Synthetic data
Name variants 合成数据 · 合成數據
arXiv:2611.05512v1 Announce Type: new Abstract: AI Agents are becoming digital coworkers that can browse the web, write code, run experiments, and assist humans over extended horizons. Yet existing benchmarks fall short of real knowledge work: many rely on synthetic data or isolated tasks, missing the complex, multi-party dynamics of collaborative projects,
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- First observed in our collection
- Oct 21, 2026, 4:30 PM UTC
- Earliest cited publication
- Oct 21, 2026, 4:07 PM UTC
- Last observed
- Nov 5, 2026, 10:00 AM UTC
Evidence in the last complete seven days
- Source mentions
- 1 Previous seven days 0
- Distinct sources
- 1 Previous 0
- Comparable growth
- Not comparable
- Ranking appearances
- 0
Collection observations and undated records are separate from dated publication counts. Observed in 7d：1 · Undated records：0
Collection coverage · Limited historyCurrent / previous observed days 7 / 4
Counts use complete UTC days. First observed means first seen by this collection, not when a term was coined.
Current window：2026-10-30 → 2026-11-06 (end excluded) · History 12 days
| Source | Current days | Previous days | Last success |
|---|---|---|---|
| arXiv | 6 | 4 | Nov 5, 2026, 10:00 AM UTC |
| arXiv | 7 | 3 | Nov 5, 2026, 10:01 AM UTC |
| Ask HN | 7 | 4 | Nov 5, 2026, 9:15 PM UTC |
| Ask HN | 7 | 4 | Nov 5, 2026, 8:30 PM UTC |
| Ask HN | 7 | 4 | Nov 5, 2026, 9:15 PM UTC |
| Ask HN | 7 | 4 | Nov 5, 2026, 9:15 PM UTC |
| GitHub | 7 | 4 | Nov 5, 2026, 12:15 PM UTC |
| GitHub Issues | 7 | 4 | Nov 5, 2026, 4:00 PM UTC |
| GitHub Releases | 7 | 2 | Nov 6, 2026, 12:15 AM UTC |
| GitHub | 7 | 4 | Nov 5, 2026, 2:30 PM UTC |
| GitHub Issues | 7 | 4 | Nov 5, 2026, 4:15 PM UTC |
| GitHub Releases | 7 | 4 | Nov 5, 2026, 4:15 PM UTC |
| GitHub | 7 | 4 | Nov 5, 2026, 2:30 PM UTC |
| GitHub Issues | 7 | 4 | Nov 5, 2026, 4:45 PM UTC |
| GitHub Releases | 7 | 4 | Nov 5, 2026, 4:15 PM UTC |
| GitHub Discovery | 1 | 0 | Nov 5, 2026, 9:15 PM UTC |
| GitHub Trending | 7 | 4 | Nov 5, 2026, 6:45 PM UTC |
| 7 | 3 | Nov 5, 2026, 10:00 AM UTC | |
| Hugging Face | 7 | 3 | Nov 5, 2026, 8:15 AM UTC |
| Hugging Face Spaces | 1 | 0 | Nov 5, 2026, 9:15 PM UTC |
| Hacker News | 7 | 4 | Nov 5, 2026, 6:45 PM UTC |
| Hacker News | 7 | 4 | Nov 5, 2026, 9:30 PM UTC |
| Hacker News | 7 | 4 | Nov 5, 2026, 9:30 PM UTC |
| Hacker News | 1 | 0 | Nov 5, 2026, 9:15 PM UTC |
| Show HN | 1 | 0 | Nov 5, 2026, 9:15 PM UTC |
| MIT News | 7 | 3 | Nov 5, 2026, 4:15 PM UTC |
| OpenAI | 7 | 4 | Nov 5, 2026, 11:30 AM UTC |
| Product Hunt | 7 | 4 | Nov 5, 2026, 6:45 PM UTC |
| 量子位 | 6 | 4 | Nov 4, 2026, 10:15 AM UTC |
| OpenRouter | 1 | 0 | Nov 5, 2026, 11:58 PM UTC |
| Hugging Face Models | 1 | 0 | Nov 5, 2026, 11:58 PM UTC |
| HF Daily Papers | 1 | 0 | Nov 5, 2026, 11:58 PM UTC |
| OpenRouter | 1 | 0 | Nov 5, 2026, 11:58 PM UTC |
| SEC EDGAR | 7 | 4 | Nov 5, 2026, 10:30 PM UTC |
| Sequoia Capital | 7 | 3 | Nov 5, 2026, 10:00 AM UTC |
| Simon Willison | 1 | 0 | Nov 5, 2026, 3:13 PM UTC |
| TechCrunch | 7 | 4 | Nov 5, 2026, 8:30 PM UTC |
| Contracts Finder | 7 | 4 | Nov 5, 2026, 7:45 PM UTC |
| V2EX | 7 | 4 | Nov 5, 2026, 8:30 PM UTC |
- Counts describe monitored public sources, not the whole web or search demand.
- Both weeks need adequate, comparable collection coverage.
Daily history · up to 90 days
Bars show dated source mentions. The latest UTC day may be incomplete. Zero mentions and missing collection coverage are different conditions.
Dotted markers indicate days with no record. They are not counted as zero mentions.
View accessible daily data
| UTC date | Mentions | Sources | Observations | Ranking appearances |
|---|---|---|---|---|
| 2026-10-21 | 1 | 1 | 1 | 0 |
| 2026-10-22 · No record | — | — | — | — |
| 2026-10-23 | 0 | 0 | 0 | 0 |
| 2026-10-24 | 0 | 0 | 0 | 0 |
| 2026-10-25 | 0 | 0 | 0 | 0 |
| 2026-10-26 | 0 | 0 | 0 | 0 |
| 2026-10-27 | 0 | 0 | 0 | 0 |
| 2026-10-28 | 0 | 0 | 0 | 0 |
| 2026-10-29 | 0 | 0 | 0 | 0 |
| 2026-10-30 | 0 | 0 | 0 | 0 |
| 2026-10-31 | 0 | 0 | 0 | 0 |
| 2026-11-01 | 0 | 0 | 0 | 0 |
| 2026-11-02 | 0 | 0 | 0 | 0 |
| 2026-11-03 | 0 | 0 | 0 | 0 |
| 2026-11-04 | 0 | 0 | 0 | 0 |
| 2026-11-05 | 1 | 1 | 1 | 0 |
| 2026-11-06 | 0 | 0 | 0 | 0 |
Source evidence
Sources in the evidence shown：arXiv 1 · V2EX 1
arXiv · Published Nov 5, 2026
SlackBench: Benchmarking Agents on Collaborative Projects Grounded in Real Code Repositories
arXiv:2611.05512v1 Announce Type: new Abstract: AI Agents are becoming digital coworkers that can browse the web, write code, run experiments, and assist humans over extended horizons. Yet existing benchmarks fall short of real knowledge work: many rely on synthetic data or isolated tasks, missing the complex, multi-party dynamics of collaborative projects,
View source record →V2EX · Published Oct 21, 2026
[分享创造] [开源] 从生图到骨骼动画全自动：用 Blender 合成数据微调 DINOv2，把视频动作还原到非人形 3D 模型上
从粗糙的 Astra 建模踩坑开始，我就一直在琢磨：到底怎么才能让整套 3D 资产与动画生成流程真正自动化起来？ 一路从 OpenAI 产出概念效果图，到 Tripo3D 生成 3D 模型，再到非人形怪物的自动绑骨蒙皮；更进一步，用 MiniMax H3 生成想要的动作视频，并基于 Meta 的 DINOv2 训练专属小模型来识别骨骼与关节位置，最终把视频动作还原到 3D 骨骼上（这里必须感谢 @CanItCode ，核心思路正是从他的视频中学到的），才终于跑通并达到了视频里这种勉强可用的效果。 核心实现思路 模型生成 ：通过 Tripo3D 生成基础 3D 模型。 自动绑骨 ：使用 TokenRig 完成非人形结构的自动绑骨与蒙皮。 **合成训练集(imoportant)**：在 Blender 中采用双视角
View source record →
Review priority 40 / 100
This score orders research candidates. It is not search volume, a forecast or verified commercial demand.
Evidence
8 / 30Distinct dated articles or first observations; repeat crawling adds nothing.
Publisher diversity
8 / 25Conservative owner grouping; syndicated copies count once.
Comparable change
0 / 20Comparable complete weeks only; no percentage from a zero baseline.
Recent evidence
14.4 / 15Latest publication or first observation, not repeated fetch time.
Name specificity
9.7 / 10Explicit entities and technical concepts outrank ambiguous acronyms.
Limitations and interpretation
- First observation is a collection milestone, not proof that a word or product is new to the world.
- Counts describe monitored public sources, not the whole web or search demand.
- First observed here is not the invention date of a name or idea.
- Candidates come from public titles, summaries and entity URLs, not verified full-text analysis.
- Owner grouping is conservative; undisclosed syndication may remain.
- An established concept or common product name retained as a baseline, not a newly invented term.
- Both weeks need adequate, comparable collection coverage.
- The previous week is too small for a reliable percentage change.
- Current evidence comes from one publisher or owner.