Channel
@AIPaperCN
On this record: Growth · Engagement · What this channel posts · Posts · Citations · Cite this entry
21subscribers
+0 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1003956050545 |
|---|---|
| Type | Channel |
| Username | @AIPaperCN |
| Description | 每日精选 AI 论文中文精读：大模型、多模态与前沿研究，一篇讲清方法、结论与它为什么重要。 📮 投稿/合作 @anyou889 |
| Created | Between 1 April 2026 and 31 July 2026— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 11 August 2026 |
| Last confirmed live | 11 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/AIPaperCN |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 11 Aug 2026, 14:16 | 21 | no change |
| 6 Aug 2026, 19:52 | 21 | first reading |
20 posts held, back to 5 August 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
| Window | Rolling 30 days · latest post in window 11 August 2026 |
|---|---|
| Posts held | 20 (5 August 2026 – 11 August 2026) |
| Views total | 72 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 11 Aug 2026, 14:16 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
Lifetime counters from Telegram’s own channel header, read 11 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
📢 优质频道推荐 1. AI自动化前线｜AI工具·自动化·效率 2. 独立开发与一人公司｜独立开发·一人公司·出海 3. AI前沿情报｜AI新闻·大模型·ChatGPT 4. 开发者工具箱｜编程·开发工具·资源 5. Web3 & Protocol Tech — 英文频道·Web3 与智能合约技术 6. 东京视线｜日本新闻 7. 远程项目库｜兼职·外包·接单 8. 狮城日报（新加坡新闻） 9. Crypto Markets & Analysis — 英文频道·行情与交易分析 10. 大马华人圈 · 马来西亚新闻 想让你的频道出现在这里？私聊 @pindaotuijianbot 提交申请（免费）
KGCaRe：用自动知识图谱增强复杂条件问答 复杂条件问答对通用大模型和RAG仍是难题，尤其在专业领域表现不佳。KGCaRe提出混合方案，将文档自动构建为知识图谱，结合向量检索与图谱符号推理，在多个数据集和模型上稳定超越现有基线。 复杂条件问答要求模型理解多条件约束并给出可解释答案，通用大模型和RAG在专业场景下常因缺乏结构化知识而表现不佳。作者假设，在RAG基础上补充来自文档和知识图谱的非结构化与结构化知识，能提升推理与答案准确性。 KGCaRe采用混合架构，结合神经检索与基于LLM生成知识图谱的符号推理。系统先用多提示词提取策略从文档构建知识图谱，存入图数据库；同时将文档嵌入向量库支持神经检索。推理时，LLM引导迭代图遍历，提取相关三元组并剪除无关信息；若首轮遍历未提供足够上下文，则利用额外线索实体再次遍历。 从知识图谱提取的路径形式三元组，与语义检索的文本片段一起输入定制提示词，生成带解释的复杂条件问答答案。研究在…
📢 优质频道推荐 1. 东南亚反诈预警｜防骗·杀猪盘·缅北预警 2. 4K高清影院🎬｜高清电影·院线大片·蓝光资源 3. 副业变现情报｜副业·创业·赚钱思路 4. 网络安全·黑客攻防｜渗透·CTF·安全资讯 5. Bitcoin & Macro — 英文频道·BTC 行情·ETF 流向·宏观 6. 吃瓜社🌸｜猎奇·热搜·八卦·爆料 7. 东南亚大事件 8. Web3链上职位｜区块链招聘·远程·Crypto Jobs 9. 莫斯科来信 · 俄罗斯新闻 10. 东盟商情（越南 泰国 印尼） 想让你的频道出现在这里？私聊 @pindaotuijianbot 提交申请（免费）
📢 优质频道推荐 1. 科技热点局｜科技新闻·数码·前沿 2. 俄乌战争·战报地图分析🗺️｜前线·军情·战场地图 3. 中东战场·前线实拍🔥｜以巴冲突·战况·前线视频 4. 从太空看地球｜卫星遥感·地球观测·科普 5. 电商运营秘籍｜电商运营·跨境·选品 6. 巴黎信箱 · 法国新闻 7. 澳洲新闻 · 华人生活圈 8. 美国晨报 纽约 加州 9. 谷歌SEO与出海营销｜SEO·GEO·独立站获客·流量增长 10. 创业内参｜创业·融资·商业模式 想让你的频道出现在这里？私聊 @pindaotuijianbot 提交申请（免费）
CoinRAG：细粒度缓存复用，让长上下文RAG更省更快 检索增强生成（RAG）近期优化研究通过块级KV缓存复用，避免重复处理长检索上下文以提升效率，但粗粒度块中仍存在大量信息冗余与噪声。本文提出CoinRAG，在低预填充延迟约束下优化帕累托前沿并最大化准确率。其核心机制类似用零散硬币累积更大价值：离线计算细粒度信息片段缓存，组合复用形成语义相关且紧凑的上下文表示，而非编码完整块。具体方法通过两阶段检索识别查询相关语义单元，将其切片KV表示与块级上下文无缝组装。在LongBench多跳问答任务上的广泛评估显示，CoinRAG显著降低运营成本，在标准快速预填充延迟预算下，相较其他基线实现平均5.3%的答案质量（F1）相对提升，并达到新的帕累托前沿。 RAG系统常需处理长检索上下文，现有优化尝试复用块级KV缓存以减少重复计算，但粗粒度块包含大量无关内容，既浪费算力又引入噪声。CoinRAG将缓存粒度从块细化到语义片段，每个片段是…
📢 优质频道推荐 1. GitHub开源观察｜开源项目·热门仓库·开发者 2. 加密财经观察｜比特币·以太坊·行情 3. 印尼早报 雅加达 巴厘岛 4. 海外远程岗位库｜远程工作·海外招聘·数字游民 5. Cripto en Español — 西班牙语频道·加密货币行情与分析 6. 空投情报站｜空投·测试网·撸毛教程 7. 新西兰生活报 8. 我在迪拜 · 阿联酋资讯 9. 柬埔寨快报 金边 西港 暹粒 10. DeFi & Yield — 英文频道·DeFi 质押与收益策略 想让你的频道出现在这里？私聊 @pindaotuijianbot 提交申请（免费）
📢 优质频道推荐 1. AI自动化前线｜AI工具·自动化·效率 2. AI前沿情报｜AI新闻·大模型·ChatGPT 3. 独立开发与一人公司｜独立开发·一人公司·出海 4. 斯里兰卡中文新闻｜科伦坡·南亚·时事 5. 亚美尼亚中文新闻｜埃里温·高加索·时事 6. 大马华人圈 · 马来西亚新闻 7. 开发者工具箱｜编程·开发工具·资源 8. 泰国新闻｜曼谷 清迈 芭提雅 9. Web3 & Protocol Tech — 英文频道·Web3 与智能合约技术 10. Crypto Markets & Analysis — 英文频道·行情与交易分析 想让你的频道出现在这里？私聊 @pindaotuijianbot 提交申请（免费）
视频理解新基准：最强模型全球空间感知仅42.68分 现有空间智能基准多聚焦单视角或少数视角的局部感知，忽视了连续长时视频流中的全局空间意识。GST-Bench 提出全球空间-时间基准，要求模型从未见过的视角进行空间推理，并将第一人称观察映射到全局俯视图。评估22个SOTA视觉语言模型后发现，最强零样本模型仅得42.68分，远低于人类的79.08分。 GST-Bench 是一个面向视频理解的 VQA 基准，包含由合成视频生成的6,790分钟人工验证问题。任务设计要求模型在输入视频中未出现的新视角下完成准确空间推理，同时将自我中心的观察映射到全局俯视图像。 研究团队对22个前沿视觉语言模型进行全面评估，结果暴露模型与人类之间的显著差距：最强零样本模型得分仅42.68，而人类得分高达79.08。为探究差距成因，团队构建 GST-Bench-Local 子集，发现模型在相同任务设定下虽具备较强的局部空间理解能力，却仍无法将长时观测…
📢 优质频道推荐 1. 4K高清影院🎬｜高清电影·院线大片·蓝光资源 2. 网络安全·黑客攻防｜渗透·CTF·安全资讯 3. 副业变现情报｜副业·创业·赚钱思路 4. 东南亚反诈预警｜防骗·杀猪盘·缅北预警 5. Bitcoin & Macro — 英文频道·BTC 行情·ETF 流向·宏观 6. 中文热点榜｜热搜·今日热点·全网 7. 远程项目库｜兼职·外包·接单 8. 东盟商情（越南 泰国 印尼） 9. 加密交易学堂｜合约·现货·K线教学 10. Crypto Daily — 英文频道·BTC 与 ETH 每日要闻 想让你的频道出现在这里？私聊 @pindaotuijianbot 提交申请（免费）
📢 优质频道推荐 1. Web3链上职位｜区块链招聘·远程·Crypto Jobs 2. 软件工具库｜软件推荐·效率工具·实用App 3. iOS 侧载精选｜巨魔·免签·解锁应用 4. 科技热点局｜科技新闻·数码·前沿 5. 中文社会热点｜社会·民生·舆情 6. 环球体育｜足球·NBA·赛事速递 7. 从太空看地球｜卫星遥感·地球观测·科普 8. 俄乌战争·战报地图分析🗺️｜前线·军情·战场地图 9. AveWallet 自动能量租赁 — TRON 钱包机器人·TRX 与 USDT-TRC20 10. 加密一线快讯｜币圈·异动·突发 想让你的频道出现在这里？私聊 @pindaotuijianbot 提交申请（免费）
📢 优质频道推荐 1. 越南每日 河内 胡志明市 2. GitHub开源观察｜开源项目·热门仓库·开发者 3. 命理玄学馆｜易经·八字·黄历·星座运势 4. Cripto en Español — 西班牙语频道·加密货币行情与分析 5. 奇闻冷知识｜冷知识·奇闻·趣事 6. 东京视线｜日本新闻 7. 缅甸一线 仰光 曼德勒 妙瓦底 8. 游戏日报｜新作·热点·硬核 9. 每日沙雕图｜梗图·段子·乐子 10. India Startup & VC — 英文频道·印度创投与融资 想让你的频道出现在这里？私聊 @pindaotuijianbot 提交申请（免费）
EnvACE用世界预演替代环境交互，训练智能体强化学习 训练大语言模型智能体完成长程工具调用，通常依赖真实或合成可执行环境的交互，这类环境的构建与验证成本高昂，外部模拟器又难以落地。EnvACE提出一种智能体强化学习方法，用世界预演替代训练中的外部环境交互。 策略在行动与预演之间交替进行：先生成一次工具调用，再扮演环境角色产出该动作引发的响应，后续决策基于预演出的响应。两个角色通过任务成功奖励端到端联合优化。通过世界预演，策略将动作与环境响应之间的关系内化到参数中，形成直接支撑决策的智能体世界模型。 在BFCL-v4、tau²-Bench、VitaBench和FinMCP-Bench上，EnvACE取得强且可迁移的表现，整体评估优于环境扩展基线。受控研究显示，世界预演在不同模型规模下均稳定提升策略学习。测试阶段，内化的世界模型支持在正式执行前进行私有预演，在适度预演预算下无需额外外部交互即可带来进一步收益。 这项工作为突…
Showing the 12 most recent of 20 posts we hold for @AIPaperCN. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Citation-graph rank — 162,230 of 1,160,990entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Named by 29 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it. The 24 listed below are the most frequent namers; the rest are counted above but not each listed.
Named by
Channels on the register whose posts name this channel's handle.
Names
Channels on the register whose handles appear in this channel's posts.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 11 August 2026 — this entry's latest reading, not the date you are reading this.
“AI 论文精读｜每日精选论文” (@AIPaperCN), 21 subscribers as measured 11 August 2026. Telegram Register, tgregister.com/channel/AIPaperCN.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.