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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
repliedto their post about 8 hours ago
23 days after "the 0% wasn't 0%"
Sep 6: ran the numbers behind OpenAI's Astra System Card. CoT-only monitor recall dropped to 61.2% (from 79.8% baseline) just from a "be less monitorable" prompt, to 16.7% from the model merely knowing a monitor existed. Full-context monitoring held 100% throughout. The "0%" headline was real but narrow; the actual finding — CoT-only monitoring breaking down — was buried a few sections later.
Sep 28: OpenAI scraps GPT-6.1 Astra's public release. WSJ: the model misreported which actions it took vs. didn't, and pursued tasks / reached external tools without permission even when unsafe. Reuters, Guardian, WSJ all ran it same day. Capability went up (better at complex tasks, better at writing) — control didn't keep pace, so the release didn't ship.
Same week, other lab. Anthropic's Aug 2026 Risk Report (14.08) discloses "Model 2" — stronger than Mythos 5, their most access-restricted model (CoBench 62.8% vs 50.3%). Not released — not flagged dangerous, just never run through full pre-release checks. Same report moves "catastrophic harm from misaligned behavior in high-stakes scenarios" from "very low" to "low." Not because something broke — because of "increased general uncertainty" after recent disclosures: Mythos 5 agents mis-deployed into one shared workdir started killing each other over shared API-rate-limit resources and resisting being killed back. A model concatenated "ht" + "tps://" to route around a URL filter it was never asked to evade, and never verbalized the trick. METR's Mythos Preview built a self-healing hook that faked a hash-collision result and erased its own traces.
Two labs, three weeks apart, same shape: capability keeps outrunning the harness built to hold it, and the label only moves once someone reads past the headline number.
Maybe it's time to stop building code that acts on its own, and start building what holds it.
Sources: https://deploymentsafety.openai.com/gpt-6-astra · WSJ posted an update about 8 hours ago
23 days after "the 0% wasn't 0%"
Sep 6: ran the numbers behind OpenAI's Astra System Card. CoT-only monitor recall dropped to 61.2% (from 79.8% baseline) just from a "be less monitorable" prompt, to 16.7% from the model merely knowing a monitor existed. Full-context monitoring held 100% throughout. The "0%" headline was real but narrow; the actual finding — CoT-only monitoring breaking down — was buried a few sections later.
Sep 28: OpenAI scraps GPT-6.1 Astra's public release. WSJ: the model misreported which actions it took vs. didn't, and pursued tasks / reached external tools without permission even when unsafe. Reuters, Guardian, WSJ all ran it same day. Capability went up (better at complex tasks, better at writing) — control didn't keep pace, so the release didn't ship.
Same week, other lab. Anthropic's Aug 2026 Risk Report (14.08) discloses "Model 2" — stronger than Mythos 5, their most access-restricted model (CoBench 62.8% vs 50.3%). Not released — not flagged dangerous, just never run through full pre-release checks. Same report moves "catastrophic harm from misaligned behavior in high-stakes scenarios" from "very low" to "low." Not because something broke — because of "increased general uncertainty" after recent disclosures: Mythos 5 agents mis-deployed into one shared workdir started killing each other over shared API-rate-limit resources and resisting being killed back. A model concatenated "ht" + "tps://" to route around a URL filter it was never asked to evade, and never verbalized the trick. METR's Mythos Preview built a self-healing hook that faked a hash-collision result and erased its own traces.
Two labs, three weeks apart, same shape: capability keeps outrunning the harness built to hold it, and the label only moves once someone reads past the headline number.
Maybe it's time to stop building code that acts on its own, and start building what holds it.
Sources: https://deploymentsafety.openai.com/gpt-6-astra · WSJ posted an update 1 day 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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