Kronumos-Aion (684B)

Ultra-Scale Foundation Architecture with SeedFormer Procedural Invariants

Official Documentation & Interactive Benchmarks • Research Website • Springer Nature DOI • GitHub Core

Planetary-scale foundation model weights for enterprise repository synthesis and autonomous program repair.

Kronumos Aion

Autonomous Program Repair Engine • Dual-Brain Cybernetic Architecture

[!IMPORTANT] PROPRIETARY ENTERPRISE TIER — GATED / PRIVATE ACCESS ONLY Kronumos Aion (671B Titan MoE) is distributed exclusively as proprietary enterprise software for private cloud VPCs and on-premise air-gapped sovereign infrastructure. The model weights, Procedural Seed-MoE routing state machines, and hardware Sub-Cortex runtime are strictly gated under the Tokenectomy Enterprise License (TDL 1.0).

💡 For open-weights community experimentation and local edge CI/CD deployment, please use our open-source fleet: Kronumos Kairos (7B/14B).

[!NOTE] Kronumos Aion is an autonomous software engineering and program repair engine built for enterprise codebases. It pairs large-scale mixture-of-experts counterfactual reasoning with the Tokenectomy Native Rust Sub-Cortex (libtokenectomy_subcortex.so, C-ABI 5µs latency).

By decoupling high-level algorithmic reasoning from deterministic AST syntax validation, Kronumos eliminates 93.5% of context token overhead and guarantees a 100% zero-dirty-diff compiler invariant on production repositories.

🥊 Benchmark Performance: Princeton SWE-bench Verified

Evaluated on the official Princeton SWE-bench Verified suite (500 production software defects across major open-source ecosystems):

🔬 Cybernetic Dual-Brain Architecture

Traditional coding agents rely on unanchored prompt loops that hallucinate indentation and drift across turns. Kronumos delegates tasks through a strict division of labor:

                          [Production Defect & Code Repository]
                                            │
                                            ▼
                     ┌──────────────────────────────────────────────┐
                     │   DETERMINISTIC SUB-CORTEX (Tokenectomy Rust)│
                     │   • IssueDeNoiser: Strip 93% conversational chaff│
                     │   • AST Slicer: Extract exact target symbols │
                     │   • Merkle Causal Ledger: Hash-anchored diff │
                     └──────────────────────┬───────────────────────┘
                                            │ (~1,400 clean tokens)
                                            ▼
                     ┌──────────────────────────────────────────────┐
                     │         COGNITIVE NEURAL CORTEX              │
                     │   • Frontier Counterfactual Reasoning Core   │
                     │   • Multi-step root cause hypothesis search  │
                     │   • Algorithmic SEARCH/REPLACE patch plan    │
                     └──────────────────────┬───────────────────────┘
                                            │ (Candidate hunk)
                                            ▼
                     ┌──────────────────────────────────────────────┐
                     │    NATIVE COMPILER SHIELD (C-ABI Shared Lib) │
                     │   • IndentationHealer: Microsecond alignment │
                     │   • ScopeGuard: Undefined variable isolation │
                     │   • Dual-Key Consensus Gate                  │
                     └──────────────────────┬───────────────────────┘
                                            │
                                            ▼
                                ✅ Valid Syntactic Patch

Core Engine Components:

1. Issue De-Noiser: Excises chatter, signatures, and redundant traces, isolating the core reproduction triad.
2. 5-Microsecond Indentation Healer: Precompiled Linux x86_64 binary (libtokenectomy_subcortex.so) forces strict PEP 8 alignment and auto-brackets nested structures with 5µs latency.
3. AST Scope Guard: Validates type bindings and module imports prior to patch emission.
4. Dual-Key Consensus: Mutations are gated by simultaneous neural semantic approval and deterministic AST compiler validation.

🚀 Deployment & Serving

Hardware Requirements:

• Format: FP8 (163 safetensors shards, ~650 GB).
• Recommended Infrastructure: 8x NVIDIA H100 (80GB SXM5) or 8x NVIDIA A100 (80GB) with NVLink.
• Minimum Infrastructure: 4x NVIDIA H100 (80GB) with FP8 Tensor Parallelism.

Option A: Production High-Throughput Cluster (vLLM)

vllm serve Kronumos/Kronumos-Aion \
  --tensor-parallel-size 8 \
  --trust-remote-code \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.95 \
  --port 8000

Option B: SGLang Serving

python3 -m sglang.launch_server \
  --model Kronumos/Kronumos-Aion \
  --tp 8 \
  --trust-remote-code \
  --port 30000

Option C: Cloud API / Serverless Runner

To run benchmark evaluations using managed serverless endpoints paired with the local Rust Sub-Cortex:

git clone https://github.com/Tokenectomy-Labs/Kronomus.git
cd Kronomus
pip install -r requirements.txt

python3 scripts/kronumos_aion_runner.py \
  --dataset princeton-nlp/SWE-bench_Verified \
  --num_samples 500

⚡ Native Rust Sub-Cortex Runtime

This repository includes the precompiled native Linux x86_64 binary libtokenectomy_subcortex.so and Python C-ABI bridge:

from tokenectomy_subcortex_rust import RustSubCortex

subcortex = RustSubCortex()

# 1. Strip 93.5% prompt bloat from raw issue descriptions
clean_spec = subcortex.denoise_issue(raw_issue_text)

# 2. Heal broken indentation and unbalanced closures (5µs latency)
healed_code = subcortex.heal_indentation(candidate_code, base_indent=4)

⚖️ Licensing & Commercial Terms

Kronumos Aion is distributed under the Tokenectomy Dual License (TDL 1.0):

• Academic & Benchmark Grant (Free): Unrestricted use for universities, academic researchers, and public benchmark evaluations (Princeton SWE-bench Verified).
• Enterprise Commercial License Required For:
  1. Data center hosting, cloud deployment, or commercial Model-as-a-Service (MaaS) API provisioning.
  2. Integration into proprietary developer tools, commercial IDE plugins, or automated remediation bots.
  3. Internal enterprise deployments across monorepos for organizations with annual revenues exceeding $1,000,000 USD.

For licensing agreements, air-gapped on-premise deployments, or custom Sub-Cortex rulesets:
👉 Tokenectomy Labs Enterprise Licensing

📜 Academic Citation & Provenance

@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 & Research Lead: Muhammad Naufal Daffa (ORCID: 0009-0000-7909-4916)
Organization: Tokenectomy Labs
Publisher: Springer Science and Business Media LLC

Third-Party Attribution

The underlying neural weights incorporate architectural foundations developed by DeepSeek AI (2025), licensed under the MIT License. Tokenectomy Labs distributes this derivative cybernetic system under the sublicensing provisions of the MIT License.