`;
       }
       md += '\n';
     }
@@ -258,9 +295,7 @@ function generateMarkdownEN(items, topCount, topics, date) {
   md += `## 🔥 Top ${topCount} Highlights\n\n`;
   for (let i = 0; i < top10.length; i++) {
     const item = top10[i];
-    md += `${i + 1}. [${item.title}](${item.url}) - **${item.source}**\n`;
+    md += `${i + 1}. ${item.title}\n   ${item.url}\n\n`;
-    if (item.summary) md += `   > ${item.summary.slice(0, 150)}${item.summary.length > 150 ? '...' : ''}\n`;
-    md += '\n';
   }
   
   md += `## 📂 Categories\n\n`;
@@ -282,7 +317,7 @@ function generateMarkdownEN(items, topCount, topics, date) {
       const topicNameEN = topicNamesEN[topic.name] || topic.name;
       md += `### ${topicNameEN}\n\n`;
       for (const item of topicItems.slice(0, 10)) {
-        md += `- [${item.title}](${item.url}) - ${item.source}\n`;
+        md += `- ${item.title}\n  ${item.url}\n`;
       }
       md += '\n';
     }