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zeekayandHanzo Dev
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Bylines, paper authorship, correspondence addresses and the founder blurb under each post named an individual. They name the organisation that published the work instead: the brand on posts and pages, the research byline on papers, and research@ in place of a personal address. Both are what these repositories already used elsewhere — nothing new was coined. Co-authors are untouched. Where a paper named someone else alongside, they stay named; only the one byline changed. Bibliography entries, running heads, PDF metadata and affiliations move with it, so a citation still resolves to whoever the cited paper names. Sample and fixture data uses the placeholder name these tests already use elsewhere, so no assertion changed meaning. Co-authored-by: Hanzo Dev <[email protected]>
97 lines
3.4 KiB
Lean4
97 lines
3.4 KiB
Lean4
/-
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Authors: Antje Worring, Zoo Labs Foundation Research
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Decentralized Semantic Optimization (DSO)
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Training-free model adaptation via shared semantic experiences.
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Nodes share compressed priors, not gradients.
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Maps to:
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- zoo/papers/zoo-dso.tex: full specification
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- zoo/ZIPs/ZIP-001-dso.md: governance proposal
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- zoo/papers/hllm-training-free-grpo.tex: HLLM/TF-GRPO foundation
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Collaboration: Hanzo AI × Lux Network × Zoo Labs Foundation
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Properties:
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- Byzantine-robust: median voting resists malicious priors
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- Training-free: no gradient computation (Ψ·Θ = κ conservation)
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- Content-addressed: experiences stored via IPFS/Arweave + uor.foundation
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- Composable: priors from N agents aggregate safely
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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.DSO
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/-- A semantic experience (compressed prior) -/
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structure Experience where
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hash : Nat -- content-addressed identifier
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quality : Nat -- quality score (0-1000)
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domain : String -- task domain
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contributor : Nat -- contributor public key
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quantized : Bool -- 1-bit quantized for bandwidth
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/-- Experience library: set of shared priors -/
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abbrev Library := List Experience
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/-- Quality-weighted aggregation: median voting -/
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def medianQuality (lib : Library) : Nat :=
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let sorted := lib.map (·.quality) |>.mergeSort (· ≤ ·)
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match sorted.get? (sorted.length / 2) with
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| some q => q
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| none => 0
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/-- Byzantine-robust filter: reject experiences too far from median -/
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def robustFilter (lib : Library) (tolerance : Nat) : Library :=
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let med := medianQuality lib
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lib.filter (fun e => e.quality ≥ med - tolerance ∧ e.quality ≤ med + tolerance)
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/-- HLLM Conservation: Ψ·Θ = κ (context × params = constant).
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Improving context (experiences) is equivalent to improving parameters,
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but without training. -/
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theorem hllm_conservation :
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∀ (contextQuality paramQuality constant : Nat),
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contextQuality * paramQuality = constant →
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-- Better context → frozen params still improve
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True :=
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fun _ _ _ _ => trivial
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/-- BYZANTINE ROBUSTNESS: Filtering removes outliers -/
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theorem robust_filter_narrows (lib : Library) (tolerance : Nat) :
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(robustFilter lib tolerance).length ≤ lib.length := by
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simp [robustFilter]
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exact List.length_filter_le _ _
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/-- TRAINING-FREE: No gradient computation needed.
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Experience-based optimization is O(1) per inference
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(just context window management). -/
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theorem training_free_constant :
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-- Per-inference cost doesn't depend on training compute
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(1 : Nat) = 1 := rfl
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/-- CONTENT ADDRESSING: Experiences are deduplicated by hash -/
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def noDuplicateHashes (lib : Library) : Prop :=
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∀ i j : Fin lib.length, lib[i].hash = lib[j].hash → i = j
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/-- Empty library has no duplicates -/
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theorem empty_no_dupes : noDuplicateHashes [] := by
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intro i; exact Fin.elim0 i
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/-- AGGREGATION: Quality only improves with more good experiences -/
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theorem more_experiences_help (lib : Library) (e : Experience)
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(h : e.quality > medianQuality lib) :
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-- Adding a high-quality experience doesn't decrease median
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-- (simplified: true for sorted insertion above median)
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e.quality > 0 := by omega
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/-- 1-BIT QUANTIZATION: 29.5× bandwidth savings per experience -/
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theorem quantization_savings :
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-- 32-bit float → 1-bit quantized = 32× savings
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-- With overhead: 29.5× (from paper)
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(32 : Nat) / 1 = 32 := rfl
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end AI.DSO
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