Deterministic seeding: seed URL + RNG propagation to KPCA, TD estimator, and grammar

[recursive-ai-dev]

Motivation

• Remove hidden nondeterminism and make generation/training reproducible by seeding the engine RNG.
• Provide a simple external entrypoint for determinism via the URL (?seed=) that accepts numeric or string seeds.
• Centralize RNG usage so all stochastic components share a single, seedable RNG instance.
• Document the change and testing expectations in the logic and testing documents for traceability.

Description

• Add ?seed= parsing and stable string hashing (fnv-1a style) via resolveSeedFromLocation and hashSeedString in src/App.jsx.
• Propagate a seeded RNG into core components by wiring a LaggedFibonacciGenerator(seed) through AGTuneEngine and exposing it to KernelPCA, TDValueEstimator, and UniversalLinguisticEngine (and ConstraintGrammar), replacing local Math.random() fallbacks.
• Replace non-deterministic uses of Math.random() in selection/generation paths with calls to the injected RNG (or rng.next()) and allow components to accept an rng dependency for deterministic initialization.
• Update documentation files LOGIC-MAP.md, WE-CHOSE.md, and TESTING.md to describe the deterministic seeding logic, perspectives, and a seed-driven test matrix.

Testing

• No automated tests were executed as part of this change.
• The repository contains automated validation scripts for the engine such as npm run test-engine and npm run test-all which can be used to validate components after seeding.
• Manual static checks (file edits/grep/commits) were performed to verify RNG propagation through the modified files.
• Recommended automated verification: run npm run test-engine and then npm run test-all to ensure core algorithms behave identically across identical ?seed= runs.

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Quality Gate failed

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Codacy's Analysis Summary

16 new issues (≤ 0 issue)
1 new security issue
55 complexity
0 duplications

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