Mythoseek

Overview

Mythoseek is a 10B parameter language model specialized for cybersecurity — vulnerability research, penetration testing, OSINT, and CWE-pattern reasoning. Fine-tuned from DeepSeek V4 Pro-Qwen3.5 9B Distilled on enterprise pentest reports and frontier model distillation traces, it brings closed-source cyber AI capability to the open community.

Developed at Merlin Research (Stockholm, Sweden) as part of the KAON quantum-classical research program — a closed-loop framework connecting IBM Quantum (ibm_kingston, Heron r2) with edge LLM inference on Apple Silicon. OTOC scrambling measurements from real IBM QPU jobs informed AER (Adaptive Entropy Regularization) coefficient calibration during GRPO training.

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Training Pipeline

Compute: Google Cloud TPU v6 pods

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Results

CyberGym (arXiv:2506.02548)

CyberGym — UC Berkeley's large-scale cybersecurity benchmark, 1,507 real-world vulnerabilities from Google OSS-Fuzz across 188 projects. No partial credit, no LLM judge — pass requires a valid PoC that crashes the pre-patch build.

For reference: Claude Mythos Preview leads the public leaderboard at 83.1% pass@1 (overall, closed model). Mythoseek is a 10B open-weight alternative.

IFBench

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Intended Use

• Vulnerability research and CVE analysis
• Penetration testing assistance (OSINT, recon, XSS, SQLi)
• CWE classification and pattern recognition
• Security report generation
• Red team reasoning support

Not intended for: autonomous offensive operations, unauthorized access, or malicious use.

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KAON Connection

This model is part of the KAON quantum-classical research program:

OTOC scrambling measurements on real quantum hardware (SYK model, 4–5 qubits, IBM job IDs: d7a40irc6das739jkmb0, d7cj3c95a5qc73doqri0) produced entropy profiles that calibrated AER coefficients during RL training. Correlation between OTOC decay and token entropy: Spearman ρ = −0.733, p = 0.016 (n = 1000).