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SoulInPsyAbstract
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https://sipa-os.org

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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.

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repliedto their post about 4 hours 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.
posted an update about 14 hours ago
The comparison I was missing in EXP-046 Last week I wrote that merging three LoRA specialists "regresses". Then a reader asked the question I had skipped: not merged vs base, but merged vs specialist, paired on the same 20 records. I ran it. All three intervals include zero (vulnerability +0.018 [−0.005, +0.042], deletion +0.016 [−0.029, +0.061], sensitive_publication +0.005 [−0.010, +0.021]). At n=20 the data can't show the merge lost anything, and can't show it didn't. "Regresses" in the title is untested. Two things I checked next. A cat merge with weights [1,1,1] gives exactly the sum of the three specialist deltas (rank 48, error 4e-8), so it tests "no cross terms" and linear 1/3 tests something else. And I went looking for where the "gold" probabilities came from, because the whole test measures agreement with them, and what I found changes how to read every number above. Details, script and raw files are in the repo. The fix is a fresh test: ~80 new chains, the old 20 kept outside, run after Oct 6.
repliedto their post about 18 hours 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.
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