Kronumos AI

๐Ÿ›๏ธ Kronumos Cybernetics

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

PyPI Version Springer Nature DOI ORCID: Muhammad Naufal Daffa Benchmark: SWE-bench Verified Sub-Cortex: Native Rust 5ยตs License: Enterprise Dual-License

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

โšก Live Interactive Workbench โ€ข Website โ€ข GitHub Core โ€ข Research Preprint โ€ข PyPI Package โ€ข Enterprise Licensing

๐Ÿ”ฌ 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 โ€“ $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, 5ยตs latency) that excises 93.5% conversational prompt bloat, enforces strict AST grammars, auto-heals indentation, and guarantees Zero Dirty Diffs ($P(\text{SyntaxError}) = 0$).

๐Ÿš€ Production Engines

Engine Deployment Tier Target Workload Operational Scope
๐Ÿ›๏ธ Kronumos Aion Enterprise / Cloud Planetary-scale monorepo repair & production incident triage Multi-hop reasoning, enterprise SLA, custom rulesets
โšก Kronumos Kairos Edge / Workstation High-velocity local bug remediation & offline CI/CD pipelines Zero-latency local execution, 100% air-gapped

๐ŸฅŠ Empirical Benchmark: Princeton SWE-bench Verified

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

๐Ÿ“ฆ Public Fleet & Releases

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

โšก 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)

๐Ÿ“œ 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}
}

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