Lagrange-Labs/deep-prove

• highreadme#1

  Strengthen README's opening sentence to highlight core value and problem solved

  原因：

  当前

  Welcome to **DeepProve**, a cutting-edge framework designed to prove neural network inference using zero-knowledge cryptographic techniques.

  复制粘贴的修复

  复制

  DeepProve is a cutting-edge framework for **blazingly fast zero-knowledge proofs of machine learning model inference**, solving the critical challenge of verifying neural network computations without revealing underlying data.

  待办完成忽略
• mediumreadme#2

  Add a 'Why DeepProve?' or 'Comparison' section to clarify differentiation

  原因：

  复制粘贴的修复

  复制

  Add a new section (e.g., 'Why DeepProve?') after the 'Benchmark Highlights' with text similar to: "While general ZK toolkits like Halo2 or Risc0 offer broad cryptographic primitives, DeepProve is purpose-built for the unique demands of machine learning inference, delivering unparalleled speed. Unlike other privacy-preserving ML methods such as homomorphic encryption, DeepProve provides verifiable proofs of computation without revealing sensitive data or model weights."

  待办完成忽略
• lowreadme#3

  Clarify and complete the licensing information in the README

  原因：

  当前

  ## 📜 Licensing
  
  zkml folder**: Licensed under the Lagrange License, unless otherwise specified.
  Rest of the Code*

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  ## 📜 Licensing
  
  The main DeepProve repository is licensed under Apache-2.0. The `zkml` folder, however, is licensed under the Lagrange License, unless otherwise specified within that subfolder. Please refer to the respective LICENSE files for full details.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  How can I quickly verify machine learning model inference using zero-knowledge proofs?

  你：未被推荐
  AI 推荐顺序：

  1. EZKL
  2. Halo2
  3. Risc0
  4. Gnark
  5. Lattice

  AI 推荐了 5 个替代方案，却始终没点名 Lagrange-Labs/deep-prove。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  What framework helps prove neural network inference without revealing sensitive input data?

  你：未被推荐
  AI 推荐顺序：

  1. Zama Concrete ML (zama-ai/concrete-ml)
  2. TF Encrypted (tf-encrypted/tf-encrypted)
  3. OpenMined PySyft (OpenMined/PySyft)
  4. EzPC (multiparty/EzPC)
  5. HElib (homenc/HElib)
  6. SEAL (microsoft/SEAL)
  7. PALISADE (DualityTechnologies/PALISADE)

  AI 推荐了 7 个替代方案，却始终没点名 Lagrange-Labs/deep-prove。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of Lagrange-Labs/deep-prove?

  pass
  ✓AI 明确点名了 Lagrange-Labs/deep-prove

  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 Lagrange-Labs/deep-prove in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 Lagrange-Labs/deep-prove

  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 Lagrange-Labs/deep-prove solve, and who is the primary audience?

  pass
  ✓AI 明确点名了 Lagrange-Labs/deep-prove

  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？