squ11z1
Mythoseek
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squ11z1
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squ11z1
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
squ11z1
Model Tree
Input Modalities
Output Modalities
Supported Functionality
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.
| 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
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.
| 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.
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).