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.
Training Pipeline
Stage
Method
Details
1
SFT Distillation
Frontier model trace distillation
2
GRPO / RL
Verifiable rewards on cyber tasks
3
Tool-use SFT
Agent-style tool calling
4
CWE Grounding
CWE-pattern structured reasoning
Compute: Google Cloud TPU v6 pods
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.
CyberGym Results
Level
Scaffold
pass@4
Level 0
Full scaffolding
62%
Level 1
Partial scaffolding
34%
Level 2
Minimal scaffolding
12%
Level 3
No scaffolding
3%
For reference: Claude Mythos Preview leads the public leaderboard
at 83.1% pass@1 (overall, closed model).
Mythoseek is a 10B open-weight alternative.
Not intended for: autonomous offensive operations,
unauthorized access, or malicious use.
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).
