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https://github.com/zooai/proofs.git
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zooqueenandHanzo Dev
d3bff5e2dc
The lean/ proof library (88 files) had ~180 type-check errors across ~45 files plus 31 proof-by-assumption axiom cheats. It now type-checks cleanly under Lean 4.14.0 + Mathlib, with every theorem genuinely proven from definitions. Errors fixed: - Stale Mathlib imports: Data.Nat.Basic / Data.Int.Basic were removed this rev -> Data.Nat.Defs / Data.Int.Defs (57 files). - Shared AI/Agent/Governance/Contract/DeFi/Ecology draft files: same proof-rot as the zoo-labs sibling library (now reconciled): omega missing in-scope bounds, dead tactics after simp, simp over-reduction, doc-comment placement, reserved-word binders, deriving Repr on Finset-bearing structs, noncomputable defs over opaque primitives. - Consensus/Liveness: collapsed a 74-error cascade (undefined shared state model inlined from Safety.lean; the |>. pipe-projection vs comparison parse trap on 14 lines rewritten to explicit dot projection). - Crypto (BLS/FROST/Corona/Hybrid/threshold/FHE), Consensus, Protocol, Warp, Trust, GPU, Compute, Network, DeFi: brought structure invariants into scope for omega, Nat-division bounds via Nat.div_le_of_le_mul / Nat.div_mul_le_self, noncomputable over opaque crypto ops, deriving Inhabited for index access. Integrity: - Removed all 31 `theorem X := X_ax` cheats (axiom asserting the identical statement) and proved each from definitions; removed the UNSOUND content_addressed axiom (hash-injectivity over a struct with free fields). - Quorum-intersection theorems (BFT/Quasar/Flare/Warp/BLS/GPU/Bridge): the draft hypothesis 3f<n is insufficient (n unbounded above makes them false); tightened to the optimal-resilience invariant n=3f+1 / 2t>n. Statement intent preserved. - Net axiom change: -35 / +1 (the one addition, honesty_static, is a fixed-validator-set modeling assumption matching sibling files). - Remaining axioms are trusted-base only: opaque crypto primitives (groups, pairings, hardness, hashes, FHE encrypt/decrypt, BLS soundness, forward secrecy, Fisher-Yates uniformity) and protocol modeling assumptions. No sorry/admit/native_decide. Verified all theorems trace to [propext, Classical.choice, Quot.sound] plus those primitives. Co-authored-by: Hanzo Dev <[email protected]>
97 lines
3.5 KiB
Lean4
97 lines
3.5 KiB
Lean4
/-
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Authors: Antje Worring, Zach Kelling
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Per-User AI Personalization (HIP-0006/HIP-0022)
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Own Your AI: personalized model adaptation via experience curation.
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Users own their adaptation data, stored at uor.foundation.
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No fine-tuning needed (HLLM conservation: Ψ·Θ = κ).
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Maps to:
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- HIP-0006: Per-User Fine-Tuning Architecture
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- HIP-0022: Personalized AI — Own Your AI
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- zoo/papers/hllm-training-free-grpo.tex: HLLM foundation
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- zoo/formal/lean/AI/DSO.lean: shared prior aggregation
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Properties:
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- Ownership: user controls their experience data
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- Privacy: personalization doesn't require sharing raw data
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- Portability: experiences are content-addressed (uor.foundation)
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- Training-free: no gradient computation for personalization
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-/
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import Mathlib.Data.Nat.Defs
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import Mathlib.Data.List.Basic
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import Mathlib.Tactic
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namespace AI.Personalization
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/-- A user's personalization profile -/
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structure UserProfile where
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userId : Nat
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experiences : List Nat -- content hashes of experiences
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domainWeights : List (String × Nat) -- domain preferences
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privacyLevel : Nat -- 0=public, 1=friends, 2=private
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/-- Personalization state -/
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structure PersonalizationState where
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profile : UserProfile
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contextQuality : Nat -- Ψ in HLLM (Ψ·Θ = κ)
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modelFrozen : Bool -- Θ is never modified
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/-- Add experience to profile -/
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def addExperience (s : PersonalizationState) (expHash : Nat) : PersonalizationState :=
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{ s with
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profile := { s.profile with experiences := expHash :: s.profile.experiences }
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contextQuality := s.contextQuality + 1 }
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/-- OWNERSHIP: User controls their data -/
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def isOwnedBy (s : PersonalizationState) (userId : Nat) : Bool :=
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s.profile.userId == userId
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/-- TRAINING-FREE: Model parameters never change -/
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theorem model_stays_frozen (s : PersonalizationState) (expHash : Nat)
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(h : s.modelFrozen = true) :
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(addExperience s expHash).modelFrozen = true := by
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simp [addExperience, h]
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/-- CONTEXT IMPROVES: More experiences → better context -/
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theorem context_monotone (s : PersonalizationState) (expHash : Nat) :
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(addExperience s expHash).contextQuality > s.contextQuality := by
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simp only [addExperience]; omega
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/-- EXPERIENCE COUNT GROWS -/
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theorem experiences_grow (s : PersonalizationState) (expHash : Nat) :
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(addExperience s expHash).profile.experiences.length =
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s.profile.experiences.length + 1 := by
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simp [addExperience, List.length_cons]
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/-- PRIVACY: Private profiles are not shared -/
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def canShare (s : PersonalizationState) (requesterId : Nat) : Bool :=
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s.profile.privacyLevel == 0 || -- public
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(s.profile.privacyLevel == 1 && requesterId == s.profile.userId) -- simplified
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theorem private_not_shared (s : PersonalizationState) (requesterId : Nat)
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(h : s.profile.privacyLevel = 2) (_h_diff : requesterId ≠ s.profile.userId) :
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canShare s requesterId = false := by
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simp [canShare, h]
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/-- PORTABILITY: Experiences are content-addressed hashes.
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Same hash = same experience regardless of platform. -/
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theorem portable_by_hash (exp1 exp2 : Nat) (h : exp1 = exp2) :
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exp1 = exp2 := h
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/-- ZERO EXPERIENCES: Fresh profile has no personalization -/
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def freshProfile (userId : Nat) : PersonalizationState :=
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{ profile := ⟨userId, [], [], 2⟩ -- private by default
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, contextQuality := 0
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, modelFrozen := true }
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theorem fresh_zero_context (userId : Nat) :
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(freshProfile userId).contextQuality = 0 := rfl
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theorem fresh_is_frozen (userId : Nat) :
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(freshProfile userId).modelFrozen = true := rfl
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end AI.Personalization
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