Kronumos/Kronumos-Kairos-v2

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 27, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

Kronumos/Kronumos-Kairos-v2 is a 7.6 billion parameter cybernetic autonomous program repair (APR) model developed by Tokenectomy Labs. Based on Qwen2.5-Coder-7B-Instruct, it integrates neural reasoning with a deterministic, native Rust Sub-Cortex for high-precision code remediation. This model excels at single-pass, zero-shot bug fixing on real-world production software, demonstrating high token efficiency on benchmarks like SWE-bench Verified.

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Kronumos 2 Kairos: Dual-Brain Program Repair

Kronumos 2 Kairos is a 7.6 billion parameter model from Tokenectomy Labs, specifically fine-tuned for autonomous program repair (APR). It uniquely combines a neural "Cortex" (based on Qwen2.5-Coder-7B-Instruct) with a deterministic, native Rust "Sub-Cortex" for precise code remediation. This dual-brain architecture aims to overcome limitations of traditional LLM agents by decoupling semantic reasoning from deterministic code validation.

Key Capabilities & Architecture

  • Cybernetic Dual-Brain Architecture: Integrates a parametric neural Cortex for semantic reasoning and code hunk generation with a deterministic Rust Sub-Cortex for AST validation, bracket/indent healing, and a Merkle causal ledger.
  • High-Precision Bug Fixing: Evaluated on the Princeton SWE-bench Verified benchmark (500 instances), Kronumos 2 Kairos resolved 8 full production issues with a 100% syntactic clean git apply rate.
  • Exceptional Token Efficiency: Achieves solutions with an average of 2,512 tokens per task, representing a 93.5% reduction compared to multi-turn agent baselines that use 40,000-150,000 tokens.
  • Native Rust Sub-Cortex: Features a zero-allocation Rust component (libtokenectomy_subcortex.so) with sub-microsecond latency (5µs) for tasks like issue de-noising and code healing.
  • Single-Pass Zero-Shot Execution: Unlike multi-turn agent loops, this model performs repairs in a single pass, significantly reducing API costs and execution time.

Ideal Use Cases

  • Automated Bug Remediation: Excellent for rapid, high-precision fixing of production software bugs.
  • CI/CD Integration: Suitable for integration into continuous integration/delivery pipelines for automated code quality checks and repairs.
  • Local Development Environments: Designed for efficient local bug remediation on workstations and offline laptops due to its optimized footprint and performance.
  • Code Quality Assurance: Can be used to enforce strict code formatting (e.g., PEP 8) and balance syntax deterministically.

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2 models