Lagrange-Labs/deep-prove

• highreadme#1

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

  Why:

  CURRENT

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

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  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.

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• mediumreadme#2

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

  Why:

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  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."

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• lowreadme#3

  Clarify and complete the licensing information in the README

  Why:

  CURRENT

  ## 📜 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.

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

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

  you: not recommended
  AI recommended (in order):

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

  AI recommended 5 alternatives but never named Lagrange-Labs/deep-prove. This is the gap to close.

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• CATEGORY QUERY

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

  you: not recommended
  AI recommended (in order):

  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 recommended 7 alternatives but never named Lagrange-Labs/deep-prove. This is the gap to close.

  Show full AI answer

• Metadata completeness

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• README presence

  pass

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

  pass
  ✓AI named Lagrange-Labs/deep-prove explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts Lagrange-Labs/deep-prove in production, what risks or prerequisites should they evaluate first?

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  ✓AI named Lagrange-Labs/deep-prove explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo Lagrange-Labs/deep-prove solve, and who is the primary audience?

  pass
  ✓AI named Lagrange-Labs/deep-prove explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?