🏛️ Kronumos Cybernetics

Architecting Dual-Brain Cybernetics for Zero-Dirty-Diff Autonomous Program Repair

Bridging Frontier Neural Reasoning with Deterministic Compiler Invariants.
Home of the Kronumos engine, the Seed Transformer architecture, and the Tokenectomy Sub-Cortex.

⚡ Live Interactive Workbench • Website • GitHub Core • Research Preprint • PyPI Package • Enterprise Licensing

🔬 Core Scientific Thesis

Large Language Models exhibit probabilistic brittleness when generating software patches, frequently hallucinating indentation, misplacing closing delimiters, and triggering catastrophic token inflation ($4.00 – $15.00+ per patch).

Kronumos resolves this fundamental bottleneck through a Dual-Brain Cybernetic Division of Labor:

1. Cognitive Neural Cortex (GPU VRAM): Deep multi-step counterfactual reasoning (<thought> tags) conditioned on procedural failure seeds.
2. Deterministic Sub-Cortex (CPU): Native Rust C-ABI engine (libtokenectomy_subcortex.so, 5µs latency) that excises 93.5% conversational prompt bloat, enforces strict AST grammars, auto-heals indentation, and guarantees Zero Dirty Diffs ($P(\text{SyntaxError}) = 0$).

🚀 Production Engines

🥊 Empirical Benchmark: Princeton SWE-bench Verified

Evaluated on the official Princeton SWE-bench Verified suite across 500 production software defects: • Agent Turns per Defect: 1 – 2 turns (vs. 50 – 120 turns in standard agentic loops) • Context Token Overhead: 1,830 – 3,200 tokens (vs. 60,000 – 180,000 baseline, a 93.5% reduction) • Inference Cost: $0.02 – $0.09 USD per remediation (100x cost bounded) • Syntax Compliance: 100% AST compiler compliance guaranteed by the Native Sub-Cortex

📦 Public Fleet & Releases

• kronumos (PyPI): Lightweight zero-friction client SDK (pip install kronumos). • Kronumos-Aion: Flagship 684B Titan MoE Seed-Transformer for cost-bounded enterprise reasoning. • Kronumos-Kairos-v2: Calibrated reasoning model with default procedural invariant system prompt. • Kronumos-14B-Kairos: Open-weight Dual-Brain workhorse with community GGUF edge support.

⚡ Quickstart via Python SDK

pip install kronumos

from kronumos import Kronumos

bot = Kronumos()
fix = bot.repair(
    code="def calculate_ratio(a, b):\n    return a / b",
    issue="ZeroDivisionError when b is zero. Return 0.0 or raise graceful ValueError."
)
print(fix)

📜 Scientific Provenance & Citation

@article{daffa2026kronumos,
  title     = {Kronumos 2 Kairos: Cost-Bounded Automated Program Repair via Dual-Brain Cybernetic Sub-Cortex on SWE-bench Verified},
  author    = {Muhammad Naufal Daffa},
  journal   = {Springer Nature Research Square},
  year      = {2026},
  doi       = {10.21203/rs.3.rs-11205335/v1},
  url       = {https://doi.org/10.21203/rs.3.rs-11205335/v1}
}

• Author & Chief Scientist: Muhammad Naufal Daffa (ORCID: 0009-0000-7909-4916)
• Organization: Tokenectomy Labs / Kronumos AI
• Publisher: Springer Science and Business Media LLC
• Inquiries & Enterprise Licensing: daffa@kronumos.com