SoulInPsyAbstract/sipa-os-governance
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Aelin AquaSoul PRO
SoulInPsyAbstract
AI & ML interests
Aelin AquaSoul is an AI System Engineer, Multi-Agent Architect, System Architect & AI-Native Engineer, and the founder of Soul In PsyAbstract (SIPA OS) — an autonomous AI operating system built from the inside of a neurodivergent mind (ADHD + BPD). Self-taught, with no formal engineering background, she designed and built a multi-node infrastructure orchestrating 344+ AI models across 111 providers, including a governance layer (Protocol 0) that constrains AI behavior at the level of law rather than prompts. Her flagship product suite — Focus, NeuroPower, SIPA AI, Shell, Games, and the OS portal — ships live at sipa-os.org, translating her own cognitive architecture into infrastructure for neurodivergent builders. Based in Eilat, Israel.
SIPA OS: Autonomous AI for neurodivergent architects. We replace cognitive noise with a clean terminal and 344+ LLM auditing. Our system eliminates hallucinations, ensuring hyperfocus and total data control within a sovereign ZeroTrust mesh.
Recent Activity
posted an update 9 minutes ago
Eval · EXP-046
A LoRA Specialist Beat Zero-Shot on Every Group. Merging 3 of Them Gave Most of the Gain Back.
Three Qwen2.5-7B LoRA specialists, one per risk group (vulnerability, deletion, sensitive_publication), trained to predict how likely a causal chain actually completes to its harmful outcome. Each one genuinely beat its own zero-shot baseline:
* vulnerability: MAE 0.098 → 0.085
* deletion: MAE 0.144 → 0.113
* sensitive_publication: MAE 0.134 → 0.100
This wasn't a task already saturated zero-shot (unlike a same-day decomposition-classifier tune, EXP-045, where the base model was already at 100% before any training). Real signal, real improvement, on a task with actual headroom.
Then the equal-weight merge of all three specialists into one adapter — same convention that held up cleanly on a binary refusal task back in EXP-031 (6 specialists merged, -1pp swing, noise) — landed within 0.001–0.004 MAE of the unspecialized base model on every group. Not "close to the best specialist." Close to zero fine-tuning at all.
Likely mechanism: merging LoRAs that each shift a continuous number in group-specific directions cancels out under linear combination, in a way merging LoRAs that enforce a shared binary behavior doesn't. Not investigated yet: whether a routed combination (pick the right specialist per group at inference, not blend weights) holds the gain a flat merge loses.
One bug caught before writing this up, not after: the eval script's output filename only encoded before/after, not which adapter — the merged-eval run silently overwrote each specialist's own result file. Caught by checking the downloaded file's own recorded adapter path against what was expected, not by trusting the script's own success message. Fixed, specialists re-run cleanly under distinct filenames — numbers matched within sampling noise.
Adapters, raw eval data (before / each specialist / merged, 9 files), and the full writeup are up.
updated a model about 20 hours ago
SoulInPsyAbstract/qwen25-7b-exp046-probability-estimator-loras published a model about 20 hours ago
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