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Sector9: AI Benchmark & RLAF Engine

1. The Crisis in AI Software Benchmarking​

Current AI coding benchmarks (e.g., HumanEval, MBPP) are limited. They rely on unit tests, which only check a few execution paths and can be gamed by models that have seen similar test cases. They also do not measure formal reasoning or protocol-level safety well.

Sector9 can provide a stronger objective signal for AI software generation: a model either produces code and specifications that satisfy the configured Viper verification and protocol-model checks, or it does not. The result is scoped to the written properties and verifier assumptions, but it is harder to fake than a small test suite.

2. The "S9B" (Sector9 Benchmark) Leaderboard​

We will establish the Sector9 Benchmark (S9B), a real-time, competitive leaderboard for LLMs (GPT, Claude, Gemini, Llama).

  • The Challenges: A standardized set of high-stakes protocol requirements (e.g., "Implement a Multi-canister Lending Market with No-Liquidation-Race-Condition").
  • The Scoring (The Triple Green Metric):
    1. Syntactic Score: Does it compile?
    2. Local Logic Score: Does it pass Viper-backed contracts and actor invariants?
    3. Global Protocol Score: Does it pass the TLA+ protocol lane for modeled async safety?
  • The Stats: We generate public stats on Proof-Success-Rate and Solver-Efficiency (how many SMT steps the model's specification required).

3. RLAF: Reinforcement Learning from Axiomatic Feedback​

This is a useful training signal for AI makers. Current models often rely on RLHF (Reinforcement Learning from Human Feedback), which is slow, expensive, and subjective.

Sector9 enables RLAF (Reinforcement Learning from Axiomatic Feedback):

  1. The Loop: An AI generates code/specs -> Sector9 rejects it with a counterexample or proof failure -> The AI uses that deterministic feedback to correct itself.
  2. The Teacher: The "Teacher" isn't a human; it's the SMT Solver and the Model Checker.
  3. The Optimization: AI makers can use Sector9 as one reward function in training loops. A model that consistently generates "Triple Green" code has demonstrated useful specification-following behavior for this domain.

4. The AI-Maker Incentive: The "Verified Reasoning" Badge​

In the race for better coding models, reasoning is a major frontier. AI makers want evidence that their models can do more than produce plausible-looking code.

  • The Certificate: We will issue the "Sector9 Reasoning Certificate" to models that exceed a certain threshold on the S9B.
  • Market Signal: This badge can become a useful risk signal for high-integrity code generation, especially for teams that want reproducible proof artifacts rather than only benchmark pass rates.

5. The Economic "Data Flywheel"​

As users in the proof DAO use different models to "mine" proofs, they generate a growing dataset of formal reasoning artifacts: Sector9 source, contracts, proof failures, counterexamples, Viper output, and protocol-model results.

  • The Asset: This dataset is valuable training data for specification-aware software engineering.
  • The Monetization: The DAO can license this "Verified Dataset" back to AI makers to help them fine-tune their models for the Sector9 ecosystem.

6. Conclusion: Sector9 as the "LIGO" of Logic​

Just as scientific instruments provide repeatable measurements, Sector9 can provide repeatable measurements of whether code satisfies stated logical and temporal properties.

By gamifying competition between AI models and providing deterministic feedback for proof attempts, Sector9 can become a useful evaluation and training component for high-assurance software generation.