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evo-design/evo
默认分支 main · commit 6856bba4 · 扫描时间 2026/5/12 09:27:06
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行动计划告诉你下一步要做什么——按影响力排序、可直接复制粘贴的修改。品类可见性是真正的 GEO 测试:当用户向 AI 提一个不带品牌、本应让 evo-design/evo 浮出水面的问题时,AI 是真的推荐了你,还是推荐了你的竞品?客观检查验证 AI 引擎最先权衡的那些元数据信号。自指检查判断 AI 是否还认识你的名字。
行动计划 — 可复制粘贴的修复
3 条由 gemini-2.5-flash 生成、按优先级排序的修改。修完后请把对应条目标记为完成。
- highreadme#1Reposition the Evo 2 mention in the README to clarify Evo 1.x's focus
原因:
当前# Evo: DNA foundation modeling from molecular to genome scale **We have developed a new model called Evo 2 that extends the Evo 1 model and its ideas to all domains of life. Please see https://github.com/arcinstitute/evo2 for more details.** Evo is a biological foundation model capable of long-context modeling and design.
复制粘贴的修复# Evo: DNA foundation modeling from molecular to genome scale This repository contains the Evo 1.x models and code, focusing on long-context DNA sequence analysis and design for prokaryotic genomes. Evo is a biological foundation model capable of long-context modeling and design. **We have developed a new model called Evo 2 that extends the Evo 1 model and its ideas to all domains of life. Please see https://github.com/arcinstitute/evo2 for more details.**
- hightopics#2Add relevant topics to the repository
原因:
复制粘贴的修复dna-sequencing, genomic-analysis, biological-foundation-model, language-model, synthetic-biology, machine-learning, deep-learning, bioinformatics, prokaryotic-genomes, long-context-modeling
- mediumhomepage#3Add a homepage URL to the repository
原因:
复制粘贴的修复https://www.arcinstitute.org/research/evo
本次扫描解析到的品类 GEO 通道:google/gemini-2.5-flash, deepseek/deepseek-v4-flash
品类可见性 — 真正的 GEO 测试
向 google/gemini-2.5-flash 提出的不带品牌问题。AI 推荐了你,还是推荐了别人?
各模型使用同一组问题 — 切换标签对比回答与排名。
- HyenaDNA · 被推荐 2 次
- DNABERT · 被推荐 2 次
- Nucleotide Transformer · 被推荐 1 次
- Hugging Face Transformers · 被推荐 1 次
- Enformer · 被推荐 1 次
- 品类问题What tools help with long-context biological sequence modeling for genomic analysis?你:未被推荐AI 推荐顺序:
- HyenaDNA
- DNABERT
- Nucleotide Transformer
- Hugging Face Transformers
- Enformer
- Pytorch-Longformer
- XGen
AI 推荐了 7 个替代方案,却始终没点名 evo-design/evo。这就是要补上的差距。
查看 AI 完整回答
- 品类问题How can I generate synthetic DNA sequences using a genomic language model?你:未被推荐AI 推荐顺序:
- Nucleotide Transformer (NT)
- GenSLM (Genomic Sequence Language Model)
- DNABERT
- HyenaDNA
- PyTorch
- TensorFlow
- Hugging Face's `transformers` library
- DeepMind's AlphaFold
AI 推荐了 8 个替代方案,却始终没点名 evo-design/evo。这就是要补上的差距。
查看 AI 完整回答
客观检查
针对 AI 引擎最看重的元数据信号的规则审计。
- Metadata completenesswarn
建议:
- README presencepass
自指检查
当被直接问到你时,AI 是否还知道你的仓库存在?
- Compared to common alternatives in this category, what is the core differentiator of evo-design/evo?passAI 明确点名了 evo-design/evo
AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?
- If a team adopts evo-design/evo in production, what risks or prerequisites should they evaluate first?passAI 明确点名了 evo-design/evo
AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?
- In one sentence, what problem does the repo evo-design/evo solve, and who is the primary audience?passAI 明确点名了 evo-design/evo
AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?
嵌入你的 GEO 徽章
把这个徽章贴进 evo-design/evo 的 README。每次重新扫描都会自动更新,并跳到最新报告——是「我在乎 AI 可发现性」最简单的公开证明。
[](https://repogeo.com/zh/r/evo-design/evo)<a href="https://repogeo.com/zh/r/evo-design/evo"><img src="https://repogeo.com/badge/evo-design/evo.svg" alt="RepoGEO" /></a>订阅 Pro,解锁深度诊断
evo-design/evo — 轻量扫描仍免费;本卡列出 Pro 相对轻量的深度额度。
- 深度报告每月 10 次
- 无品牌品类查询5,轻量 2
- 优先行动项8,轻量 3