- 1. Executive Summary
- 2. Current State (v1.0.0-alpha, 50%)
- 3. Phase 1: Road to v1.0 (MVP) — 6 Months
- 3.1. v1.0 Milestone Requirements
- 3.2. v0.8.0: Compliance Sprint (Month 1)
- 3.3. v0.9.0: Container & Verification (Month 2)
- 3.4. v0.10.0: Real-World Applications (Month 3)
- 3.5. v0.11.0: Visualization & Accessibility (Month 4)
- 3.6. v0.12.0: Publication Sprint (Month 5)
- 3.7. v1.0.0: Official Release (Month 6)
- 4. Phase 2: Expansion (v2-v4) — 12 Months
- 5. Phase 3: Ecosystem Integration (v5-v8) — 24 Months
- 6. Phase 4: AI & Automation (v9-v10) — 18 Months
- 7. Phase 5: Universal Platform (v11-v12) — 24 Months
- 8. Resource Requirements
- 9. Risk Mitigation
- 10. Success Metrics
- 11. Conclusion
This roadmap charts Absolute Zero’s evolution from a research prototype (current v1.0.0-alpha at 50%) through v1.0 release to v12.0 — a comprehensive formal verification platform for computational nullity.
Timeline: 18 months (v1.0) → 7 years (v12.0)
Vision: Transform from academic proof-of-concept to production-ready verification infrastructure used by compiler writers, security researchers, and formal methods practitioners worldwide.
-
Core Theory: 6 proof systems, 22 theorems, ~7000 lines of proof code
-
Multi-Prover Verification: Coq, Lean 4, Z3, Agda, Isabelle, (Mizar pending)
-
Advanced Modules: Statistical mechanics, category theory, lambda calculus, quantum, filesystem
-
Research Foundation: Paper drafts, examples, documentation
-
Python interpreters (violates RSR language policy → migrate to Julia/Rust)
-
npm/package.json (violates Deno-only policy)
-
License inconsistencies (AGPL references → PMPL-1.0-or-later)
-
Incomplete checkpoint files (ECOSYSTEM.scm needs detail)
-
Container verification not validated
Goal: Production-ready research artifact with published paper
| Category | Deliverable | Status |
|---|---|---|
Theory |
All 6 proof systems verified in containers |
🟡 90% |
Implementation |
Python → Rust migration complete |
🔴 0% |
Documentation |
Peer-reviewed paper accepted |
🔴 0% |
Standards |
Full RSR compliance (PMPL, Deno, no Python) |
🔴 30% |
Infrastructure |
CI/CD with all proof systems |
🟡 70% |
Applications |
3 real-world CNO examples |
🟡 50% |
Focus: Fix technical debt, achieve RSR compliance
-
❏ License Migration
-
Replace all AGPL-3.0 references with PMPL-1.0-or-later
-
Update SPDX headers in all 500+ files
-
Create LICENSE and LICENSE-MPL-2.0 files
-
Remove LICENSE-PALIMPS.md stub
-
-
❏ Language Policy Enforcement
-
Migrate Brainfuck interpreter: Python → Rust
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Migrate Whitespace interpreter: Python → Rust
-
Remove package.json, npm dependencies
-
Add deno.json for JS runtime needs
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Add Cargo.toml for Rust interpreters
-
-
❏ Checkpoint File Completion
-
Complete ECOSYSTEM.scm with proper descriptions
-
Update STATE.scm with recent progress
-
Add detailed related-projects section
-
-
❏ Repository Hygiene
-
Remove duplicate TypeScript code (use ReScript only)
-
Clean up Elm playground (assess if needed)
-
Consolidate documentation
-
Deliverable: Clean, compliant codebase ready for publication
Focus: Bulletproof verification infrastructure
-
❏ Container Validation
-
Build Containerfile with all 6 proof systems
-
Verify all proofs run in container
-
Add container publish workflow
-
Test on multiple architectures (amd64, arm64)
-
-
❏ Proof System Integration
-
Mizar installation automation
-
Cross-system theorem synchronization
-
Automated proof checking in CI
-
Proof coverage reporting
-
-
❏ Performance Optimization
-
Parallel proof verification
-
Cached proof artifacts
-
Incremental verification
-
Deliverable: One-command verification (podman run absolute-zero verify-all)
Focus: Demonstrate practical utility
-
❏ Compiler Optimization
-
Dead code elimination example
-
LLVM IR CNO detection
-
Benchmark performance gains
-
-
❏ Database Transactions
-
Prove rollback is CNO
-
PostgreSQL integration example
-
Transaction safety verification
-
-
❏ Secure Sandboxing
-
Untrusted code safety proof
-
WebAssembly CNO validator
-
Docker/Podman sandbox
-
Focus: Make theory accessible to non-experts
-
❏ Web-Based Proof Explorer (ReScript + Deno)
-
Interactive proof tree visualization
-
Step-through proof execution
-
Theorem dependency graphs
-
Mobile-responsive design
-
-
❏ CNO Playground (Tauri 2.0)
-
Write/test programs in browser
-
Real-time CNO verification
-
Visual state transition diagrams
-
Share proof URLs
-
Focus: Research paper finalization
-
❏ Abstract & introduction
-
❏ Formal CNO definition (all 6 systems)
-
❏ Composition theorems with proofs
-
❏ Thermodynamic foundations (Landauer, Bennett)
-
❏ Complexity analysis (undecidability proof)
-
❏ Industrial applications & benchmarks
-
❏ Related work comparison
-
❏ Future research directions
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Tier 1: POPL, PLDI, ICFP, OOPSLA
-
Tier 2: ITP, CPP, VSTTE
-
Journals: TOPLAS, JFP, PACMPL
Focus: Stable, documented, published
-
✅ All 6 proof systems verified
-
✅ Zero
Admittedorsorryin core proofs -
✅ Full RSR compliance
-
✅ Paper accepted (or in revision)
-
✅ 3 industrial examples working
-
✅ Container verified on 2+ architectures
-
✅ Documentation complete
-
✅ GUI functional
-
❏ Git tag:
v1.0.0 -
❏ GitHub/GitLab release notes
-
❏ DOI via Zenodo
-
❏ crates.io package:
absolute-zero -
❏ Announcement blog post
-
❏ Social media campaign
Goal: Extend beyond esoteric languages
-
❏ C: Prove
return;is CNO -
❏ Rust: Verify
()and no-op functions -
❏ Python: Detect CNO patterns via AST
-
❏ JavaScript: ReScript-based CNO linter
-
❏ SQL: Transaction rollback verification
-
❏ Assembly: x86-64
nopinstruction proof
-
❏ Universal CNO specification format
-
❏ Language-agnostic verification engine
-
❏ Plugin architecture for new languages
Goal: AI-assisted proof discovery
-
❏ Machine Learning Models
-
Train on existing proofs
-
Suggest proof strategies
-
Auto-complete proof sketches
-
-
❏ Proof Search
-
Automated theorem proving
-
SMT solver integration
-
Sledgehammer-style tactics
-
-
❏ Proof Refactoring
-
Simplify complex proofs
-
Detect proof duplication
-
Suggest lemmas
-
Goal: Enterprise-ready verification platform
-
❏ Performance
-
Parallel proof checking
-
Distributed verification
-
GPU-accelerated SMT solving
-
-
❏ Scalability
-
Verify large codebases (1M+ LOC)
-
Incremental verification
-
Proof caching & memoization
-
-
❏ Security
-
Proof auditing & provenance
-
Cryptographic proof commitments
-
Supply chain verification
-
Goal: Seamless integration with existing toolchains
-
❏ LLVM Plugin
-
CNO detection pass
-
Dead code elimination
-
Optimization hints
-
-
❏ GCC Plugin
-
Similar to LLVM
-
GCC-specific optimizations
-
-
❏ Rust Compiler (rustc)
-
Macro for CNO annotation
-
Compile-time verification
-
Zero-cost abstractions
-
Goal: Bridge to existing verification tools
-
❏ Frama-C: C verification
-
❏ Why3: Multi-prover integration
-
❏ Dafny: Program verification
-
❏ F*: Dependent types
-
❏ TLA+: Temporal logic
-
❏ SMT-LIB 2.6 output
-
❏ TPTP problem format
-
❏ Proof certificates (LFSC, Dedukti)
Goal: Extend CNO theory to quantum realm
-
❏ Formal Definition
-
Quantum state preservation
-
Unitary operation verification
-
Entanglement preservation
-
-
❏ Proof Systems
-
QPL (Quantum Programming Language) integration
-
Qiskit circuit verification
-
Cirq CNO detection
-
-
❏ Applications
-
Quantum algorithm optimization
-
Error correction verification
-
Noise mitigation
-
Goal: Extend to hardware design
-
❏ Verilog: RTL CNO detection
-
❏ VHDL: Hardware CNO verification
-
❏ Chisel: Scala-based HDL
-
❏ Bluespec: Formal hardware design
Goal: State-of-the-art AI-assisted proving
-
❏ Transformer-based Prover
-
Train on 1M+ proofs
-
Beat human experts on benchmarks
-
Transfer learning across systems
-
-
❏ Reinforcement Learning
-
Learn proof strategies
-
Optimize proof length
-
Discover novel theorems
-
-
❏ Neuro-Symbolic Methods
-
Combine neural nets with symbolic reasoning
-
Explainable AI proofs
-
Human-readable justifications
-
Goal: Zero-human-in-the-loop verification
-
❏ Auto-Fix
-
Detect non-CNO code
-
Suggest CNO rewrites
-
Automated refactoring
-
-
❏ Continuous Verification
-
GitHub Actions integration
-
Pre-commit hooks
-
Real-time code review
-
-
❏ Proof Repair
-
Fix broken proofs automatically
-
Handle API changes
-
Maintain proof health
-
Goal: Verify CNOs in every computational domain
-
❏ Biology: Protein folding simulations
-
❏ Chemistry: Molecular dynamics
-
❏ Physics: Lattice QCD simulations
-
❏ Finance: Zero-knowledge trading
-
❏ Cryptography: Homomorphic encryption
-
❏ Julia integration (native)
-
❏ NumPy/SciPy CNO detection
-
❏ BLAS/LAPACK verification
-
❏ HPC cluster support
Goal: Establish CNO as universal computational primitive
-
❏ ISO Standard: Submit CNO specification
-
❏ IEEE Standard: Formal verification methods
-
❏ W3C: Web platform CNO API
-
❏ Taught in CS curriculums
-
❏ Required for safety-critical software
-
❏ Referenced in regulations (FDA, FAA)
-
❏ 10,000+ users
-
❏ 1,000+ papers citing Absolute Zero
-
❏ 100+ companies using in production
-
❏ 50+ programming languages supported
-
❏ Distributed proof network
-
❏ Proof marketplace
-
❏ CNO certification authority
-
❏ Global verification registry
| Phase | Roles | FTE |
|---|---|---|
v1.0 (6mo) |
Lead researcher, 2 proof engineers |
2.5 |
v2-v4 (18mo) |
+ 2 software engineers, 1 ML researcher |
5.5 |
v5-v8 (24mo) |
+ 2 integration engineers, 1 quantum expert |
8.5 |
v9-v10 (18mo) |
+ 3 AI researchers, 1 DevOps engineer |
12.5 |
v11-v12 (24mo) |
+ 2 domain experts, 1 standards liaison |
15.5 |
| Phase | Duration | Budget (USD) |
|---|---|---|
v1.0 |
6 months |
$200K (salaries, compute, publication) |
v2-v4 |
18 months |
$800K (team expansion, cloud infra) |
v5-v8 |
24 months |
$1.5M (partnerships, hardware) |
v9-v10 |
18 months |
$2M (AI compute, research) |
v11-v12 |
24 months |
$3M (global expansion, standards) |
Total |
7 years |
$7.5M |
| Risk | Impact | Mitigation |
|---|---|---|
Proof complexity explosion |
HIGH |
Focus on decidable subsets, use SMT solvers |
AI model hallucinations |
MEDIUM |
Formal verification of AI outputs |
Performance bottlenecks |
MEDIUM |
Parallel execution, caching, incremental verification |
Quantum CNO undecidability |
LOW |
Limit to finite-dimensional systems |
-
✅ Paper accepted at top-tier venue
-
✅ 500+ GitHub stars
-
✅ 10+ external contributors
-
✅ 3 industrial case studies
-
🎯 50+ languages supported
-
🎯 1,000+ users
-
🎯 10 companies in production
-
🎯 20+ academic citations
Absolute Zero has the potential to transform from a research curiosity into foundational computer science infrastructure. By systematically expanding from esoteric languages to mainstream compilers, from manual proofs to AI-assisted proving, and from academic prototypes to industrial platforms, we can establish Certified Null Operations as a universal computational primitive.
The vision: In 2032, every compiler, every proof assistant, and every verification tool will have CNO detection built-in. Absolute Zero will be the reference implementation, the theoretical foundation, and the community hub for this transformation.
Next step: Execute Phase 1 (v1.0) to prove the concept, then secure funding for the 7-year journey to v12.0.
"From nothing, everything. From zero, infinity."
— Jonathan D. A. Jewell, 2026