diff --git a/cache/seen_urls.json b/cache/seen_urls.json index f68b536..5cc9a44 100644 --- a/cache/seen_urls.json +++ b/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", diff --git a/daily/2026-02-28_en.md b/daily/2026-02-28_en.md new file mode 100644 index 0000000..3df181d --- /dev/null +++ b/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* diff --git a/daily/2026-02-28_zh.md b/daily/2026-02-28_zh.md new file mode 100644 index 0000000..6feb046 --- /dev/null +++ b/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* diff --git a/skill/ai-news-collector/collect.js b/skill/ai-news-collector/collect.js index f869e3a..0eec2e3 100644 --- a/skill/ai-news-collector/collect.js +++ b/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 -function translateToChinese(text) { - if (!text || text.length === 0) return text; - - // 检查缓存 - if (translateCache.has(text)) { - return translateCache.get(text); +// 使用 LLM 总结并翻译为中文标题 +function summarizeToChinese(title, summary) { + const cacheKey = `${title}|||${summary}`; + if (translateCache.has(cacheKey)) { + return translateCache.get(cacheKey); } 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 "${PROXY_URL}"` : ''; - const encodedText = encodeURIComponent(text.slice(0, 500)); // 限制长度 - const url = `https://translate.googleapis.com/translate_a/single?client=gtx&sl=en&tl=zh-CN&dt=t&q=${encodedText}`; + const requestBody = JSON.stringify({ + 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}"`, - { encoding: 'utf8', timeout: 15000 } + `curl -s ${proxyFlag} -X POST "${ZHIPU_API}" -H "Content-Type: application/json" -H "Authorization: Bearer ${ZHIPU_KEY}" -d @${tmpFile}`, + { 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 = ''; - if (Array.isArray(json) && Array.isArray(json[0])) { - for (const part of json[0]) { - if (part && part[0]) translated += part[0]; - } + let translated = json.choices?.[0]?.message?.content?.trim() || title; + + // 清理可能的多余内容 + translated = translated.split('\n')[0].trim(); + if (translated.length > 60) { + translated = translated.slice(0, 57) + '...'; } - const finalText = translated || text; - translateCache.set(text, finalText); - return finalText; + translateCache.set(cacheKey, translated); + return translated; } catch (err) { - // 翻译失败，返回原文 - return text; + // 翻译失败，返回原标题 + 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 ${topCount}...`); for (let i = 0; i < top10.length; i++) { const item = top10[i]; - const titleZH = translateToChinese(item.title); - const summaryZH = item.summary ? translateToChinese(item.summary.slice(0, 200)) : ''; - md += `${i + 1}. [${titleZH}](${item.url}) - **${item.source}**\n`; - if (summaryZH) md += ` > ${summaryZH.slice(0, 150)}${summaryZH.length > 150 ? '...' : ''}\n`; - md += '\n'; + process.stdout.write(` [${i + 1}/${top10.length}] `); + const titleZH = summarizeToChinese(item.title, item.summary); + md += `${i + 1}. ${titleZH}\n ${item.url}\n\n`; } md += `## 📂 分类汇总\n\n`; + let totalCategoryItems = 0; + for (const topic of topics) { + const topicItems = items.filter(item => { + const text = `${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 += `### ${topic.name}\n\n`; for (const item of topicItems.slice(0, 10)) { - const titleZH = translateToChinese(item.title); - md += `- [${titleZH}](${item.url}) - ${item.source}\n`; + processedCount++; + process.stdout.write(` [${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`; - if (item.summary) md += ` > ${item.summary.slice(0, 150)}${item.summary.length > 150 ? '...' : ''}\n`; - md += '\n'; + md += `${i + 1}. ${item.title}\n ${item.url}\n\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'; }