值得关注的技术线索。

完整摘要 · 原始资料Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or self-hosted, so teams move from prototype to production without rebuilding the stack.

来源证据已读取仓库 README（作者资料，未经独立验证）

推荐依据匹配关注方向：llm、agent、search、LLM、Agent；新鲜度 1.00、同源同类型热度分位 0.00、资料完整度 1.0

解读状态未完成 AI 解读

发布时间

04-12 15:40（北京时间）

仓库更新时间

10-01 00:20（北京时间）

语言

TypeScript

许可证

NOASSERTION

发现入口

official_search_fallback (不是 Trending 榜单)

Star 增量未知：尚无有效历史基线。

完整摘要 · 原始资料The agent that grows with you

来源证据仅来源元数据；未访问正文

推荐依据匹配关注方向：llm、agent、LLM、Agent；新鲜度 1.00、同源同类型热度分位 0.67、资料完整度 0.4

解读状态未完成 AI 解读

原文读取说明：原文读取失败；信息范围以已归档证据为准。

发布时间

07-23 06:22（北京时间）

仓库更新时间

10-01 00:30（北京时间）

语言

Python

许可证

MIT

发现入口

official_search_fallback (不是 Trending 榜单)

Star 增量未知：尚无有效历史基线。

完整摘要 · 原始资料Get up and running with Kimi, GLM, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.

来源证据已读取仓库 README（作者资料，未经独立验证）；已读取官方 Release 元数据及发布说明

推荐依据匹配关注方向：llm、LLM；新鲜度 0.40、同源同类型热度分位 0.33、资料完整度 1.0

解读状态未完成 AI 解读

发布时间

09-29 05:23（北京时间）

仓库更新时间

09-30 10:33（北京时间）

语言

Go

许可证

MIT

发现入口

official_search_fallback (不是 Trending 榜单)

Star 增量未知：尚无有效历史基线。

完整摘要 · 原始资料TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents [](LICENSE) [](https://github.com/caipeng328/TeleOCR) [](https://www.teleai.com.cn/docparse/DocumentParsing) [](https://arxiv.org/abs/2608.12898) ## 🔥 News - **2026/09/10** - We have renamed NaviDC-OCR to TeleOCR, and all subsequent model iterations will be developed and released under the TeleOCR version. - **2026/09/01** - We noticed that EMNLP 2026 is hosting the [Dr.DocBench Challenge](https://eval.ai/web/challenges/challenge-page/2717/overview), a document parsing competition. We evaluated NaviDC-OCR with its native weights, achieving better results than MinerU 2.5 Pro and PaddleOCR-VL 1.6. Detailed results are shown below **dr.docbench-challenge**. We welcome the use of Navi

来源证据已读取官方 Hub 模型元数据和作者模型卡；性能描述属作者宣称

推荐依据匹配关注方向：language model、inference；新鲜度 1.00、同源同类型热度分位 0.67、资料完整度 1.0

解读状态未完成 AI 解读

发布时间

08-14 14:15（北京时间）

仓库更新时间

09-29 08:30（北京时间）

任务

image-text-to-text

参数总量（元数据）

1415072768

许可证

apache-2.0

发现入口

trending

下载数是滚动统计，不是每日新增，也不代表质量或性能。

完整摘要 · 原始资料Large language model (LLM) routing reduces serving cost by assigning each query to an appropriate model while preserving response quality. Learning such a router, however, often requires executing multiple candidate models on historical queries to collect query--model quality feedback, creating a nontrivial supervision cost before deployment. Existing work largely focuses on serving-time efficiency, overlooking whether the resulting savings are sufficient to recover this upfront expenditure. We further observe that routing quality often saturates well before all query--model feedback is collected, suggesting that dense supervision can be economically over-provisioned. We propose SaveRouter, a sparse-supervision routing framework that selectively acquires informative model feedback and shares capability information across related queries, while retaining query-level refinement for fine-grained routing. We evaluate routing by jointly accounting for supervision expenditure and subsequent serving-time savings. Across four routing benchmarks, the main setting uses only about 33--41% of available training feedback while maintaining competitive or better routing quality, and reduces the break-even deployment volume by approximately 1.9--9.5 times compared with the fastest conventional router. Further analysis shows that acquiring more supervision is not always economically preferable: the supervision level that minimizes serving cost can differ from the one that achieves the earliest payback. Our code is publicly available at https://github.com/LAMDA-Model-Reuse/SaveRouter.

来源证据已读取 Daily Papers 论文摘要；未读取论文全文，实验结果为作者宣称

推荐依据匹配关注方向：llm、language model、serving、LLM；新鲜度 0.76、同源同类型热度分位 0.50、资料完整度 1.0

解读状态未完成 AI 解读

发布时间

09-29 08:00（北京时间）

完整摘要 · 原始资料The AI Race Just Got Awkward

来源证据已读取原文可见文本（可能包含导航或付费墙）；未抓取评论树

推荐依据匹配关注方向：search、inference、serving；新鲜度 0.99、同源同类型热度分位 0.63、资料完整度 1.0

解读状态未完成 AI 解读

发布时间

09-30 23:50（北京时间）