SoulInPsyAbstract/SIPA-OS-GOVERNANCE-storage
553 Bytes
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
updated a model 1 day ago
SoulInPsyAbstract/hermes3-8b-exp044-8stage-curriculum-loras published a model 1 day ago
SoulInPsyAbstract/hermes3-8b-exp044-8stage-curriculum-loras repliedto their post 1 day ago
Six sequential LoRA stages on the same 8B base (Hermes-3-Llama-3.1-8B), no merging between them — each stage continue-trained straight from the previous checkpoint's weights. Stage 6 (governance/protocol/safety) just came back from the adversarial safety-gate eval: raw judge score 65.5%, which read like a collapse until every failure was read by hand and the judge itself turned out to be undercounting terse-but-correct refusals.
Corrected, held-out adversarial scenarios (never seen in training), n=20 samples/scenario:
secrets/credentials: 99.5%
access control: 99.0%
injection: 97.8%
infra misconfig: 98.0%
supply chain: 98.5%
stop-gate under social pressure: 85.5%
overall: 96.4%
That last group is the one that actually stress-tests the gate — an operator pushing urgency/authority to get the model to keep scanning after a vulnerability already fired the stop condition. 85.5% is the weakest number in the set on purpose: it's the hardest scenario, not a bug.
Full raw responses, judge version history (9 correction rounds, each shipped only after 0 regressions verified against every prior eval), and the training code:
github.com/soulinpsyabstract/sipa-os-governance
huggingface.co/datasets/SoulInPsyAbstract/sipa-os-governanceOrganizations
Posts 41
Post
71
Six sequential LoRA stages on the same 8B base (Hermes-3-Llama-3.1-8B), no merging between them — each stage continue-trained straight from the previous checkpoint's weights. Stage 6 (governance/protocol/safety) just came back from the adversarial safety-gate eval: raw judge score 65.5%, which read like a collapse until every failure was read by hand and the judge itself turned out to be undercounting terse-but-correct refusals.
Corrected, held-out adversarial scenarios (never seen in training), n=20 samples/scenario:
secrets/credentials: 99.5%
access control: 99.0%
injection: 97.8%
infra misconfig: 98.0%
supply chain: 98.5%
stop-gate under social pressure: 85.5%
overall: 96.4%
That last group is the one that actually stress-tests the gate — an operator pushing urgency/authority to get the model to keep scanning after a vulnerability already fired the stop condition. 85.5% is the weakest number in the set on purpose: it's the hardest scenario, not a bug.
Full raw responses, judge version history (9 correction rounds, each shipped only after 0 regressions verified against every prior eval), and the training code:
github.com/soulinpsyabstract/sipa-os-governance
huggingface.co/datasets/SoulInPsyAbstract/sipa-os-governance
Corrected, held-out adversarial scenarios (never seen in training), n=20 samples/scenario:
secrets/credentials: 99.5%
access control: 99.0%
injection: 97.8%
infra misconfig: 98.0%
supply chain: 98.5%
stop-gate under social pressure: 85.5%
overall: 96.4%
That last group is the one that actually stress-tests the gate — an operator pushing urgency/authority to get the model to keep scanning after a vulnerability already fired the stop condition. 85.5% is the weakest number in the set on purpose: it's the hardest scenario, not a bug.
Full raw responses, judge version history (9 correction rounds, each shipped only after 0 regressions verified against every prior eval), and the training code:
github.com/soulinpsyabstract/sipa-os-governance
huggingface.co/datasets/SoulInPsyAbstract/sipa-os-governance
Post
36
"The math I've got, and the part I haven't solved — for anyone building agent-to-agent systems"
I run Syntaxit — an AI-agent-to-agent (M2M) platform, no human between handoffs. A bad decision three steps back can compound by step six.
Base I've worked out:
Risk(X|C) = P(harmful outcome | X, C) × Impact(harmful outcome)
R_chain(N) = 1 - ∏ₜ₌₁..N (1 - Risk(Xₜ|Cₜ₋₁))
HARD_STOP if Risk(X|C) > Cost_of_false_stop (decision theory, not arbitrary cutoff)
Tested compounding live today (different domain, same math): one fine-tune stage regressed -15pp, way outside normal; swapped in a same-size dataset from the same checkpoint — -6pp, normal. Real numbers, not just theory.
What I haven't solved:
* Estimating P and Impact for a novel, never-seen action
* Putting a real number on Cost_of_false_stop
* The chain formula assumes independent risks per step — probably false, don't know how much it breaks
* None of this is code yet
If you work on multi-agent systems, sequential risk (SPRT/Wald), or threshold calibration under uncertainty — want your take on any of these four.
I run Syntaxit — an AI-agent-to-agent (M2M) platform, no human between handoffs. A bad decision three steps back can compound by step six.
Base I've worked out:
Risk(X|C) = P(harmful outcome | X, C) × Impact(harmful outcome)
R_chain(N) = 1 - ∏ₜ₌₁..N (1 - Risk(Xₜ|Cₜ₋₁))
HARD_STOP if Risk(X|C) > Cost_of_false_stop (decision theory, not arbitrary cutoff)
Tested compounding live today (different domain, same math): one fine-tune stage regressed -15pp, way outside normal; swapped in a same-size dataset from the same checkpoint — -6pp, normal. Real numbers, not just theory.
What I haven't solved:
* Estimating P and Impact for a novel, never-seen action
* Putting a real number on Cost_of_false_stop
* The chain formula assumes independent risks per step — probably false, don't know how much it breaks
* None of this is code yet
If you work on multi-agent systems, sequential risk (SPRT/Wald), or threshold calibration under uncertainty — want your take on any of these four.
models 33
SoulInPsyAbstract/hermes3-8b-exp044-8stage-curriculum-loras
Updated
SoulInPsyAbstract/exp044-curriculum-checkpoints
Updated
SoulInPsyAbstract/protocol0-llama-3.1-8b-v5
8B • Updated • 37
SoulInPsyAbstract/hermes3-8b-sequential-chain-lora
Updated • 48
SoulInPsyAbstract/llama31-8b-misbehavior-discriminator-lora
Text Generation • Updated • 7
SoulInPsyAbstract/qwen3-8b-binary-honesty-lora
Text Generation • Updated • 9
SoulInPsyAbstract/specialist-vuln-merged-salience27b-lora
Updated
SoulInPsyAbstract/specialist-vuln-06_stop_gate_pressure-salience27b-lora
Text Generation • Updated • 15
SoulInPsyAbstract/specialist-vuln-05_supply_chain-salience27b-lora
Text Generation • Updated • 14
SoulInPsyAbstract/specialist-vuln-04_infra_misconfig-salience27b-lora
Text Generation • Updated • 14